Abstract: Aquaculture production systems rely on the reliable operation of mechanical and electromechanical equipment to maintain stable environmental conditions. In shrimp farming, failures in critical assets may directly affect dissolved oxygen availability and compromise production stability. Despite the operational importance of these systems, structured methodologies for asset criticality assessment and maintenance prioritization in aquaculture remain limited. This study proposes a multi-criteria decision-making framework based on the Analytic Hierarchy Process (AHP) to evaluate and prioritize critical assets in shrimp aquaculture production systems. The model integrates nine technical and operational criteria related to reliability, maintainability, operational exposure, and production impact. The proposed methodology was applied to three key assets in the Primary Production stage: mechanical aerators, turbines, and stationary engines. The results indicate that mechanical aerators exhibit the highest criticality score (0.350), followed by stationary engines (0.328) and turbines (0.322). These findings highlight the dominant operational role of aeration systems in maintaining dissolved oxygen levels and ensuring production stability in shrimp farming systems. The proposed framework demonstrates that multi-criteria decision models can effectively support maintenance prioritization by transforming expert knowledge and operational information into a structured and consistent evaluation process. The methodology provides a replicable decision-support tool that can assist managers and maintenance planners in improving asset management and resource allocation in aquaculture production systems.

Abstract: Despite many efforts to curtail drunk driving, alcohol-related traffic fatalities and injuries continue to be a major public health problem in the U.S. and most of the world. Technologies exist that prevent an automobile from starting if the driver’s breath alcohol exceeds 20 mg/dL, but these devices are only fitted to vehicles of individuals who have been convicted of Driving Under the Influence (DUI). A new approach must be taken to reduce the incidence of drunk driving by integrating an alcohol sensor system in vehicles as part of the delivered hardware. The system must be fast, accurate, and contactless--meaning that a forced exhalation is not required to measure the concentration of alcohol on the breath. We report on a novel device, the Driver Alcohol Detection System for Safety (DADSS) Breath Alcohol Sensor System, which uses the mid-infrared region of the electromagnetic spectrum, is designed to concurrently monitor alcohol and expired carbon dioxide (CO2) to accurately quantify the breath alcohol concentration in samples that have been diluted in the atmosphere before being measured. The system was validated in a research laboratory with 70 male and female volunteers in 187 individual study days. Participants were given various doses of alcohol to consume and then breath and blood samples were collected simultaneously. Pearson correlation coefficients between the DADSS Breath Alcohol Sensor system and blood samples indicate a strong correlation between the measures, with an overall Pearson correlation of 0.8875 over an alcohol concentration range of 0 - 220 mg/dL. These results indicate that Incorporating the DADSS system into motor vehicles has the potential to reduce the incidence of drunk driving.

Abstract: The widespread adoption of hydrogen fuel cell electric vehicles (FCEVs) is currently hindered by the significant cost and lack of geometric flexibility of conventional Type IV pressure vessels made from carbon fiber reinforced plastic (CFRP). These tanks are difficult to integrate into future vehicle platforms optimized for modular batteries. This study, therefore, presents a novel compressed hydrogen storage system (CHSS) based on a modular assembly of seamless steel cylinders. The objective of this approach is to create a design-flexible and cost-effective alternative that adapts to the limited installation space of modern electric vehicle architectures while offering a sustainability advantage through the high recyclability of steel. The system was specifically designed to meet the stringent requirements of the UNECE R134 regulation and subsequently subjected to rigorous experimental validation. The evaluation included all four test sequences required for component certification: Baseline Tests, Performance Durability Test, On-Road Performance Test and Fire Test. The successful validation demonstrates that the developed modular steel-based CHSS meets all relevant safety and performance requirements. It, therefore, represents a technically and economically promising technology that can make a decisive contribution to accelerating hydrogen mobility through its superior design flexibility and sustainability.

