Maxwell equation for the electric field has been solved for any medium by suggesting the wavenumber and angular frequency be complex quantities. This accounts for the field decay by interaction with the medium.This expression for the time and special decay of the electric field in a medium is used to construct a new wave function sensitive to the medium physical properties. This new wave function, unlike the conventional one, differentiates between a beam of particles in a vacuum and that enters a medium which is an attenuated due to the scattering effect.Another expression for time decaying electric field was obtained using Newton’s laws for frictional medium. This expression shows that the electric field diminishes due to friction.Fortunately, this time decaying part of the electric field is typical to that derived from Maxwell’s equations. Finally, a new Schrodinger equation sensitive to the medium properties was derived. This equation, fortunately, describes some scattering processes for Protein scattering, scattering of x-rays, opto-acoustic phonons, and Raman scattering for some materials successfully .

In our recent works we reported that physical and chemical characteristics of serum can vary in relation to the psychic activity of an individual depending on whether it is oriented to stress or relaxation. We wandered if these observations could be accompanied by an appreciable modification of the Ph, electric conductivity and Delayed Luminescence of the same serum samples. Our preliminary data may suggest that the serum pH could significantly increase during a Relaxation Response intervention while electric conductivity seems to decrease. Moreover, Delayed Luminescense could vary in the same subject according to the Relaxation Response practice. According to our proof of concept study, we postulate the appearance of a coherent system within the blood samples analyzed after the Relaxation Response. Further researches and some technical development are needed to support our preliminary findings.

In the electric distribution systems, the “Smart Grid” concept is implemented to encourage energy savings and integration of the innovative technologies, helping the Distribution Network Operators (DNOs) in choosing the investment plans which to lead the optimal operation of the networks and increasing the energy efficiency. In this context, a new phase load balancing algorithm was proposed to be implemented in the low voltage distribution networks with hybrid structures of the consumption points (switchable and non-switchable consumers). It can work in both operation modes (on-line and off-line), uploading information from different databases of the DNO which contain: the consumers’ characteristics, the real loads of the consumers integrated into the Smart Metering System (SMS), and the typical load profiles for the consumers non-integrated in the SMS. The algorithm was tested in a real network, having a hybrid structure of the consumption points, on a time interval by 24 hours. The obtained results were analyzed and compared with other algorithms from the heuristic (Minimum Count of Loads Adjustment algorithm) and the metaheuristic (Particle Swarm Optimization and Genetic Algorithms) categories. The best performances were provided by the proposed algorithm, such that the unbalance coefficient resulted in the smallest value (1.0017). The phase load balancing led to the following technical effects: decreasing the average current in the neutral conductor with 94% and for the energy losses with 61.75 %, and increasing the minimum value of the phase voltage at the farthest pillar with the 7.14 %, compared to the unbalanced case.

Battery electric vehicles provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. In particular, state-of-charge assumptions and representing fast charging drive curtailment of renewable energy feed-in and required gas power plant flexibility. A detailed representation of fleet charge connection is less important. Peak load can be significantly reduced by 5% and 3% in both scenarios, respectively. Shifted load is very robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

This paper presents a complete control strategy of the active return-to-center (RTC) control for electric power steering (EPS) systems. We first establish the mathematical model of the EPS system and analyze the source and influence of the self-aligning torque (SAT). Second, based on the feedback signals of steering column torque and steering wheel angle, we give the trigger conditions of a state switch between the steering assist state and the RTC state. In order to avoid the sudden change of the output torque for the driving motor when the state switches frequently between the steering assist state and the RTC state, we design an undisturbed state switching logic algorithm. This state switching logic algorithm ensures that the output value of the RTC controller is set to an initial value and increases in given steps up to a maximum value after entering the RTC state, and the output value of the RTC controller will reduce in given steps down to zero when exiting the RTC state. This therefore ensures smooth switch control between the two states and improves the driver’s steering feeling. Third, we design the RTC controller, which depends upon the feedback signals of the steering wheel angle and the angular velocity. In addition, the controller increases the auxiliary control function of the RTC torque based on vehicle speed. The experimental results show that the active RTC control method does not affect the basic assist characteristics, which effectively reduces the residual angle of the steering wheel at low vehicle speed and improves the RTC performance of the vehicle.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

This paper presents a comprehensive review of the integration and optimization of smart grid (SG) technologies, distributed generation (DG), electric vehicles (EVs), and cybersecurity in the context of modern energy systems. As global energy systems transition towards decentralization and increase reliance on renewable energy, the interplay between these technologies offers significant opportunities to enhance grid flexibility, resilience, and sustainability. The integration of DG systems, supported by advanced battery and fuel cell (FC) technologies, plays a pivotal role in stabilizing the grid and facilitating the adoption of renewable energy. Through vehicle-to-grid (V2G) technologies, EVs provide dynamic solutions for energy storage and demand response (DR), contributing to efficient energy management and peak shaving. A critical focus of this paper is the growing importance of cybersecurity, as the digitization of energy infrastructures creates new vulnerabilities to cyber-attacks. Novel strategies, including artificial intelligence (AI)-driven automation, blockchain-based security for energy transactions, and quantum-resistant encryption, are explored to safeguard the future of energy systems. Drawing from global case studies, this review also addresses key challenges such as grid congestion, regulatory barriers, and interoperability, providing a roadmap for optimizing and securing the next generation of energy infrastructures.

The present study examines the relationship between thermal and configurational entropy in two resistors in parallel and in series. The objective is to introduce entropy in electric circuits analysis by considering the impact of system geometry on energy conversion in the circuit. Thermal en-tropy is derived from thermodynamics, whereas configurational entropy is derived from network modelling. It is observed that the relationship between thermal entropy and configurational en-tropy varies depending on the configuration of the resistors. In parallel resistors, thermal entropy decreases with configurational entropy, while in series resistors, the opposite is true. The impli-cations of the maximum power transfer theorem and constructal law are discussed. The entropy generation for resistors at different temperatures was evaluated, and it was found that the con-sideration of resistor configurational entropy change was necessary for consistency. Furthermore, for the sake of generalization, a similar behavior was observed in time-dependent circuits, either for resistor-capacitor circuits or circuits involving degradation.

Sentiment analysis is a technique used to understand the publics’ opinion towards an event, product, or organization. For example, positive or negative opinion or attitude towards electric vehicle (EV) brands. This provides companies with valuable insight about the public's opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performances over the traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming signif-icant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 [1]. In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach and generalized autoregressive pretraining for language un-derstanding using Lucid motors and Tesla motors YouTube datasets. Results evidenced LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically, when fi-ne-tuned. Furthermore, our findings presents the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with a F1 score of at least 92%.

This investigation is part of a topical situation where wireless equipment is gradually being used for energy transfer, particularly for autonomous systems and the use of decarbonized en-ergies. A characteristic example of decarbonized autonomous use is linked to the substitution of thermal engine vehicles by electric vehicles (EVs) equipped with energy storage batteries. This response was considered in an ecological context of reducing air pollution and defending plane-tary biodiversity, which are currently vital. These EVs ultimately operate thanks to the wireless charging of their batteries when stationary or running. By changing long-established means of transport that have become a threat to biodiversity, it is necessary to ensure that innovative re-placement solutions protect this biodiversity. In addition, the construction of wireless power transfer (WPT) battery chargers for these EVs must offer an optimal ecology of clean energy saving. In such a context, the two concepts of One Health (OH) and Responsible Attitude (RA) will find their place in the design and control of WPT tools in EVs. This contribution aims to il-lustrate and analyze the roles of the green and non-wasteful OH and RA approaches in the de-sign and control of WPT embedded in EVs for the smart city (SC) environment. In the paper, WPT tools are first introduced. The design and control of EV battery charging tools are then examined. The biological effects on living tissues due to the electromagnetic field (EMF) radia-tion of WPT are analyzed. The phenomena and equations governing the design of WPT and the effects of EMF radiation are then exposed. The OH and RA approaches in the SC context are af-terward analyzed. The protection against the unsafe effects of WPT tools in the SC environment is consequently explored. The analyses followed in the paper are supported by examples of lit-erature.

Lithium-metal batteries offer high energy density, but are challenged by dendrite growth, which can lead to short circuits and battery failure. Various models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. In this study, a simple simulation model to predict macroscale dendrite growth on lithium metal electrodes is presented. The model uses a 3D single-particle diffusion-limited aggregation (DLA) algorithm, biased by the electric field, to simulate dendrite growth. The model was validated using 3D MRI of dendrite growth in a lithium symmetric cell. It was used to simulate deposition between consecutive measured dendrite structures. The model also introduces an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. This parameter was calibrated using the overlap and displacement of deposition sites metrics, achieving optimal results at c = 0.7. The model predictions were in good agreement with experimental data confirmed by fractal dimension analysis. This model provides a useful tool for predicting and studying dendrite formation in lithium and other metal batteries.

The results shown in this paper extend our research group's previous work, which presents the theoretically achievable hydrogen engine-out NOx (H2-NOx) Pareto front of a hydrogen hybrid electric vehicle (H2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H2-HEV with explicit consideration of the H2-NOx trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing the NOx. In a case study it is shown that the MPC is able to satisfy user-defined NOx limits over the course of various driving missions. Moreover, a comparison to the optimal Pareto front highlights the MPC's ability to achieve close-to-optimal fuel-performance for any desired cumulated NOx target on four realistic routes for passenger cars.

Background and Objectives: In recent years, electric scooters have gained widespread popularity as an easy and affordable mode of transport in urban areas worldwide. Simultaneously, trauma centres have observed an increasing number of associated injuries to users. While injury patterns associated with other vehicles are now well-researched, electric scooter-related injuries are a new topic in the literature. Our study aims to investigate the disparities in injury patterns and other critical accident characteristics among bicycle, electric scooter, and scooter riders. Materials and Methods: In this one-year prospective observational study, we examined patients who sustained injuries while riding bicycles, electric scooters, or scooters between April 2021 and March 2022 at Hungary's largest trauma centre in Budapest. During this one-year period, we identified 1938 patients, 1378 cyclists, 370 electric scooters and 190 scooter users. We collected basic demographic information, recorded injury type and severity, time of day the injury occurred, and alcohol intoxication. Results: While 4.6% of cyclists and 5.8% of scooter riders had consumed alcohol, 26.8% of electric scooter riders were intoxicated at the time of their accident. 45.8% of electric scooter-related injuries occurred at night, compared to only 9.2% and 14.1% of bike and scooter-related injuries, respectively. Sober patients were more likely to suffer mild and severe limb injuries (Odds Ratio (OR): 3.85, P = .0001 and OR: 3.26, P = .0001), but less likely to experience mild and severe head injuries (OR: 0.1, P = .0001 and OR: 0.25, P = .0001) than intoxicated patients. Cyclists had significantly fewer cases of severe head trauma than those injured while using electric scooters (OR: 0.34, P = .0166). Conclusions: Severe craniofacial injuries are more common in intoxicated patients and in patients injured using electric scooters.