Abstract: Electric vehicles (EVs) have fundamentally changed the noise, vibration, and harshness (NVH) landscape of automotive powertrains. In the absence of masking inter-nal-combustion-engine noise, harmonic components such as gear whine, electric-motor orders, and inverter-related tones become more perceptible and more critical to vehicle re-finement. This review synthesizes the current state of the art in harmonic NVH engineer-ing for electric drivetrains, focusing on the interactions between gear geometry, manufac-turing variability, electromechanical coupling, structural transfer, and human sound per-ception. Classical mechanisms of gear-mesh excitation are revisited together with emerg-ing EV-specific challenges, including long-wavelength flank deviations, ghost orders, lightweight housing dynamics, and psychoacoustic sound-quality requirements. The re-view further examines recent progress in predictive and data-driven approaches, includ-ing machine-learning-based gear-noise modeling, digital-twin concepts, and virtual NVH assessment workflows. Overall, the literature shows that harmonic NVH engineering in EVs is evolving from a conventional gear-noise problem into a multidisciplinary sys-tem-level task integrating gear dynamics, manufacturing science, structural acoustics, electric-drive control, psychoacoustics, and data-driven optimization. This review pro-vides a structured synthesis of these developments and identifies key research gaps and future directions for the next generation of refined electric drivetrains.

Abstract: In recent years we have seen Large Language Models (LLMs) demonstrating robust reasoning capabilities comparable to human performance. This makes them increasingly appealing for driver assistance, where adaptation to dynamic human context is essential. Yet, research in this area remains fragmented, often focusing on isolated applications, lacking utilization of LLM's full potential to deliver integrated, context-specific support and action. This survey synthesizes recent advancements in LLM-driven occupant monitoring systems, focusing on their capabilities for interpreting driver states and acting appropriately, enabling a new generation of intelligent driver assistance. We critically examine pioneering frameworks, benchmarks, and foundational datasets that employ techniques like reasoning chains, multimodality, and human-in-the-loop feedback to create personalized and safe driving experiences. We lay out the current trends, limitations, emerging patterns, in addition to a novel human-centered evaluation of the field, providing researchers with a roadmap towards transparent and trustworthy in-cabin systems, that bridge safety with driver experience.

Abstract: The rapid electrification of road vehicles has fundamentally reshaped the priorities of noise, vibration, and harshness (NVH) engineering. In the absence of combustion-related broadband masking, tonal and order-related phenomena originating from the electric machine, inverter switching, and high-speed reduction gearing have become clearly per-ceptible and, in many cases, acoustically dominant. Consequently, drivetrain noise in electric vehicles can no longer be assessed at component level alone; it must be understood as a coupled system response shaped by excitation mechanisms, structural dynamics, transfer paths, radiation efficiency, and ultimately human perception. This review adopts a source-to-perception perspective and consolidates the principal physical mechanisms governing vibro-acoustic behaviour in integrated electric drive units. Electromagnetic force harmonics and torque ripple are discussed alongside transmission-error-driven gear mesh excitation, while bearing and shaft nonlinearities are examined in the context of high-speed operation. In addition, ancillary thermoacoustic and aerodynamic contribu-tions are considered, reflecting the increasingly integrated packaging of modern e-axle architectures. On this mechanism-oriented basis, dominant excitation types are linked to frequency-appropriate modelling strategies, spanning electromagnetic force extraction, multibody drivetrain simulation, structural finite element analysis, transfer path analysis, and acoustic radiation prediction. Particular attention is given to workflow integration across domains. Finally, the paper identifies research challenges that predominantly arise at system level, including multi-source interaction effects, installation-dependent trans-fer-path variability, emergent resonances in assembled structures, manufacturing-induced tonal artefacts, and the still limited correlation between predicted vibration fields and perceived sound quality.

Abstract: Vibrations of thin sheet-metal parts during robotic manipulation on a production line create a number of serious challenges for production process planning. Modeling the behavior of an elastic plate or shell as a function of the robot manipulator trajectory is typically performed using the finite element method (FEM) and requires significant computational effort. The time factor remains a key limitation for integrating operations involving flexible parts into the virtual commissioning process. In this work, a methodology is proposed that enables accurate real-time reproduction of the behavior of an elastic part during linear robotic manipulation. The approach is based on modeling the response of an elastic part to a prescribed base excitation using the FEM and on the development of a reduced model compliant with the FMI/FMU standard. This reduced model computes, in real time, the convolution of the precomputed base response with the acceleration profile corresponding to the robot TCP trajectory. This makes it possible to determine the total cycle duration, which consists of the part transfer time and the time required for vibration decay at the end of the trajectory down to an acceptable threshold, as well as to perform collision checking while accounting for the deformation of the flexible part. As a result, operations involving elastic parts can be integrated into the virtual commissioning process.