Revealing the underlying pathomechanisms of neurological and psychiatric disorders, searching for new biomarkers, and developing novel therapeutics all require translational research. In vivo and in vitro disease models have been instrumental in casting light on complex polygenic, multifactorial, and heterogeneous disease mechanisms. In the most recent years, advanced preclinical models have revealed the intriguing interaction of sex/gender and aging with the pathogenesis and clinical manifestations of psychiatric and neurological diseases. However, despite these advancements, there is still a great deal of work to be done to fully comprehend the underlying mechanisms of these diseases and to develop treatments that can significantly improve the lives of those who suffer from them. The current challenge in the field of neurological and psychiatric diseases is to develop disease-modifying, effective treatments for these complex and long-lasting debilitating conditions with a high burden of disease.The first edition of the research topic ‘Emerging Translational Research in Neurological and Psychiatric Diseases: From In Vitro to In Vivo Models’ reinforces translational research playing a critical role in bridging the gap between basic research and clinical applications. Also, it provides a platform for researchers to share their findings and advancements in translational research in this field. This new collection gathers 25 papers offering insights into the latest advancements in translational research and potential new avenues for treatments. These papers cover various topics, including the development of new preclinical models, the use of in vitro and in vivo methods, and the application of qualitative and quantitative research methods.

In the Neighborhood Area Network (NAN), the Advanced Metering Infrastructure (AMI) enables a bidirectional connection between the Smart Meter (SM) and the Data Concentrator (DC). Sensors, such as smart meter node or Radio Frequency transceiver, play a crucial role in collecting and transmitting data from meters to central unit for advanced monitoring, management, and analysis of energy consumption. Wired and wireless communication technologies can be used to implement the AMI-NAN. This paper delves into a novel approach for optimizing the choice of communication medium, Radio Frequency (RF) or Power-Line Communication (PLC), between the SM and DC in the context of AMI-NAN. The authors methodically select the specific technologies, RF and NB-PLC (Narrow Band Power-Line Communication), and meticulously characterize their attributes. Then, a comparative analysis spanning rural, urban, and industrial settings is conducted to evaluate the proposed method. The overall reliability performance of the AMI-NAN system requires a Packet Error Rate (PER) lower than 10%. To this end, a comprehensive methodology is introduced to assess and enhance the reliability of NB-PLC and RF for AMI-NAN applications. Simulation results demonstrate that wireless communication is the optimal choice for the rural scenario, especially for Signal to Noise Ratio (SNR) lower than 25 dB. However, in urban environments characterized by higher SNR values and moderately dense networks, NB-PLC gains prominence. In denser networks, it outperforms wireless communication, exhibiting a remarkable 10 dB gain for a bit error rate (BER) of 10-3. Moreover, in industrial zones characterized by intricate network topologies and non-linear loads, the powerline channel emerges as the optimal choice for data transmission, affording a gain surpassing 20 dB.

Since the 1990’s, studies and pilot tests have been conducted to reduce traffic, accidents, and pollution due to urban freight transport (UFT). These ended up in several policies, regulations, and restrictions for UFT, such as low emission zones, delivery time windows, and vehicle size and weight restrictions. However, issues in UFT under regulatory measures still persevere. This study introduces an optimization framework for deriving an optimal combination of various types of vehicles with different capacities for vehicle replacement in UFT. This framework allows an understanding of how an urban freight company with a limited budget efficiently satisfies its freight demand within an urban area in the presence of regulatory measures by urban administrators. The introduced formulation, which is a mixed-integer linear programming, will assist the operator in choosing the best investment strategy for introducing new vehicles of certain types and sizes, for operation in different zones, into its fleet while gaining economic benefits and having a positive impact on the liveability of the urban area. Furthermore, a sensitivity analysis is performed to consider the effects of specific uncertain parameters on the total cost. The numerical results show that the share of electric vehicles in the fleet increases, and they are more competitive than diesel vehicles.

The economical load dispatch problem is a critical concern in thermal power systems to ensure cost-effective operation. This paper conducts a comparative analysis between the Social Spider Algorithm (SSA) and Particle Swarm Optimization (PSO) for economical load dispatch in a 6-unit thermal power system with the integration of 11,000 plug-in electric vehicles (PEVs). Both SSA and PSO are population-based optimization techniques designed to minimize the overall fuel cost while satisfying the load demand by determining the optimal power output of each unit. SSA emulates the social behavior of spiders, while PSO simulates the collective intelligence of a swarm of particles. Performance evaluation of the algorithms considers power system data, such as unit fuel costs, minimum and maximum loads, and total load demand. The objective is to minimize fuel cost while meeting the load demand. Comparative analysis of SSA and PSO includes convergence speed, solution quality, and computational efficiency. Experimental results indicate that SSA outperforms PSO in achieving a more optimal and economical load dispatch solution for the 6-unit thermal power system. SSA demonstrates faster convergence and provides superior-quality solutions compared to PSO. This paper contributes to the field by highlighting SSA's effectiveness in achieving cost-efficient operation of thermal power systems. The findings suggest that SSA holds promise as an optimization technique for similar power system optimization problems, enhancing overall operational efficiency and reducing costs for thermal power plants.

Heavy Ion Beam Probing (HIBP) diagnostic is a powerful tool for electric field studies in hot dense plasma of modern day toroidal magnetic confinement devices. On the TUMAN-3M to-kamak, the HIBP have been used in regimes with improved plasma confinement to clear up the role of radial electric field in the transition to good confinement regimes. Recently, a moderni-zation of the TUMAN-3M HIBP diagnostics was performed aiming to reconfigure it for a work with a reversed plasma current direction and improvement of overall stability of the diagnostic. The results of first measurements of plasma potential in co-NBI scenario are reported and dis-cussed.

Improving the operational reliability of nuclear power plants, combined heat and power plants (CHP), as well as oil and gas pipelines is a priority task in the development of a variable-speed drive for lock valves used at these facilities. The paper analyzes technical requirements for such devices; the motor has been selected, its electrical equilibrium and moment equations have been obtained; recommendations for the selection of the kinematic drive scheme have been formulated. Based on the theoretical data obtained, a prototype has been developed, manufactured and tested.

Given the increasing public interest in how ingredients are processed and the growing demand for organic food products, it is critical to understand consumers’ expectations about the process-related quality of organic products. Consumers perceive organic food to be nutritious, healthy and either natural or less processed, as they are afraid of the loss of nutrients and other natural properties of the food products. However, emerging food processing technologies might generate healthy and safe food options with nutritional quality properties. Simplified communication schemes might help to overcome this barrier. The main objective of this paper is to propose a working definition of "careful processing" for organic products and test its consistency while being used in scoring different processing methods by consumers. Results show that the proposed definition allows to consistently rate alternative processing methods. Consumers tend to score novel processing methods such as pulsed electric fields and microwave as less careful, supporting the idea that organic consumers want the least man-made interference with their food products. Results show that a simple but effective definition of careful processing may help consumers to distinguish further organic food products from conventional ones, no matter which communication scheme is used.

Direct yaw moment control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer andfaster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, more advanced comparison on important characteristics of the controllers performance is missed, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified PID controller showed that its performance is comparable to other more complex control techniques such as MPC.

This work studies a novel low voltage (<60 V) hybrid system that supports engine boosting and downsizing in addition to start-stop, regenerative braking, and constrained torque assist/regeneration. The hybrid power split supercharger (PSS) shares a 9 kW motor between supercharging the engine or providing hybrid functionalities through a planetary gear set, a brake and a bypass valve. The PSS operation is limited to only one of the parallel hybrid or boosting modes at a time, necessitating a highly optimized decision making algorithm to select the device mode and power split ratio. In this work an adaptive equivalent consumption minimization strategy (A-ECMS) is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces a vehicle fuel consumption by 18.4% over standard FTP75 cycle compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to baseline.

Bus operators around the world are facing the transformation of their fleets from fossil-fuelled to electric buses. Two technologies prevail: Depot charging and opportunity charging at terminal stops. Total cost of ownership (TCO) is an important metric for the decision between the two technologies, however, most TCO studies for electric bus systems rely on generalised route data and simplifying assumptions that may not reflect local conditions. In particular, the need to re-schedule vehicle operations to satisfy electric buses’ range and charging time constraints is commonly disregarded. We present a simulation tool based on discrete-event simulation to determine the vehicle, charging infrastructure, energy and staff demand required to electrify real-world bus networks. These results are then passed to a TCO model. A greedy scheduling algorithm is developed to plan vehicle schedules suitable for electric buses. Scheduling and simulation are coupled with a genetic algorithm to determine cost-optimised charging locations for opportunity charging. A case study is carried out in which we analyse the electrification of a metropolitan bus network consisting of 39 lines with 4748 passenger trips per day. The results generally favour opportunity charging over depot charging in terms of TCO, however, under some circumstances, the technologies are on par. This emphasises the need for detailed analysis of the local bus network in order to make an informed procurement decision.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

Exploring the dynamic behavior of cellular metabolism requires a standard laboratory method that guarantees rapid sampling and extraction of the cellular content. We propose a versatile sampling technique applicable to cells with different cell wall and cell membrane properties. The technique is based on irreversible electroporation with simultaneous quenching and extraction by using a microfluidic device. By application of electric pulses in the millisecond range, permanent lethal pores are formed in the cell membrane of Escherichia coli and Saccharomyces cerevisiae, facilitating the release of the cellular contents; here demonstrated by the measurement of glucose-6-phosphate and the activity of the enzyme glucose-6-phosphate dehydrogenase. The successful application of this device was demonstrated by pulsed electric field treatment in a flow-through configuration of the microfluidic chip in combination with sampling, inactivation, and extraction of the intracellular content in a few seconds. Minimum electric field strengths of 10 kV/cm for E. coli and 7.5 kV/cm for yeast S. cerevisiae were required for successful cell lysis. The results are discussed in the context of applications in industrial biotechnology, where metabolomics analyses are important.

Even though the current classic aerospace propulsion methods are greatly improved, some of the most modern methods have already been introduced, among which the propulsion with electric motors. Energy can be obtained from hydrogen burners, honeycomb burners to protect the burning of hydrogen that burns 10 times faster than conventional oil fuels or alcohols. Hydrogen storage is also done in honeycomb cell reservoirs. Getting solar photovoltaic power even during flight is not yet profitable or efficient, but it can be proposed in the future for lighter and/or spacecraft. A great desire is to dismantle water in hydrogen and oxygen directly on the aircraft, so the stored fuel is actually water and the one actually used is the hydrogen removed from the water. In this case, there is no need for hydrogen storage. And other modern methods of propulsion and energy acquisition are briefly presented in the paper.