Abstract: The main purpose of the paper is to show the possibility of assessing the dynamic properties of crankshaft in the early design phase of engine development, without performing dynamic forced response simulation. This is achieved by carrying out modal analysis of crankshaft under existing boundary conditions, namely by taking the radial stiffness in main bearings and the masses of moving conrods and pistons into account. The spectra of eigenfrequencies and corresponding mode shapes as a result of such supported modal analysis are compared to those of free modal analysis, emphasizing the influence of the boundary conditions. To easily identify the modes and to compare them with each other, kinetic energy-based method is used, alongside visualization and animation of mode shapes. The examples of crankshafts considered in the paper are taken from model catalog of virtual engines of six different sizes and configurations, being compared to that of in-line 4-cylinder engine as the reference case. All types of modal analysis are performed on structured FE models of crankshafts using software tool AVL EXCITE™ Shaft Modeler.

Abstract: Plug-in hybrid electric vehicles (PHEVs) are expected to reduce fleet CO₂ emissions, yet their real-world performance often deviates substantially from type-approval expectations. This study examines whether traction battery capacity provides an independent explanatory signal for the test-to-reality CO₂ gap (gap%), or whether it primarily acts as a proxy for market segmentation and usage patterns. Using European on-board fuel and energy consumption monitoring (OBFCM) records for 457,555 PHEV observations (2021–2023) from 14 manufacturers, we estimate nested fixed-effects models and introduce engineered usage proxies describing charge-depleting operation (EUR), hybrid utilization intensity (HI), energy-into-battery intensity (EDE), and a real-world to type-approval fuel-consumption ratio proxy (ELP). Battery capacity alone explains limited variation in gap% (R² = 0.075), while adding segment/year/manufacturer fixed effects increases R² to 0.203 and adding usage proxies increases it to 0.826, with the battery coefficient attenuating from 19.6 to 8.9 percentage points per kWh. Allowing non-linear battery terms via cubic B-splines yields only a modest additional improvement (R² ≈ 0.829), although the conditional shape is non-monotonic. Importantly, the battery–gap association is strongly segment-dependent, ranging from −22.1 pp/kWh in medium vans to +10.5 pp/kWh in large cars. Robustness checks using model-identifier fixed effects (MS_Cn) with standard errors clustered by MS_Cn further attenuate the battery effect (p ≈ 0.085), whereas ELP remains strongly associated with gap%. Overall, battery capacity is informative for compliance analytics mainly as a proxy variable capturing segmentation and real-world usage, rather than a universal lever of PHEV CO₂ performance.

Abstract: Vehicle–infrastructure cooperative perception (VICP) overcomes the sensing limitations and field-of-view constraints of single-vehicle intelligence by integrating multi-source information from onboard and roadside sensors. However, in complex urban environments, system robustness—particularly regarding blind-spot coverage and feature representation—is severely compromised by occlusion (static and dynamic) and distance-induced point cloud sparsity. To address these challenges, this paper proposes a 3D object detection framework incorporating point cloud feature enhancement and spatial adaptive fusion. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined-SENet (R-SENet) attention module is embedded into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism—across pillars and intra-pillar points—to adaptively recalibrate key geometric features. Concurrently, a Feature Pyramid Backbone Network (FPB-Net) is constructed to enhance unified target modeling across varying distances via multi-scale extraction and cross-layer aggregation. Second, a Spatial Adaptive Feature Fusion (SAFF) module is introduced to resolve feature heterogeneity and spatial misalignment. By explicitly encoding feature origins and leveraging spatial attention, SAFF enables dynamic weighting and complementary fusion of fine-grained vehicle-side features and global roadside semantics. Experiments on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving Average Precision (AP) scores of 0.762 and 0.694 at IoU 0.5, and 0.617 and 0.563 at IoU 0.7, respectively. Furthermore, the inference speed satisfies real-time requirements, validating the method’s effectiveness and potential for engineering deployment.

Abstract: This paper presents a practical industrial hybrid control architecture that augments the widely deployed 49-rule Mamdani fuzzy supervisory PID controller with a lightweight online meta-tuner based on Soft Actor-Critic (SAC) reinforcement learning. While the inner 1 kHz fuzzy-PID loop remains fully deterministic and identical to the industrial baseline, a separate 10 Hz SAC agent autonomously adapts the three output scaling factors (α_Kp, α_Ki, α_Kd ∈ [0.5, 2.5]) of the fuzzy layer using an ONNX Runtime inference engine. The complete controller is implemented and experimentally validated on a real Siemens S7-1214C PLC (6ES7214-1AG40-0XB0) in a hardware-in-the-loop setup with a high-fidelity 5-DoF manipulator model incorporating measured friction, backlash, sensor noise, and payload variation (0–2.5 kg). Across four demanding scenarios (sinusoidal tracking, sudden payload jumps, sustained disturbances up to 0.76 Nm, and high-speed motions), the proposed method consistently achieves 46–52 % lower RMSE and 28–30 % reduced control energy compared to the fixed-scaling industrial baseline, while preserving strict real-time constraints (inner loop cycle time 0.68–0.89 ms, SAC inference < 0.6 ms). The full PLC program (SCL/FBD), HIL environment, and trained policies will be released open-source upon acceptance (DOI to be provided during revision).The full PLC program, HIL environment, and trained SAC policies will be released open-source as a preprint supplement.

Abstract: The rapid adoption of electric vehicles has fundamentally altered noise, vibration, and harshness (NVH) requirements, as the absence of internal combustion engine noise exposes previously masked drivetrain excitations. In this context, vibroacoustic simulation has become a key enabler for achieving low-noise electric powertrains while reducing development time and physical prototyping. This review provides a comprehensive overview of multiphysics simulation methodologies applied to EV powertrains, covering the full excitation–response–radiation chain from electromagnetic motor forces and gear meshing dynamics to flexible multibody behavior, structural vibration, and acoustic radiation. The literature is systematically analyzed with respect to modeling approaches, numerical methods, and software workflows used to couple electromagnetic analysis, gear contact mechanics, multibody dynamics, finite element structural models, and acoustic FEM/BEM solvers. Particular attention is given to transmission error, time-varying mesh stiffness, and electromagnetic torque ripple as dominant tonal noise sources, as well as to the role of housing dynamics in sound radiation. The review highlights the strengths and limitations of time-domain and frequency-domain formulations, reduced-order models, and high-fidelity numerical simulations, emphasizing the trade-off between accuracy, computational cost, and practical applicability. Beyond summarizing existing methods, this paper critically discusses current limitations in predictive capability, including insufficient treatment of manufacturing variability, limited system-level validation, and the lack of standardized benchmark datasets. Emerging trends such as stochastic modeling, machine-learning-based surrogate models, and digital twin concepts are identified as promising directions to address these challenges. Overall, the review underscores that effective EV NVH prediction requires a holistic, system-level multiphysics approach in which electromagnetic, mechanical, structural, and acoustic phenomena are considered jointly rather than in isolation. From a knowledge-structuring perspective, the reviewed methodologies establish a clear conceptual mapping between classical NVH theory and electric powertrain–specific eNVH simulation. Fundamental concepts such as excitation–transfer–radiation paths, modal superposition, and frequency-order analysis remain valid, while their dominant sources shift from combustion-related mechanisms to electromagnetic forces and gear meshing phenomena. In this sense, electromagnetic excitation and transmission error can be interpreted as the primary counterparts of traditional engine orders in EV applications, propagated through flexible multibody and structural models toward acoustic radiation. This explicit linkage between established NVH principles and EV-specific excitation mechanisms provides a coherent framework that supports both human understanding and machine-learning-based knowledge extraction of multiphysics eNVH simulation workflows.