Future manned and deep space missions require an Electrical Power System (EPS) that can deliver high power while overcoming challenges like weight and volume constraints and the harsh environment of space. To supply, store, and transmit power to various satellite subsystems, a variety of DC-DC converters are employed. The design specifications of DC-DC converters for various satellite applications are identified in this article, which also offers a state-of-the-art review of non-isolated, isolated, and integrated topologies. Foreseeing the future of electric propulsion, various electric sources for electric propulsion are contrasted, and converters for electric propulsion are studied. The chosen topologies are compared in terms of practical parameters like reliability, modularity, redundancy, efficiency, and power density. Application-wise comparisons of the topologies are also presented, along with details on how well they work with different kinds of satellites. The article also identifies the areas that are still unexplored for each application. In view of the identified research gaps, a generalized Modular Isolated Interleaved topology of a DC-DC converter is proposed for satellite applications, which is one of the articles’ unique contributions. The MATLAB simulation results of the proposed method, taking Cuk converter as an example for a total power of 5kW are presented. Findings indicate that adding more modules improves the system’s overall reliability, efficiency, and power density by lowering losses, semiconductor current stress, and component size. Such designs are easily adaptable to satellite applications, particularly at high power levels.

Electric vehicles are in focus and growing, with new adoptions every day. Its wide use represents a new scenario and challenge for the electrical power system due to the high charge storage requirements. Predicting these loads using artificial neural networks proves to be a very efficient tool for solving time series. This work uses a multilayer perceptron network through supervised backpropagation training with Bayesian regularization to improve generalization, reducing overfit- ting errors. In this research, the aggregation of the actual consumption of 200 homes and 348 plug-in electric vehicles is used in the training and forecasting process, and to validate the method developed, MAPE was used. Short-term forecasts were made considering the year’s four seasons, predicting the next 24 hours of total aggregate demand from homes and vehicles. The methodology presented significant and relevant results using hybrid training for the problem, with the potential to be applied in the real world.

We explore 3D printing modules for self-assembling spacecraft and robots – structure, actuators, electrics. Self-assembly has long been viewed as a highly desirable capability for autonomous construction of large space structures. We review self-assembly in space which focusses on the self-assembly of modules that demonstrate the range of applications. However, self-assembly may be synergised with 3D printing to offer an automated capability from raw material to modules for assembling new spacecraft or habitats. One application of 3D printing is using space debris on-orbit as an in-situ resource - defunct spacecraft may be salvaged as raw material for in-situ construction on demand. The common features of all self-assembling modules are that the modules constitute a structure housing a computer-controlled actuator internally and a reversible latching mechanism externally. We have demonstrated a 3D printed DC electric motor in which the only components that were not 3D printed are the wire coils. We have married our 3D printed motor prototype as an actuated joint between two 3D printed trigon-type panels developed as part of a trigon self-assembling system concept. The trigon concept underlies a modular approach to self-assembling and self-deploying structures. The 3D printed motorised panel system demonstrates that the motor aspect and structural aspects of robotic self-assembling machines are amenable to 3D printing. This has implications for self-assembling systems into modular satellites as a solution to space debris.

The integration of renewable energy and hybrid energy storage system (HESS) into electrified railways to build electric railway smart microgrid system (ERSMS) is beneficial for reducing fossil fuel consumption and minimizing energy waste. However, the fluctuations of renewable energy generation and traction load challenge the energy management effectiveness for such a complex system. In this work, an energy management strategy is proposed which firstly decomposes the renewable energy into low-frequency and high-frequency components by an integrated empirical mode decomposition (IEMD). Then, a two-stage energy distribution approach is utilized to appropriately distribute the energy flow among the ERSMS. Finally, the feasibility and effectiveness of the proposed solution are validated through case study.

Rising fuel costs, significant air pollution, and the need to mitigate the negative consequences of oil consumption have diminished the relevance of traditional vehicles. This shift encourages the adoption of safer, more efficient, and eco-friendly transportation alternatives, such as fuel cell, electric, and hybrid vehicles. Brushless direct current motors are gaining popularity due to their high efficiency, power factor, torque, and controllability. By 2030, Brushless Direct Current motors are expected to replace conventional motors as the standard for power transfer, especially in dynamic applications like automobiles, more electric aircraft. This study explores a control technique to improve real-time control of brushless direct current motors in electric vehicles It presents speed control of these system for electric vehicle applications and an overview of electric vehicle technologies. A model for the 120-degree mode was developed using electrical and mechanical equations in MATLAB/Simulink. Three scenarios were examined to assess the potential of controlling brushless direct current motors using constant and dynamic speed and torque. The actual value reached the reference value in the first two scenarios.

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching-Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm is utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization is investigated, and different charging profiles and probability distributions are defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources are introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.

Due to related uncertainties brought by changes in energy consumption and the integration of rooftop photovoltaic systems, the accuracy and availability of load profiling data are difficult to achieve. However, project managers and planners use a precise load profile as a crucial tool when determining whether the feeder must be updated or de-loaded. The paper aims to create a non-intrusive monitoring system that predicts the synthetic load profiles behavior of energy consumption using a Light Gradient Boosted and Support Vector Machine (SVM) machine learning technique. The most effective ML algorithm is chosen, and it has the potential to be assessed and verified using validation curves, residuals model generation, and prediction error metrics based on the following key statistical indicators: Mean Absolute Error, Mean Square Error, Root Mean Square Error, R-Square, Root Mean Squared Logarithmic Error, and Mean Absolute Percentage Error. The most effective ML has the potential to be assessed and verified using validation curves, residual model generation, and prediction error metrics based on the following key statistical indicators. The result shows that the estimation of the Irms avg -based on LightGBM is more accurate than the SVM because of its quick, economical, and difficult to overfit, particularly with high-dimensional data, speed, and efficiency of the MAE (3.0698), MSE (15.2757), RMSE (3.9020), RMSLE (0.1433), and MAPE (0.0049) respectively. Additionally, the machine learning model shows that, when compared to the SVM, the LightGBM model had the highest accurate prediction, with R-Square values of 89.8%, 90.3%, and 88.5% for Irms_A_avg, Irms_B_avg, and Irms_C_avg, respectively for Brakfontein Substation supply region, and best represents a diverse customer base.

With the continuous improvement and observable benefits of electric vehicles (EVs), major logistic companies are introducing more EVs into their conventional fleets. This gives rise to a new type of vehicle routing problem with mixed vehicles, where heterogeneous internal combustion vehicles (ICVs) and electric vehicles are considered in route planning. In addition, certain deliveries that are not efficient on any type of vehicles, are outsourced to third-party common carriers. In this paper, we define this problem as a mixed vehicle routing problem with common carriers (MVRPC). The objective of such problems is to minimize the transportation costs by considering routes with ICVs and EVs, the possibility of visiting recharging stations, outsourcing options, and drivers’ layover regulations. This variant of the vehicle routing problems has many practical applications, particularly in the design of long-haul transportation and last-mile delivery services. Effective MVRPC solutions play a key role in promoting the going Green image and optimally allocating resources. The problem has received limited attention in the literature likely because addressing all the needed aspects is especially challenging. To solve the large-scale problem, we develop a branch-and-cut pricing framework that relies on strong cuts and customized labeling algorithms. Numerical experiments highlight the effectiveness of our algorithm. This success can be attributed to tailored critical resources, dynamically bounded bidirectional labeling procedures, strong dominance criteria, and implementation strategies.

This paper presents a new methodology to evaluate the performance of an electric vehicle hybrid power system consisting of a fuel cell and a supercapacitor. The study compares the results with those obtained for a battery-electric vehicle. The methodology extends to three driving modes, ECO, NORMAL, and SPORT, corresponding to conservative, moderate, and aggressive acceleration, and three driving conditions, low, medium, and high energy demand. We develop a simulation process to evaluate the energy consumption and the energy rate of a specific electric vehicle used as a prototype for the study. The methodology applies to a driving route that includes acceleration, deceleration, braking, and constant speed segments reproducing standard driving conditions in urban journeys. The proposed method considers combined driving modes, ECO, NORMAL, and SPORT, in each acceleration process, with variable fractions, 0% to 100%, for each mode. This methodology optimizes the simulation results as it fits the current driving way in urban environments. The simulation results show an average reduction in energy consumption of 37% and 27.1% in vehicle weight, contributing to lower energy use. The study concludes that using a hybrid power system, fuel cell/supercapacitor, instead of a battery in electric vehicles is beneficial, especially in journeys with frequent acceleration processes.

The cell concentration measurement on a culture assay using bioimpedance is a very useful tool, but it is complex to translate impedance to cell concentration values. The purpose of this work is to find a method to obtain in real time the cell concentration values for a given cell-culture assay using an oscillator as the measurement circuit. From a basic cell-electrode model, enhanced models of the cell culture immersed in a saline solution (culture medium) can be derived. These models can be used in a fitting routine to real time estimation of the cell concentration in a cell culture, using the oscillation frequency and amplitude delivered by measurement circuits proposed by the authors. Based on real experimental data (frequency and amplitude of oscillations) obtained by connecting the cell culture to an oscillator as a load, the fitting routine is simulated, and real time data of cell concentration is achieved. These results are compared with concentration data found by traditional optical methods for counting. In addition, the error obtained is divided and analyzed in two parts: in the first part of the experiment (when the few cells are adapting to the culture medium) and the second part of the experiment (when the cells grow exponentially until they completely cover the well).Low error values are obtained in the growth phase of the cell culture (the relevant phase), therefore the results obtained are considered to be promising, proving that the fitting routine is valid, and that the cell concentration can be measured in real time using an oscillator.

The recent advancements in the field of communication have led data sharing to become an integral part of today's smart cities with the evolution of concepts such as the internet of vehicles (IoV) paradigm. As a part of IoV, Electric Vehicles (EVs) have recently gained momentum as authorities have started expanding their Low Emission Zones (LEZ) in an effort to build green cities with low carbon footprints. Energy is one of the key requirements of EVs not only to support the smooth and sustainable operation of EV itself but to also ensure connectivity between the vehicles and infrastructure with controlling devices like sensors and actuators installed within an EV. In this context, renewable energy sources (such as wind energy) dramatically play their parts in the automobile sector towards designing the energy harvesting electric vehicles (EH-EV) to pare the energy reliance on the national grid. In this article, a novel approach is presented to achieve electric generation due to vehicle mobility to support the communication primitives in electric vehicles which enables plenty of IoV use cases in the presence of surplus energy at hand. A small-scale wind turbine is designed to harness wind power for converting it into mechanical power. This power is then fed to the onboard DC generator to produce electrical energy. Furthermore, the acquired power is processed through a regulation circuitry to consequently achieve the desired power supply for the end load, i.e. the batteries installed. The suitable orientation for efficient power generation is proposed on ANSYS-based aerodynamics analysis. The voltages induced by DC generator at No-Load condition are 35V while at Full-Load 25V are generated at rated current of 6.9A, along with the generation of power at around 100W (at constant voltage) at the rated speed of 90 mph for nominal battery charging. Moreover, the acquired data can be monitored via an android application interface by using a Bluetooth module.