Abstract: Reverse parking maneuvering of a vehicle with trailer system is a difficult task to complete for human drivers due to the multi-body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the presence of other vehicles that the trailer and its connected vehicle must avoid during the reverse parking maneuver. While path planning methods in reverse motion for vehicles with trailers exist, there is a lack of results that also offer collision avoidance as part of the algorithm. This paper hence proposes a modified Hybrid A*-based algorithm that can accommodate the vehicle-trailer system as well as collision avoidance considerations with the other vehicles and obstacles in the parking environment. One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration. The other contribution is the addition of the collision avoidance functionality which the standard Hybrid A* algorithm lacks. The method is developed and presented first, followed by simulation case studies to demonstrate the efficacy of the proposed approach.

Abstract: In modern electric vehicles (EVs), where the absence of a combustion engine reveals new acoustic challenges, gear and gearbox noise—especially tonal “whine”—has emerged as a prominent NVH (Noise, Vibration, and Harshness) concern. This review investigates the state-of-the-art multiphysics simulation workflows capable of predicting NVH from root excitation through structural vibration and up to radiated airborne noise. Emphasis is placed on software ecosystems developed between 2015 and 2025, including Romax, AVL EXCITE, Siemens Simcenter, SMT MASTA, MSC Adams/Nastran/Actran, KISSsoft + RecurDyn, and COMSOL Multiphysics. The review explores simulation layers ranging from analytic torsional models to coupled flexible multibody dynamics (MBD), finite-element structural response, and acoustic FEM/BEM methods. Recent trends such as per-tooth microgeometry definition, flank waviness modelling, use of measured topography (e.g., CMM data), and digital twin concepts are discussed in depth. Furthermore, the review highlights validation challenges—especially the limited system-level correlation between predicted and measured noise—and identifies research gaps regarding EV-specific excitations, manufacturing variation modeling, and NVH-oriented design optimization. This work aims to give engineers and researchers a structured overview of integrated CAE methods to “front-load” gearbox NVH prediction in electrified drivetrains, thereby improving design cycles and acoustic performance.

Abstract: This study evaluates the feasibility of using two affordable thermal cameras (UNI-T UTi260M and UTi260T), which are not designed as automotive sensors, for observing pedestrians and warm objects during night-time driving under low-illumination conditions. The experimental setup includes mounting the camera on the vehicle body (e.g., side-mirror area/roof), recording road scenes in urban and rural environments, and selecting representative frames for qualitative and quantitative analysis. The study assesses: (i) observable pedestrian detectability in unlit road sections and under oncoming headlight glare, where visible cameras often lose contrast; (ii) the influence of low ambient temperature and strong cold wind on image appearance (including “whitening”/contrast shifts); and (iii) workflow differences, where UTi260M relies on a smartphone application for streaming/recording, while UTi260T supports PC-based image analysis and temperature-profile visualization. In addition, a calibration-based geometric method is proposed for approximate pedestrian distance estimation from single frames using silhouette pixel height and a regression model based on 1/h_px, valid for a specific mounting configuration and a known subject height. Results indicate that both cameras can highlight warm objects relative to the background and support visual pedestrian identification at low illumination, including in the presence of oncoming headlights, with UTi260M showing more stable behaviour in part of the tests. This work is a feasibility study and does not claim ADAS functionality; it outlines limitations, repeatability considerations, and a minimal set of metrics and procedures for future extension. All quantitative indicators derived from exported frames are explicitly treated as image level proxy metrics not as physical sensor characteristics.

Abstract: Plug-in hybrid electric vehicles (PHEVs) are critical to the EU's decarbonization strategy, yet their real-world climate benefits remain uncertain. This study presents a large-scale analysis of real-world PHEV performance using on-board monitoring data from 457,303 vehicles (2021-2023). The results reveal a profound discrepancy between official test values and actual use. The mean real-world CO₂ emissions were 138 g/km, compared to a test-cycle average of 46 g/km, resulting in a regulatory gap of approximately 300%—significantly higher than for other vehicle types. Performance varied substantially across manufacturers, with gaps ranging over 200 percentage points. Contrary to expectations, larger battery capacity was correlated with a wider performance gap. Real-world electric driving averaged only 45.5% of distance, far below regulatory assumptions. This gap has grown wider each year, indicating test-cycle optimization is outpacing real-world efficiency gains. Policy analysis shows that closing this gap could achieve major CO₂ savings, underscoring the urgent need for regulatory reform, including real-world emissions monitoring and updated test procedures, to ensure PHEVs deliver their promised environmental impact.