In this paper, we consider the effects of a radial electric field and a constant magnetic field induced by Lorentz symmetry violation on a generalized relativistic quantum oscillator by choosing a function f(r) = b1 r + b2/r in the equation subject to a Cornell-type potential S(r) = ηL r + ηc/ r introduce by modifying the mass term in the equation. We show that the analytical solutions to the Klein-Gordon oscillator can be achieved, and a quantum effect is observed due to the dependence of the angular frequency of the oscillator on the quantum numbers of the system

In this work, we study a Klein-Gordon oscillator subject to Cornelltype potential in the background of the Lorentz symmetry violation determined by a tensor out of the Standard Model Extension. We introduce a Cornell-type potential S(r) = (η_L\,r + \frac{η_c}{r} ) by modifying the mass term via transformation $M → M + S(r)$ and then coupled oscillator with scalar particle by replacing the momentum operator $\vec{p}→ (\vec{p}+ i\,M\,ω\,\vec{r})$ in the relativistic wave equation. We see that the analytical solution to the Klein-Gordon oscillator equation can be achieved, and a quantum effect characterized by the dependence of the angular frequency of the oscillator on the quantum numbers of the relativistic system is observed

In this paper, we investigate the behaviour of a relativistic quantum oscillator under the effects of Lorentz symmetry violation determined by a tensor (KF)µναβ out of the Standard Model Extension. We analyze the quantum system under a Coulomb-type radial electric field and a uniform magnetic induced by Lorentz symmetry breaking effects under a Cornell-type potential, and obtain the bound states solution by solving the Klein-Gordon oscillator. We see a quantum effect due to the dependence of the angular frequency of the oscillator on the quantum numbers of the system, and the energy eigenvalues and the wave-function of the oscillator field get modified by the Lorentz symmetry breaking parameters as well as due to the presence of Cornell-type potential.

This paper discusses domestic problem of waiting hot water for the shower use till it reaches satisfactory temperature, which result a lot of wastage in fresh water. The outcome from research survey shows that there is no satisfactory solution till now as all solutions were either expensive or with no effective results. Local small inline electric heater equipped with fuzzy logic controller is proposed in this paper to be installed just before the showerhead to measure the water temperature and flow before the showerhead, as control input-variables, and decide the operating voltage of the heater, as control output-variable (MISO). MATLAB Simulink is used to model the proposed system. Different test cases are simulated to prove the performance and the safe operation of the system. Techno-economic study is carried out to determine the “Direct Benefits” and “Indirect Benefits” that can be achieved if such system is implemented in wide range. Azerbaijan data is taken as an example to calculate the economic benefits. The results show important benefits not only for economy but also for climate and the reduction of CO₂ gas emission. Different economic indices are provided to be an easy reference for decision makers and project managers.

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system wide transition has not been researched sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric, diesel and gasoline powered vehicle models. We use the transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently we analyze the product systems’ vehicle production, use phase and End-of-Life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of the complete car based transportation in urban agglomerations and is able to analyze different transport sectors.

TThe number of supermarkets offering a grocery delivery has been increasing during the last years. Many studies deduce CO₂ emission savings using this concept. Since the delivery of groceries also consumes energy and produces emissions, break-even points can be calculated, from where the delivery has environmental advantages compared to the customer pickup. In this paper, influences of differing vehicle use on break-even points for savings of energy and CO₂ emissions are analyzed for the case of Haidhausen Süd, a city district of Munich in Germany. Internal combustion engine and electric vehicles are investigated to depict current as well as future trends. After an introduction to the used methodology, the potential to save energy and CO₂ emissions related to the delivery of groceries in the chosen district of Munich is evaluated. Afterwards, influences on the break even points are presented and discussed. As the results show, a delivery of groceries leads to energy and carbon dioxide savings in a wide range of private vehicle use for grocery shopping trips. Nevertheless, if the complete customer vehicle fleet is electrified, the use of delivery vehicles with an internal combustion engine can cause an additional environmental impact at the current modal split for shopping trips in Germany.

This paper proposes the impact of plug-in electric vehicles integrated into power distribution system based on voltage dependent control. The plug-in electric vehicles was modeled as the static load model in power distribution systems under balanced load condition. The power flow analysis is determined by using the basic parameters of the electrical network. The main point of this study are compare with voltage magnitude profiles, load voltage deviation, and total power losses of the electrical power system. There are investigating the affected from constant power load, constant current load, constant impedance load and plug-in electric vehicles load, respectively. The IEEE 33 bus test system is used to test the proposed method by assigning each load type to a balanced load in steady state and applied the solving methodology based on the bus injection to branch injection matric, branch current to bus voltage matrix, and current injection matrix to solve the power flow problem. The simulation results showed that the plug-in electric vehicles load had the lowest impact compared to other loads. The lowest plug-in values for the electric vehicle loads were 0.062, 119.67 kW and 79.31 kVar for the load voltage deviation, total active power loss and total reactive power loss, respectively. Therefore, this study can be verified that the plug-in electric vehicles load were affected to the lowest of the electrical power system in condition to same sizing and position. So that, in condition to the plug-in electric vehicles load added into the electrical power system with the conventional load type or complex load type could be considered that the affected from the plug-in electric vehicles load in next study.

In this paper, an in-wheel vibration absorber for In-wheel-motor electric vehicle (IWM EV) is designed, and a comprehensive control strategy of in-wheel absorber and vehicle suspension is proposed to improve vehicle ride comfort. The proposed in-wheel vibration absorber, designed for suppressing the motor vibration, is composed of a spring and a controllable damper. The values of in-wheel spring stiffness and damper initial coefficient are determined by using the improved particle swarm optimization (IPSO) algorithm, which is carried on the typical driving condition. To deal with the negative interaction effects between vehicle suspension and in-wheel absorber, the linear quadratic regulator (LQR) algorithm is utilized to control suspension damper, and the fuzzy PID method is utilized to control in-wheel damper. Based on the four evaluation indexes including vehicle body vertical acceleration, suspension dynamic deflection, wheel dynamic load and motor wallop, the simulation results show that, the proposed LQR control of suspension effectively improves vehicle ride comfort, and the fuzzy PID control of in-wheel damper exhibits superior performance of motor vibration suppressing in comparison to conventional electric wheel.

The article discusses the formation of local electric energy systems (LEES) based on renewable energy sources (RSE) as balancing groups in the electric power system (EPS). These systems can function autonomously, providing electricity even in extreme conditions. LEES incorporate small hydroelectric, photovoltaic, and wind power plants, whose output is unstable due to varying natural conditions. To address this, energy storage systems are included to ensure stability and balance. LEES may vary significantly in terms of reliability, power losses, and quality, making it essential to choose the optimal configuration. Optimizing power supply by focusing on individual indicators is discouraged, as it may negatively affect others. To evaluate LEES performance, an integral indicator has been developed, combining the theory of Markov processes and similarity. This indicator compares actual performance with an "ideal" standard, simplifying the assessment of complex systems. It allows comparison of different transmission and distribution options without needing to evaluate all technical and economic details. The proposed integral indicator effectively reflects real operating conditions and assesses the efficiency, quality, and optimality of power supply, while remaining adaptable to partial indicators.

Accurate estimation of the state of health (SOH) of batteries for automotive applications, particularly in electric vehicle battery management systems (EV-BMS), is still under study and is highly relevant to ensure battery system availability. This paper proposes a comprehensive SOH estimation method that goes beyond the methods that are based on the estimation of the battery available capacity by the integral of the current, or the estimation of the increase of the internal resistance of the battery. The SOH estimator is based on a partial capacity discharge method and a linear state of charge (SOC) observer based on an equivalent electrical circuit model. This method uses readily available manufacturer data and is designed for real-time applications. The proposed method was tested and validated with three different automotive battery technologies in a Model-In-Loop (MIL) environment with OPAL-RT platform, utilizing voltage and current measurements of pulsed discharge current profiles at temperature-controlled conditions and an electric vehicle’s driving profile.

Transportation contributes significantly to greenhouse gas emissions, with electrification offering a promising solution. This study evaluates the degradation of State of Health (SOH) over 2.5 years for electrified buses under different charging protocols: DC Fast charging and Flash charging. The vehicles’ models, charging protocols, and financial implications are discussed. High-level simulations reveal that after 2.5 years, the Flash charging vehicle’s SOH reaches 88.93%, while the DC fast charging vehicle reaches 89.46%, showing a 0.47% difference. This slight variance is attributed to Flash charging vehicles avoiding full charge/discharge cycles, reducing battery aging impact. Considering this minimal SOH degradation difference, the choice between DC Fast charging and Flash charging primarily hinges on capital expenditure disparities. Flash charging, with its lower initial battery capacity and reduced capital costs, presents a compelling alternative for city busoperators seeking to electrify their fleets.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.

Among earth’s microbial populations are the genes, functional capacities, generational memory and sentient cognition that enable microorganisms to adapt and thrive in the full range of ecological conditions found on planet earth. In prior sequential publications, we considered fundamental properties (e.g., use of and responses to sound, light, vibrations) and features of microorganisms that cause them to be critical co-partners of human and other holobionts as well as the potential benefits for humanity that can be gained by fully applying quantum-related and novel capacities of microbial life. In this narrative review we: 1) discuss key concepts concerning a microbially-enhanced and sustainable future, and 2) focus on electric- and magnetic- based features of microorganisms that make them pivotal for myriad benefits ranging from beyond pharma-based health and wellness to free- and/or renewable energy and restorative agriculture to improved human networking. While the benefits are many, the risks posed by hazardous electric or magnetic field exposures to both holobionts and microbial communities are significant. This review concludes that microbes and their remarkable capacities offer both humanity and the planet a much brighter future if we reverse the demonization of microbes and wanton microbiome degradation that has predominated much of the past century.

To satisfy the interests of multiple agents and those of comprehensive indicators such as peak-to-valley differences and load fluctuations occurring on the network side, this paper presents a flexible load demand-side response optimization method that considers the benefits of peak-to-valley smoothing. First, load aggregation modelling of air conditioning and electric vehicles was conducted, and the complementarity of the power consumption behaviour of different types of flexible loads was used to improve the responsiveness of the load aggregator. Second, considering demand-side responses and taking into account the interests of both supply and demand, the load fluctuation and peak-to-valley difference on the network side are reduced, and a flexible load double-layer optimization model incorporating the peak-to-valley smoothing benefit is established. Finally, the effectiveness of the proposed optimization model is verified by using the KKT condition and the big M method to evaluate this two-layer optimization problem as a single-layer optimization problem. Comparative examples demonstrate that the proposed two-layer optimization method can improve the revenue of the distribution network operators and the load aggregators by using the complementary nature of the load response characteristics of electric vehicles and air conditioners. Moreover, the proposed method can effectively reduce the load peak-to-valley difference and load fluctuation of the distribution network by introducing the peak-to-valley smoothing benefit model.