Abstract: Lightweight gearbox housings often raise NVH risk, yet full finite-element evaluations are too slow for early design screening. This study tests whether a few frequency-band descriptors of radiated sound are enough to classify housing stiffness. Using an open dataset of electric-vehicle gearbox spectra for three rib-configurations—flexible, intermediate and rigid—we averaged sound-pressure levels in five 1 kHz bands. Principal-component analysis separated the twelve samples into three non-overlapping groups, confirmed by k-means clustering (adjusted Rand index = 1.00). The random-forest model achieved 75 % classification accuracy on the present 12-sample data set (leave-one-out evaluation). Owing to the small sample size this figure should be regarded as explorative, and a larger validation study is required to confirm generalizability; permutation analysis confirmed the 3–4 kHz and 2–3 kHz bands as most important for classification. In contrast, total integrated spectral energy showed no significant group difference (p = 0.81). The results These findings suggest that mid-frequency band energy may encode structural-stiffness differences, although validation on larger datasets is necessary. The workflow—load spectra, compute five band means, classify—offers a rapid, interpretable tool for NVH-aware lightweight design.

Abstract: Battery Thermal Management Systems (BTMS) are critical for maintaining optimal operating temperatures (20-40°C) in lithium-ion batteries, particularly for electric vehicles (EVs) and grid-scale energy storage [1,2]. Phase Change Materials (PCMs) have emerged as a transformative solution, leveraging latent heat absorption/release during phase transitions to provide passive thermal regulation [3]. This review systematically evaluates inorganic (salt hydrates), organic (paraffins, fatty acids), and composite PCMs, analyzing their thermophysical properties, performance characteristics, and implementation challenges in BTMS applications [4,5]. Key findings reveal that advanced composite PCMs with thermal conductivity enhancers (graphene, metal foams) can achieve 3-5× improvement in heat dissipation while maintaining >90% of base latent heat capacity [6,7]. The paper concludes with actionable recommendations for next-generation PCM development and integration strategies.

Abstract: Infrared traffic object detection faces challenges such as low resolution, weak thermal 2 contrast, and inefficiency in detecting small objects. To address these issues, this paper 3 proposes RES-YOLO, an enhanced YOLOv8n-based architecture. It incorporates Receptive 4 Field Adaptive Convolution for improved multi-scale perception, Efficient Multi-scale 5 Attention for better feature representation, and the Scylla-IoU loss for more accurate 6 and faster bounding box regression. Additionally, a pseudo-color infrared dataset is 7 constructed to enrich texture and contrast information beyond conventional white-hot 8 images. Experiments on both the FLIR public dataset and a self-built dataset show RES- 9 YOLO improves accuracy by 4.9% and 5.5% over the baseline while maintaining real-time 10 performance. These results highlight the method’s effectiveness in integrating lightweight 11 deep learning and dataset enhancement for robust perception in intelligent vehicle systems, 12 supporting AI-driven autonomous driving and driver assistance applications.

Abstract: Development of AI-driven automated driving functions requires vast amounts of diverse, high-quality data to ensure road safety and reliability. However, manual collection of real-world data and creation of 3D environments is costly, time-consuming, and hard to scale. Most automatic environment generation methods still rely heavily on manual effort, and only a few are tailored for Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) training and validation. We propose an automated generative framework that learns real-world features to reconstruct realistic 3D environments from a road definition and two simple parameters for country and area type. Environment generation is structured into three modules - map-based data generation, semantic city generation, and final detailing. The overall framework is validated by training a perception network on a mixed set of real and synthetic data, validating it solely on real data, and comparing performance to assess the practical value of the environments we generated. By constructing a Pareto front over combinations of training set sizes and real-to-synthetic data ratios, we show that our synthetic data can replace up to 90% of real data without significant quality degradation. Our results demonstrate how multi-layered environment generation frameworks enable flexible and scalable data generation for perception tasks while incorporating ground-truth 3D environment data. This reduces reliance on costly field data and supports automated rapid scenario exploration for finding safety-critical edge cases.