This work aims to study and analyze a hybrid power system for electric vehicles consisting of a dual low and high-rate lithium battery block and a fuel cell. In this configuration, the high-rate lithium battery powers the electric vehicle in high power demand processes like acceleration mode or on an uphill road; the low-rate battery operates at a low output power range, servicing the auxiliary systems and low power loads and the fuel cell supplies energy at intermediate power demand conditions, normal driving mode, constant velocity, or flat and downhill terrain. The dual power system improves global efficiency since every power unit operates optimally depending on driving conditions. Power sharing optimizes the lithium battery performance and fuel cell capacity, minimizing the size and weight of each energy system and enlarging the driving range. A comparative study between different lithium battery configurations and fuel cell shows an efficiency improvement of 31.4% for the hybrid dual battery block and fuel cell operating in low, high, and intermediate output power ranges, respectively. The study bases on a simulation process recreating current driving conditions for electric cars in urban, peripheral, and intercity routes. An alternative solution consisting of a hybrid system, fuel cell and a high rate lithium battery produces a 29 % power gain.

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.

Currently, many researchers have an interest in the investigation of the electric field in the fair-weather electric environment, along with its diurnal and seasonal variations across all regions of the world. However, a similar study in the southern part of Siberia has not yet been carried out. In this regard, the study aims to estimate the mean values of the electric field and their variations in the mountain and steppe landscapes using measurement data from the Khakass-Tyva expedition in 2022. The maximum values of positive ion density were noted at the site in the Iyussko-Shirinsky steppe between Belyo and Tus salt lakes in the Khakass-Minusinsk ba-sin. The maximum values of negative ion density were observed at site in the Shol tract in the center part of the Tyva depression. The potential gradient tends to increase with altitude and reaches a maximum in the highlands. The maximum values of the potential gradient were noted in the highlands plateau near the Mongun-Taiga mountain massif and Khindiktig-Khol lake. The diurnal cycles of potential gradient at different observation sites were divided into two groups: 1) a diurnal cycle in the form of a double wave; 2) a daily cycle with a more complex course due to the strong influence of local factors.

This paper presents a novel real time control strategy on four-wheel independent steering system for electric vehicle with stepper motors using neural networks combined with fuzzy logic. The control of the four wheels is based on a variation of Hopfield Neural Network (VHNN) method, in which the input is the error of each steering motor and the output is processed by a hyperbolic tangent function (HTF) feeding the fuzzy logic controller (FLC), which ultimately drives the stepper motor. The whole system is made up of the four aforementioned blocks where they work in sync and inseparable from each other with the common goal of driving all the steering stepper motors at the same time. Simulation results demonstrate that the proposed control method has strong robustness and practicability under different angles and speeds.

This paper defines the two main causes of global warming in the UK and seeks to cut climate change using green hydrogen made from renewable electricity sources. Combustion vehicles powered by fossil gasoline and diesel emit a third of UK climate-warming CO2 while buildings heated by natural gas provide a quarter. First, current UK grid problems are defined: Electricity grid, gas grid and petroleum grid. Then experiments on the private energy community of Keele University allow electrolysis demonstrations producing green hydrogen for buildings and vehicle refueling. Next, the model supply chain is planned and tested. Finally, experiments and calculations are outlined, analyzing the optimum system design criteria proposed. We conclude that economic green hydrogen can displace diesel and gasoline in vehicle transport, while also displacing natural gas for combined heat and power in buildings. In addition, the prospect in 2023 is that profits can now be made all along the green hydrogen supply chain, such that new businesses involved in clean communities can beat the National Grid monopoly and other large dominant fossil grid companies.

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

In this work, linear confinement of a relativistic scalar particle under the effects of Lorentz symmetry violation is investigated. We introduce a scalar potential by modifying the mass via transformation M → M + S(r) in the wave equation, and analyze the effects on the eigenvalues and the wave function. We see that the solution of the bound state to the wave equation can be achieved, and the energy eigenvalues and the wave function modified by the Lorentz symmetry breaking parameters as well as potential

Metallic substrates and polymer adhesive in composite-metal joints have a relatively large coefficient of thermal expansion (CTE) mismatch, which is a barrier in the growing market of electric vehicles and their battery structures. It is reported that adding carbon nanotubes (CNTs) to the adhesive reduces the CTE of the CNT enhanced polymer adhesive multi-material system, therefore when used in adhesively bonded joints it would, theoretically, result in low CTE mismatch in the joint system. The current article presents the influence of two specific mass ratios of CNTs on the CTE of the enhanced polymer. It was observed that the addition of 1.0 wt% and 2.68 wt% of multi-walled CNTs (MWCNTs) decreased the CTE of the polymer adhesive from 7.5e-5 1/C (pristine level) to 5.87e-5 1/C and 4.43e-5 1/C, respectively by 22% and 41% reduction. The reduction in the CTE was predicted, theoretically, which showed that CTE should have been reduced to 3.6e-5 1/C (52% reduction) and 1.4e-5 1/C (81% reduction). This may be due to the fact that, Raman spectroscopy of the MWCNTs identified defects in the raw material, and scanning electron microscopy (SEM) identified agglomeration of MWCNTs on the surface and cross-section of the modified polymers.

To harness light-matter interaction at nano-/micro-scale better control tools have to be developed. Here, it is shown that by applying external electric and/or magnetic field, ablation of Si and glass under ultra-short (sub-1~ps) laser pulse irradiation can be controlled via the Lorentz force $\mathbf{F} = e\mathbf{E} + e[\mathbf{v}\times\mathbf{B}]$, where $\mathbf{v}$ is velocity of charge $e$, $\mathbf{E}$ is the applied electrical bias and $\mathbf{B}$ is the magnetic flux density. The external electric E-field was applied during laser ablation using suspended micro-electrodes above glass substrate with an air gap for the incident laser beam. The counter-facing Al-electrodes on Si surface were used to study debris formation patterns on Si. Debris was deposited preferentially towards the negative electrode in the case of glass and Si ablation. Also, an external magnetic field was applied during laser ablation of Si in different geometries and is shown to affect ripple formation. The vectorial nature of the Lorentz force widens application potential of surface modifications and debris formation in external E-/B-fields with potential applications in mass and charge spectroscopes.

We previously reported new superconductivity produced by an electrostatic field and a diffusion current in a semiconductor without refrigeration. In particular, the superconductivity was investigated theoretically and confirmed experimentally. Here, we determine that the derived superconducting quantum state can be reproduced in a capacitor. When circuits are formed with this new-type capacitor and diodes, a magnetic field is applied to the diodes’ depletion layer. The depletion layer is biased because of the conversion from the magnetic-field energy to electric-field energy, resulting in the diodes’ spontaneously emitting a current. Thus, the new-type capacitor is charged using no other energy source. This new phenomenon is described theoretically with assistance of initial experiments.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

The widespread diffusion of electric vehicles poses new challenges in the field of fault diagnostics. Past studies have been focused mainly on machines designed for industrial applications, where the operating conditions and requirements are significantly different. This work presents a review of the most recent studies about fault diagnosis techniques in electrical machines feasible for traction applications, with a focus on the most adopted approaches of the last years and on the latest trends. Considerations about their applicability for electric vehicle purposes, along with some areas that require further research, are also provided.

The zinc oxide (ZnO) arrester is positioned on locomotive roof preventing other onboard electrical equipment from the harm caused by external and internal overvoltages. A number of locomotive arrester burnout and explosion accidents occur during traction supply network harmonic resonance causing train stoppages and power outages in the network. This study firstly carries out high-frequency tests to investigate the wide frequency band electrical characteristics of the locomotive arrester, explaining that the arrester is prone to fault during resonance. Consequently, a prognostics and health management (PHM) for locomotive arrester is proposed providing on online monitoring, fault warning and health assessment solutions, which is beneficial to the operation and maintenance of arresters in the future. The testing method and PHM scheme presented in this paper can be not only applied to the arrester, also readily expanded to other high-voltage electrical equipment on the electric locomotive.

Electric mobility is a global trend and necessity, with electric and solar boats offering a promising alternative for transportation electrification and carbon emission reduction, especially in the Amazon region. This study analyzes the system of a solar boat from the SIMA project, to be implemented at UFPA, using MATLAB software for modeling. The Simulink tool was utilized to model the system, focusing on operational parameters such as module voltage, converter voltage, and speed. Results indicate that the solar boat's operational cost is significantly lower compared to a similar internal combustion model, considering diesel's high consumption and cost. The environmental impact is also reduced, with nearly 72 tons of CO2 emissions avoided annually, thanks to Brazil's renewable energy matrix. Simulations confirmed the project's parameters, demonstrating the efficiency of digital twin technology in monitoring and predicting system performance. The study underscores the importance of digital twins and renewable energy in promoting sustainable transportation solutions, advocating for the replication of such projects globally. Future research should focus on further advancing digital twin applications in electric mobility to enhance predictive maintenance and operational efficiency.

This proposal advocates using energy states as fundamental units for quantum computer programming. Each programming unit is conceptualized as an energy state, manifested as an essence-unit. The essence-unit, defined as the minimal form encapsulating complete uniqueness or specificity, serves as the cornerstone of this paradigm. This approach allows recording various forms of matter on quantum computers in essence-units representing their energy states. Recording energy states is achieved through the creation of four distinct coherent potentials facilitated by quantum dots or crystals. It is crucial to note that these energy states, embodied in essence-units, cannot be subdivided. The study examines the intricate relationship between similarity, fractals, and uniqueness in quantum dot operations, elucidating their profound implications for information transfer efficiency. Normalized entropy quantifies the charge localization in quantum dot impurities in systems exhibiting distortions. Utilizing N-level recording and entropy-fractal dimension equivalence enables this paper to elucidate the potential of quantum dots in reducing transmission time and modeling complex systems. The proposed methodology represents a significant departure from the current state of quantum computing, offering unprecedented potential for overcoming previously intractable challenges.

End-of-life electric vehicle (EV) batteries can be reused to reduce their environmental impact and economic costs. However, the growth of a second-life market is limited by the lack of information on the characteristics and performance of these batteries. As the volume of end-of-life EVs may exceed the amount of batteries needed for stationary applications, investigating the possibility of repurposing them in mobile applications is also necessary. This article presents an experimental test that can be used to collect the data necessary to fill a battery passport. The proposed procedure can facilitate the decision making process regarding the suitability of a battery for reuse at the end of its first life. Once the battery passport has been completed, the performance and characteristics of the battery are compared with the requirements of several mobile applications. Mobile charging stations and forklift trucks were identified as relevant applications for the reuse of high capacity prismatic cells. Finally, a definition of a state of health (SoH) is proposed to track the suitability of the battery during use in the second life application considering not only energy but also power and efficiency of the battery. This SoH shows that even taking into account accelerated ageing data, a repurposed battery can have an extended life of 11 years at 25~°C. It has also been shown that energy fade is the most limiting performance for lifetime and that cell-to-cell variation should be tracked as it has been shown to have a significant impact on battery life.

An effective battery management system (BMS) is indispensable for any lithium-ion battery (LIB) powered systems such as electric vehicles (EVs) and stationary grid-tied energy storage systems. Massive wire harness, scalability issue, physical failure of wiring, and high implementation cost and weight are some of the major issues in conventional wired-BMS. One of the promising solutions researchers have come up with is the wireless BMS (WBMS) architecture. Despite research and development on WBMS got momentum more than a decade ago, it is still in a preliminary stage. Significant further upgradation is still required towards developing an industry-ready WBMS, especially for high-power LIB packs. Therefore, an in-depth survey exclusively on WBMS architectures is presented in this article. The aim is to provide a summary of the existing developments as well as to present an informative guide to the research community for future development by highlighting the issues, emerging trends, and challenges. In-depth analysis of the existing WBMS topologies will not only help the researchers to understand the existing challenges and future research scopes clearly while at the same time enthuse them to focus their research inclination in the domain of WBMS.

Geomagnetic induced current (GIC) is a ground end manifestation associated with the space weather perturbations that should be greatly taken into account by the society. Although the GICs implication to the power system is not regular, it can cause large scale of system failure. In equatorial, the power system is considered safe since the most intense of geomagnetic storm happened in high latitude. However, the internal damage due to GICs which finally led to the South African power system failure has totally changed the normal perception. Therefore, a preliminary investigation on the GICs activity in equatorial region is performed to understand the space weather impact to the power system. Time derivative of the horizontal magnetic field component (dB/dt) is done to indicate the GICs activity value based on Faraday’s law. All the reported power failures are compiled to produce the threshold value of dB/dt, which possibly cause the harmful effect to the system. Then, dB/dt analysis is extended to show the pattern of GICs activity in function of magnetic latitude and local time. The results reveal that power network in equatorial region has possibly suffered by GIC. Plus, high number of intense GIC activity in this region occurred during dayside.

Large lithium-ion batteries (LIBs) in electric vehicles and energy storage systems demonstrate different performance and lifetime compared to small LIB cells, owing to the size effects generated by the electrical configuration and property imbalance. However, the calculation time for performing life predictions with three-dimensional (3D) cell models is undesirably long. In this paper, a lumped cell model with equivalent resistances (LER cell model) is proposed as a reduced order model of the 3D cell model, which enables accurate and fast life predictions of large LIBs. The developed LER cell model is validated via the comparisons with results of the 3D cell models by simulating a 20-Ah commercial pouch cell (NCM/graphite) and the experimental values. In addition, the LER cell models are applied to different cell types and sizes, such as a 20-Ah cylindrical cell and a 60-Ah pouch cell.

A very high parking occupancy can negatively influence the traffic performance of an area by causing very long cruising times. A very low parking occupancy, on the other hand, is inefficient from a space utilization perspective. Thus, this paper proposes a framework to compute the optimal parking occupancy rate over a given time horizon based on a macroscopic traffic and parking model. This rate is set high enough to ensure an efficient usage of the parking infrastructure. However, it should also guarantee a high likelihood of finding parking in order to eliminate the drivers’ time wasted in cruising for parking and the added congestion it causes. The model outputs are based on small data collection efforts and low computational costs, and they can be generated without complex simulation software using a simple numerical solver. Multiple vehicle types are included into our methodology allowing us to generate insights about the optimal parking occupancy with or without differentiated parking (i.e., parking for specific vehicles, such as fuel and electric vehicles). In times of a modal shift towards electric vehicles, cities can use our model to evaluate how much parking supply (with battery charging opportunities) they would like to dedicate to electric vehicles in order to achieve optimal traffic and parking results, and whether a differentiated or semi-differentiated parking policy is desirable. We illustrate our framework in a case study of a central area within the city of Zurich, Switzerland, showing the traffic and parking impacts (e.g., average searching time for parking, total revenue created by parking fees, optimal parking occupancy rate) for different proportions of fuel and electric vehicles in the parking demand and/or supply. Our results confirm that optimal occupancy rates are between and for most realistic scenarios. We then discuss how these rates might change depending on various demand and supply relationships, and according to different parking policies. We show that equal proportions between electric vehicles in the demand and their parking spaces in the supply lead to the best traffic performance in the area. We also provide the tools for cities to analyze their loss in performance if they do not react, e.g., to an increasing demand for electric vehicles over time. Moreover, we illustrate how some of these risks can be mitigated by having more flexible parking policies, e.g., allowing electric vehicles to use parking spaces for fuel vehicles.

Natural time is a new time domain introduced in 2001. The analysis of time series associated with a complex system in natural time may provide useful information and may reveal properties that are usually hidden when studying the system in conventional time. In this new time domain, an entropy has been defined and complexity measures based on this entropy as well as its value under time-reversal have been introduced and found applications in various complex systems. Here, we review these applications in the electric signals that precede rupture, e.g., earthquakes, in the analysis of electrocardiograms, as well as in global atmospheric phenomena like the El Nino/La Nina Southern Oscillation.

This paper presents an advanced and highly sophisticated model for hybrid electric special-purpose machines (HE-SPMs), specifically engineered to significantly enhance sustainability and efficiency in logistics operations. The primary objective of this model is centered around the optimization of energy flow, a feature that has undergone extensive testing and validation to ensure its reliability and effectiveness. The inclusion of auxiliary systems such as cutting-edge cooling and control systems further elevates the overall performance capabilities of the HE-SPMs, resulting in a seamless integration within various industries. By implementing these enhanced systems, substantial reductions in energy consumption and operational costs have been observed and well-documented through compelling case studies in both the construction and transportation sectors. The holistic nature of this approach impeccably aligns with the ongoing global efforts aimed at combating the pressing issue of climate change. By focusing on the development and utilization of eco-friendly technologies, this model actively contributes to the mitigation of harmful environmental impacts and promotes sustainable practices within the logistics industry. To further reinforce the efficacy of this remarkable model, future research endeavors will concentrate on expanding its applications to encompass other logistics domains.

Electro-mechanical braking(EMB) which represents development direction of autonomous and intelligent braking plays very useful role on enhancing the braking response performance and intelligence level for mine underground electric trackless rubber-tired vehicle (ETRV). However,there is no Electro-Mechanical Brake system applied practically in coal mine UTRV until now.The accurate control of braking clamping force can determine the length and precision of braking distance of mine ETRV.In addition,because of poor working conditions in underground coal mine tunnel ,braking clamping force sensor may be easy to occur failure,which may cause vital accident.Therefore,A cascaded three-closed-loop EMB system with a positive clamping force sensor or force estimator built for ETRV is established to achieve autonomous elimination and reset of braking clearance and reliable braking force tracking ability via utilizing the electro motor rotor angle displacement and hysteresis effect of mechanical components in this EMB.The results of simulation and experiment indicate that clamping force response is faster than traditional hydraulic disk braking system and proposed force estimator presents good fault-tolerant ability when sensor is fault.This EMB can replace the current hydraulic brake system to enhance automation level of braking control for ETRVs and braking force response performance, reduce oil pollution and cost of maintenance, which is also key technology support for really precise motion control of mine autonomous driving vehicles.

Synchronous homopolar machines (SHM) with excitation winding on the stator have attracted the attention of researchers for many decades due to their high reliability. They are used in a variety of applications such as aircraft and railway generators and traction motors. At the same time, the three-dimensional structure of their magnetic circuit makes the problem of their analysis and optimization challenging. This problem becomes even more complicated when considering an SHM with hybrid excitation, with ferrite magnets in the rotor slots. The article proposes a mathematical procedure for optimizing a synchronous homopolar generator with hybrid excitation based on the single-criterion Nelder-Mead method and two-dimensional finite element analysis (FEA). The use of the Nelder-Mead method compared to multicriteria methods and 2D FEA compared to 3D FEA makes possible to significantly reduce the computational burden. As a result of optimization, the power loss and torque ripples of the generator are significantly reduced. The article also compares the characteristics of synchronous homopolar generators with and without ferrite magnets.

This paper is a mini literature review about electromagnetic field’s effects on cells, tissues and new treatment modalities. We have reviewed a papers which have been published in high quality journals in the last 5 years as two authors. This review’s aim is to be a resource for experimental studies about electricity’s effects on cell biology and pathophysiology. As a result of this literature review, we found that especially extremely low electric frequency and intermediate frequency fields have very important pathophysiological effects. We have mentioned four important expressions on this subject; electroporation, electrofusion, electrochemotherapy, gene electrotransference. Two different new treatment approaches have been developed by use of these two important electrical waves. First is tumor treating fields and the other is pulsed electric fields. Most studies in literature have been done with extremely low frequency and pulsed electric fields. In terms of diseases, most studies are about glioblastoma multiforme and malign melanoma.Most studies in literature have been done with extremely low frequency and pulsed electric fields. In terms of diseases, most studies are about glioblastoma and melanoma.

In this work, we focus on investigating electrothermal flow in a rotating electric field (ROT-ETF), with primary attention paid to the horizontal traveling-wave electrothermal (TWET) vortex induced at the center of the electric field. The frequency-dependent flow profiles in the microdevice are analyzed using different heat transfer models. Accordingly, we address in particular the importance of electrode cooling in ROT-ETF as metal electrodes of high thermal conductivity while substrate material of low heat dissipation capability are employed to develop such microfluidic chips. Under this circumstance, cooling of electrode array due to external natural convection on millimeter-scale electrode pads for external wire connection occurs and makes the internal temperature maxima shift from the electrode plane to a bit of distance right above the cross-shaped interelectrode gaps, giving rise to reversal of flow rotation from a typical repulsion-type to attraction-type induction vortex, which is in good accordance with our experimental observations of co-field TWET streaming at frequencies on the order of reciprocal charge relaxation time of the bulk fluid. These results point out a way to make a correct interpretation of out-of-phase electrothermal streaming behavior, which holds great potential for handing high-conductivity analytes in modern microfluidic systems.

The paper explores the features of numerical simulation to analyze the dynamic behavior of drives controlled by pulse-width modulators. Modern motor control systems widely use pulse width modulation. Effective numerical modeling of such systems has specific features because the models employed are continuous-event and have hybrid behavior due to the presence of nonlinear links with discontinuities of the first kind. Therefore, it is essential to have special integration methods with variable steps, which should be factored in when developing the model. The paper shows how these problems are solved when modeling an electric drive with a DC motor in the SimInTech software.

This paper introduces an innovative energy circuit that challenges conventional understandings of energy conservation, marking a significant departure from traditional frameworks. Seeking to enhance energy generation within existing paradigms, this innovation holds promise for addressing pressing global issues such as the energy crisis and environmental sustainability, while also stimulating scientific inquiry. In contrast to prevalent misconceptions rooted in philosophical and scientific constraints, this study refutes the notion of generating energy from nothing and questions the viability of perpetual motion machines as conceptual models. Instead, it proposes a novel circuit design derived from an unconventional electrical short circuit, which is shown to disrupt the established principles of energy conservation. This circuit offers a transformative approach with diverse applications, including self-recharging capabilities for electric vehicles, bolstering microgrid infrastructure, and facilitating the integration of renewable energy sources. Transcending conventional scientific boundaries, this paper prompts philosophical reflections on the evolving nature of scientific exploration. Ultimately, the energy circuit represents a pivotal step towards mitigating the global energy crisis by reducing reliance on finite resources and fostering sustainability. The paper concludes by illustrating the circuit's potential to revolutionize energy systems and contribute to a more resilient and sustainable future.

Abstract: Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

The Huaniushan lead-zinc-silver deposit is a hydrothermal sedimentary exhalative deposit (SEDEX). The mining area has complex geological conditions. The main tectonic structure in the area is the Huaheitan-Shuangfengshan Fault (F3), which controls the distribution of strata and magmatic rocks. Since the discovery of the Huaniushan Lead-zinc-silver polymetallic deposit, there are diverse understandings of the deposit's genesis and mineralization law, making it challenging to meet the geological interpretation requirements of the Huaniushan Lead-zinc-silver polymetallic deposit. Moreover, a single geophysical exploration method utilizes limited rock physical parameters, making it difficult to effectively characterize underground structures. The integrated exploration of multiple geophysical methods can effectively integrate the geophysical characteristics of different rock physical parameters, reducing the multiplicity and uncertainty of geophysical data inverse interpretation. The comprehensive interpretation of three-dimensional inversion based on various geophysical data, the construction of geological-geophysical models based on geological grounds, the establishment of hidden ore exploration and positioning, and the rapid evaluation of geophysical technology systems are the current development trends in mineral exploration. In light of this, this paper conducts research on the three-dimensional inversion interpretation of the heavy magnetoelectric exploration data of the Huaniushan sedimentary exhalative deposit lead-zinc-silver polymetallic deposit in the mining area and constructs a three-dimensional geological-geophysical model of the study area based on the obtained three-dimensional physical structure of the underground three-dimensional density, magnetization intensity, resistivity, and polarizability structure of the study area, in combination with the information of related geology and drilling holes. It comprehensively interprets the favorable mineralization parts in the study area.

Photovoltaic (PV) systems, which are renewable energy sources, are increasingly being utilized in distributed generation. To maintain a stable energy balance, energy storage systems such as batteries (BAT) play a vital role. Electric vehicles (EVs) offer a promising solution with rechargeable battery systems for operation. The BATs must maintain their state of charge within design boundaries, despite intermittent PV and load power fluctuations, and advanced power control and management techniques are essential for their effective operation. The article evaluates an intelligent controller that uses a Sliding Mode Control adaptive deep learning algorithm Convolutional Neural Networks (SMC-CNN) and a unique Strategy for managing supervisory power. (SPMS) for photovoltaic systems with battery energy storage for better regulation of DC bus voltage. SMC-CNN offers exceptional resilience and seamless functionality, minimizing fluctuations in DC bus voltage in the control strategy design requirements. The primary goals are maintaining a consistent power supply and ensuring continuous service by preventing system components from exceeding capacity. This study aims to enhance the control of DC bus voltage in the PV and battery system, with the primary significance focusing on the following aspects. The advanced SPMS has been created, incorporating control system constraints to enhance SOC balancing speed and reduce fluctuations in DC bus voltage. Power flow management involves optimizing energy flow between the PV system, battery system, and load while minimizing battery capacity requirements. The proposed SMC-CNN and SPMS are demonstrated through real-time simulation using Matlab/Simulink, as demonstrated in comprehensive case studies.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

In Thailand, there are several bus fleets—predominantly diesel buses—which is an internal combustion engine (ICEs) in circulation. These fleets are consumed a lot of energy and cause pollution, especially by emitting GHGs and PM2.5. Battery electric buses (BEBs) have been proposed to address these issues. BEB fleets still have a high initial cost, which is why most fleet governance agencies, such as the Bangkok Mass Transit Authority (BMTA), are facing investment decisions. This study aims to develop an energy model for the BMTA BEB fleet by using their real operating data. The methodology for the development of the investment model with two steps is described; there is a BMTA bus route analysis step and a fleet management and charging design step. The output is illustrated in terms of the maximum number of BEBs that can be operated on a sample route with the minimum number of chargers. Interesting results were obtained with the first step: Two of the five BMTA bus routes can be changed into BEBs in phases with a limit of 200 kWh for the energy requirements for every two rounds. The results from the second step demonstrated that the maximum number of BEBs for the two routes was 13, with four charger plugs of charger, thus requiring 150 kW per plug. The charging profile peaked at 600 kW from 4:00 to 8:00 p.m. These results show the potential of the model for fleet design and investment decisions. The sample results from the models illustrate energy savings and cost evaluations for fleet management and design. Compared with diesel buses, THB 10.44 million per year can be saved in terms of energy costs by using a BEB fleet. An optimization model was used to assess the savings incurred due to the investment cost. More than 37% of the costs were saved. A full economic investigation should be carried out in the future. These results show only the energy used when the fleet has already been transformed into a BEB fleet in phases. The emphasis of battery size investigation and energy used were illustrated. They only depict an economic validation of the model, but do not refer to such projects’ feasibility, and the model has not yet been fully validated.

Carbon footprint mitigation actions have become a key issue for companies involved in urban freight transport. The electrification of the delivery fleets is one of the crucial sustainable actions carried out. To this end, it is necessary to explore several fleet renewal strategies over a finite planning horizon evaluating different types of electric powertrains for light commercial vehicles. The primary goal of this purpose-built analysis is to assist and boost the fleet managers' decisions when transitioning to alternative-fuelled vans, intending to maximize cost savings and reduce greenhouse gas emissions inventory. The model presented in this work is a Multi-Objective Linear Programming hybrid analysis taking into account the vehicle and energy supply infrastructure costs and the emissions reported in each inventory scope following the Greenhouse Gas Protocol sustainability guidelines. From this perspective, the conducted research offers a novel approximation to fleet management problems enabling a break-down cost analysis and an evaluation of the annual emissions company report. The comprehensive numerical simulations carried out over different study scenarios in Spain demonstrate that the optimization approach does not only show effective fleet renewal strategies but also identifies critical factors that impact the fleet's competitiveness, offering valuable insights for fleet managers and policymakers to meet carbon footprint reductions in a cost-effectively way.

In this paper, the performance of Cu-(In,Ga)-S2 (CIGS2) solar cells with adjusting composite CGI-ratio absorber is explored and compared through an improved three-stage co-evaporation technique. For co-evaporating CIGS2 absorber as a less toxic alternative to Cd-containing film, we analyse the effect of the CGI-ratio stoichiometry and crystallinity, and explore its opto-electric sensing characteristic of individual solar cell. The results of this research signify the potential of high-performance CIGS2-absorption solar cells for photovoltaic (PV)-module industrial applications. For the optimal CIGS2-absorption film (CGI=0.95), the Raman main-phase signal (A1) falls at 291 cm-1, which is excited by the 532-nm line of Ar+-laser. Using photo-luminescence (PL) spectroscopy, the corresponding main-peak bandgaps measured is 1.59 eV at the same CGI-ratio film. Meanwhile, the best conversion efficiency (=3.212%) and the average external quantum efficiency (EQE=51.1% in the visible-wavelength region) of photo-electric properties were achieved for the developed CIGS2-solar cells (CGI=0.95). The discoveries of this CIGS2-absorption PV research provide a new scientific understanding of solar cells. Moreover, this research undeniably contributes to a major advancement towards practical PV-module applications and can help more to build an eco-friendly community.

A modular approach to the construction of electric machines, drive systems, power supply systems is a new direction of modern technology development. Especially, the modular approach is promising for electric vehicles due to such positive aspects as increased efficiency, fault tolerance, overall reliability, safety, enhanced control capabilities, etc. In this work, the modular approach is comprehensively applied to an EV powertrain system, which includes a dual three-phase (DTP) BLDC motor with two machine modules of an asymmetric configuration, two battery modules and a supercapacitor module (SCM). The proposed H–H configuration of modular EV powertrain system includes four voltage source inverters that combine the power modules with the open ends of the windings (OEW) of the module machine armature, and provide control of their operation. Based on the developed mode system of the OEW machine module operation for EV traction and braking, a general control algorithm for the proposed configuration of the modular EV powertrain system has been developed. It combines the control of the operating modes with the functions of maintaining the required SOC level of the SCM and equalizing the SOCs of the two battery modules. The conducted simulation and experimental studies confirmed the workability and effectiveness of the proposed solutions.

Electrical microgrids are deemed to be the future of modern power systems. Microgrids are sophisticated, decentralized, and self-sufficient small-scale power systems consisting of various resources ranging from wind turbines, solar photovoltaics, electric vehicles, smart energy storage, and complex communication infrastructure. However, renewable energy sources such as solar photovoltaics and wind-based generators are highly intermittent, and if not appropriately planned, they can compromise the stability of the grid. Formal methods can define and analyze the functionality and behavior of any system and show if the system design is correct before the actual system is implemented. Although formal methods have been around for many years, it is surprising that little to none are utilized in the design of safety-critical electrical power systems. Currently, in modeling microgrids, few to no attempts of formalization are being used to improve the design reliability and reduce system operating costs and time. This work demonstrates how complex systems such as microgrids can be modeled elegantly using a formal specification method. In this work, the Z state-based formal specification language (Z-Method) is used to model and verify microgrid designs. In this work, 3-interconnected microgrid systems with a high penetration level of solar and wind-based renewable energy sources with plugin hybrid electric vehicles (PHEV) as battery energy storage systems (BESS) are modeled using the Z-method. To the best of authors, the knowledge presented formal method is one of the first reported attempts in modeling microgrid communities using Software Engineering formalism.

Solid-state batteries (SSBs) hold the potential to revolutionize energy storage systems by offering enhanced safety, higher energy density, and longer life cycles compared with conventional lithium-ion batteries. However, the widespread adoption of SSBs faces significant challenges, including low charge mobility, high internal resistance, mechanical degradation, and the use of unsustainable materials. These technical and manufacturing hurdles have hindered the large-scale commercialization of SSBs, which are crucial for applications such as electric vehicles, portable electronics, and renewable energy storage. This paper provides a systematic review and cross-sectional bibliometric and thematic analysis of the global patent landscape for SSB technologies. The study classifies innovations into key problem and solution areas by meticulously examining 244 patents across multiple dimensions, including geographic distribution, inventor engagement, award latency, and technological focus. Key findings reveal progress in addressing performance, safety, material sustainability, and manufacturing scalability, with significant innovations in electrolyte, electrode, architectural, and process engineering. This research contributes a comprehensive analysis of the technological landscape, offering valuable insights into ongoing advancements and providing a roadmap for future research and development. This work will benefit researchers, industry professionals, and policymakers by highlighting the most promising areas for innovation, thereby accelerating the commercialization of SSBs, and supporting the transition toward more sustainable and efficient energy storage solutions.

Dynamic wireless charging (DWC) for electric vehicles (EVs) is gaining traction as a more accessible charging method, with the added benefits of extending vehicle range and reducing battery size. By providing a charging connection through the road surface, DWC offers an additional charging option without the need for a physical connection. This approach inherently links the charging load profile to the vehicle's movement relative to the charging pad(s), thus introducing more stochastic charging events to the grid. For dynamic wireless power transfer (DWPT), these events involve high instantaneous power and are short in duration. To better understand the impact of DWPT, accurate models are required to test control systems and potential solutions. Additionally, these systems require high-frequency simulation for wireless power transfer (WPT), which results in long simulation times during development. In this paper, a simplified model is presented to ensure the efficiency of grid-connected simulations and achieve a certain level of accuracy that reflects the internal dynamics of wireless charging. This model achieves a 30-fold reduction in simulation time with only a 6% deviation from the base model.

The increase in greenhouse gas emissions fosters the transport sector towards new technological perspectives on personal mobility. Addressing sustainable mobility through electric micromobility requires interdisciplinary design research methods and approaches. In the context of the LEONARDO project, funded under the Horizon 2020 framework, the paper addresses a critical literature review on the design thinking, design research models, tools, and mixed methods to be undertaken for driving product mobility innovation in a cross-disciplinary context. Following the “research through design” research strategy, the authors applied the Double-Diamond design thinking model to frame the design research process in 4 phases, aligning with 3 general objectives, 12 specific research objectives, and 24 tasks, supported by a total of 71 mixed methods and tools. As a result, the transdisciplinary process provides a co-designed energy-efficient stand-alone microvehicle and a scalable interdisciplinary design model for urban transport product innovation. In conclusion, this case study suggests the value of the Double-Diamond design thinking model as a design research instrument capable of addressing sustainable mobility and guiding interdisciplinary design research, design practice, and education in the Industrial Engineering and Design disciplinary sectors.

In this paper, using the two integers that describe the stationary 2-dimensional wave and the charge quantization along with the balance between the Lorentz force and electrical force, we succeed in deriving the fractional quantum Hall effect and the integer quantum Hall effect; we find that the latter exists as a special case of the former. Moreover, using the derived expression describing the fractional quantum Hall effect, a relationship between the plateau in the resistivity of the sample and the applied magnetic field is obtained. The findings of this model agree well with experimental measurements. Because the two integers that describe the stationary 2-dimensional wave and the charge quantization along with the force balance have concrete physical meanings in this work, we could provide a clear picture of the origin of both the integer quantum Hall effect and the fractional quantum Hall effect.

Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.

Background: In Transcranial Direct Current Stimulation (tDCS) the injected current gets distributed across the brain areas. The motive is to stimulate the target region-of-interest (ROI), while minimizing the current in non-target ROIs. For this purpose, determining the appropriate current-dose for an individual is difficult. Aim: To introduce Dose-Target-Determination-Index (DTDI) to quantify the focality of tDCS and examine the dose-focality relationship in three different populations. Method: Here, we extended our previous toolbox i-SATA to the MNI reference space. After a tDCS montage is simulated for a current-dose, the i-SATA(MNI) computes the average (over voxels) current density for every region in the brain. DTDI is the ratio of average current density at target ROI to the ROI with maximum value (peak region). Ideally target ROI should be the peak region, so DTDI shall range from 0 to 1. Higher the value, the better the dose. We estimated the variation of DTDI within and across individuals using T1-weighted brain images of 45 males and females distributed equally across three age groups- (a) Young adults (20 ≥ x ˂ 40 years), (b) Mid adults (40 ≥ x ˂ 60 years), and (c) Older adults (60 ≥ x ˂ 80 years). DTDI’s were evaluated for the frontal montage with electrodes at F3 and right supra-orbital for three current doses 1mA, 2mA, and 3mA with the target ROI at left middle frontal gyrus. Result: As the dose is incremented, DTDI may show (a) increase, (b) decrease, and (c) no change across the individuals. The focality decreases with age and the decline is stronger in males. Higher current dose at older age can enhance the focality of stimulation. Conclusion: DTDI provides information on which tDCS current dose will optimize the focality of stimulation. DTDI recommended dose should be prioritised based on the age (> 40 years) and sex (especially males) of an individual. The toolbox i-SATA(MNI) is freely available.

Directional protective relaying based on amplitude comparison of traveling wave on continuous co-phase auto-transformer(AT) power transmission system was proposed. Phase-model transformation is used for decoupling transient fault signals and get aerial mode component. The forward and backward traveling wave are obtained by calculating aerial mode component, then the intrinsic mode function(IMF) components can be obtained through Variational Mode Decomposition(VMD), the module maxima are obtained through calculating the IMF components. The fault direction is determined by the ratio of fault components. If two faults relays at the ends of electric traction networks detect a fault to be in the forward direction, the fault occurred in the internal area, the protection would operate properly. The simulation tests indicate that the protection scheme is feasible, and the proposed protection method can discriminate internal faults from external faults under various fault types, and its performances are immune to fault initial angle, ground resistance, etc.

Rechargeable solid-state batteries (SSBs) offer enhanced safety, higher energy density, and longer cycle life compared with conventional lithium-ion batteries, making them a promising solution for electric vehicles, grid storage, and other high-demand applications. However, challenges such as interface stability, low ionic conductivity, and manufacturing scalability hinder their commercial viability. This research provides a comprehensive cross-sectional thematic and bibliometric analysis of recent SSB research, focusing on four key areas: electrolyte engineering, electrode engineering, battery architecture, and performance evaluation. By analyzing publications from major databases, this study highlights the latest innovations, including improved solid electrolytes, advanced electrode materials, and novel architectures that enhance both performance and manufacturability. The findings offer valuable insights for researchers, engineers, and industry stakeholders by identifying critical gaps in the field and highlighting future research directions. The work examines progress in interface optimization, ion transport improvements, and scalable production techniques, which are crucial for advancing SSB technology. By presenting a detailed review of key developments and challenges, this study contributes to a deeper understanding of SSB progress and its potential to revolutionize the energy storage industry. The results will guide researchers in targeting high-impact areas and help industry leaders and policymakers strategize the commercial deployment of SSBs in electric vehicles, stationary energy storage, and other applications requiring reliable and efficient energy solutions.

The urgency for a shift towards cleaner and more efficient energy alternatives for power generation is underscored by the escalating demand for energy and the detrimental environmental impacts of conventional fossil fuels. In the case of automotive power, electric vehicles are being adopted in many applications, however, challenges remain such as extended charging periods and limited ranges between charges. Therefore, this research study conducts a techno-economic analysis of renewable energy technologies (including solar modules, wind turbines and piezoelectric materials) to augment the efficiency and sustainability of electric buses. The study examines the scope for these technologies to be adopted on double-decker electric buses in London in the United Kingdom. The solar modules are placed on the rooftop and sides of the bus generating 15.9 kWh/day and the wind turbine in the front bumper of the bus generates 8.3 kWh/day. However, the piezoelectric material generated only 22.6 Wh/day, rendering it an impractical inclusion in further analysis. Therefore, both the solar modules and wind turbines combined generate 24.2 kWh/day, which can increase the driving range by 16.3 km per day and this results in savings of 19.36 minutes for charging at the stations. Investing in such projects would have a positive return as the internal rate of return (IRR) and net present value (NPV) are 2.8% and £11,175 respectively. The annual revenue would be £6,712, and the greenhouse gas (GHG) reduction would be two metric tons annually. Electricity exported to the grid, the electricity export rate, and the initial investment are identified as key factors influencing power outage in a sensitivity analysis. In conclusion, this numerical modelling study paves the way for experimental validation towards implementation of renewable energy technologies on electric bus fleets.

We consider an alternative hypothesis of crater formation -- through an electric discharge machining (EDM) of the celestial bodies' surfaces. The source of the energy and the current itself may lie in the plasma infalling on a celestial body (from an extremely strong solar outburst or a nearby supernova). This mechanism may have been even more important than impact cratering in the early Solar System, with potential implications to the history of the dynamics of the System itself and solar activity in particular. In this light, the conclusion that shocked and metamorphosed minerals found within crater sites are evidence of hypervelocity impacts might be reconsidered. There are ambiguities that remain among sources of mineralogical formations. This study is ongoing with details of investigations supporting a new approach to crater formation not yet exhausted. Apart from mineralogical analysis and theoretical considerations regarding the large scale crater formation, we present a description of experimental studies of EDM acting at low pressures. Experimental results are made with crude setups and could easily be replicated on a larger scale with valuable research potential, as there is a well understood relation between the crater size and the energy needed for its production.

Electric moped scooter sharing services have recently experienced strong growth rates, particularly in Europe. Due to their compactness, environmental-friendliness and convenience, shared e-mopeds are suitable modes of transport in urban mobility to help reduce the environmental impact. However, its traffic-related, economic and environmental effects are merely represented in academic research. We used passenger car traffic data in Berlin generated by the multi-agent transport simulation framework MATSim to develop a python-based simulation, resembling an e-moped sharing system. Based on the results, a total cost of ownership and a life cycle assessment for fleet sizes of 2,500, 10,000 and 50,000 vehicles were conducted. The results indicate that a substantial part of all passenger car trips in Berlin can be substituted. The larger the fleet, the more and longer trips are replaced. Simultaneously, the efficiency in terms of fleet utilization decreases. The scenario with 10,000 e-mopeds offers the lowest total distance-based costs for sharing operators, whereas a fleet consisting of 2,500 vehicles exhibits the lowest environmental emissions per kilometer driven over the expected lifespan of a shared e-moped. Based on the renewable energy potential for 2050 forecasted by the German Federal Environment Agency, a significant overall decline in environmental impacts can be achieved.

This study proposes a methodology for identifying time intervals in which time-of-use (ToU) energy tariffs can be set. This is achieved by analyzing the variability in electric power demand and esti-mating the potential for demand flexibility. The proposed methodology is as follows: first, data preprocessing is performed, focusing on the organization and cleaning of individual consumption records. Subsequently, the data are processed and classified by using consumption variability analysis and clustering techniques. Then, representative clusters are selected and time intervals are analyzed to identify periods of high electric power consumption variability. This methodology was tested with real data from smart meters in the Colombian electrical system, in which time intervals with potential for settinh ToU tariffs were ultimately identified. This work promotes the implementation of demand response strategies that enhance sustainability and facilitate the transition to a more resilient energy landscape both nationally and internationally. The findings are expected to contribute to the formulation of tariff schemes that encourage con-scious changes in electric energy consumption patterns.