1. Introduction

2. Materials and Methods

2.3. Design of Controllers

The nanogrid controller coordinates various power sources to achieve optimal power flow. The Energy Management System (EMS) proposed in [30] maintains this coordination and dispatches the required power levels demanded by the load. Various control strategies can be implemented, broadly classified into centralized, decentralized, and hybrid control topologies [39].

In the centralized control method, a central controller manages the power, voltage, and load of the nanogrid using data from various sources and loads, communicated through a network, with all units in the nanogrid directly interfacing with the central controller. This central controller makes decisions based on information collected from sensors attached to all sources and loads via communication channels. While this strategy is fast and precise, it has a significant drawback: a single communication link failure can lead to total grid failure [40]. In contrast, the decentralized control scheme has each unit in the nanogrid collect local information from sources or loads through individual sensors. This approach is more robust and reliable as it avoids costly communication links [41]. Coordination between units, fault management, and overall system data monitoring, however, become more challenging [42]. Hybrid distributed control combines elements of both distributed and decentralized control. In the hybrid topology, converters in the nanogrid communicate with each other to create a cohesive control strategy and share information with the central master controller. Local control decisions are made by local controllers, while the central master controller oversees the entire system and makes broader decisions. This approach enhances system reliability by eliminating dependence on a dedicated communication link all the time and makes the system more resistant to failures.

Figure 2. Hybrid control model of modular DC nano grid.

Figure 2. Hybrid control model of modular DC nano grid.

In this modular open-source DC nanogrid system, a hybrid control technique is used to implement the EMS. Converters manage source and load level control, while a dedicated central controller coordinates each unit shown in Figure 1. Communication between the units and the central controller occurs through two methods. Critical information is shared via the digital pins of the Arduino microcontroller, with specific pins dedicated to the PV controller and the battery converter to indicate their modes, and another for the master controller to manage all units. Additionally, the master controller connects to all converters using an I₂C path, allowing the central controller to collect local information cyclically and to interrupt, control, or send commands to each converter as needed [43]. This hybrid approach delivers a powerful and fast control system with enhanced resilience to failure.

2.3.1. PV Controller Design

Based on the battery charging status and state of charge (SOC), the PV controller operates in two different modes. When the battery is in a constant current charging state and connected to the system, the PV controller operates in MPPT mode. In this mode, the controller maximizes the input power from the PV using an MPPT algorithm implemented through PWM control, while the battery regulates the bus voltage. When the battery is nearly charged and in a constant voltage charging state, where the charging current depends only on the battery SOC, or when the battery is fully discharged and the PV is connected to the system, the PV switches to bus control mode. In this mode, the converter maintains the bus voltage at a defined 48V using a PI controller. The schematic diagram of this PV controller is given in Figure 3.

Generally, the cost of commercial MPPT devices is significantly higher than solar charge controllers, making them less accessible and affordable for users [44]. Previous work has shown a successful strategy for accelerating innovation and lowering hardware costs is to use an open source design [45,46]. Previous studies have argued open source design should be applied to PV systems and components to reduce costs [47]. Following that theory, to help make small solar PV systems more economically accessible and easier inclusion in the DC bus an efficient MPPT PV converter is designed, in this study a new open source MPPT is designed to reduce the price gap between MPPT and solar charge controllers. There has been some open-source hardware development in the electronics of PV systems [48,49], which began with the design of smart monitoring system for different PV applications and majority of these system used Arduino microcontrollers for conditioning and processing the output signals of the sensors and it was flexible, modular, and scalable, and of low cost [50]. A prototype of an MPPT using the microcontroller PIC16F877A was designed for 10W power, which is very low power and not suitable for most applications or commercial use [51].

Different tracking methods were introduced in the MPPT hardware such as particle swarm optimization (PSO) technique [52] and incremental conductance algorithm [53]. These MPPT methods can be categorized into four groups: conventional, intelligent, optimization, and hybrid techniques [54]. Among the conventional MPPT methods, the variable step size incremental conductance MPPT algorithm automatically adjusts the step size to improve MPPT speed and accuracy. The step size is determined based on the derivative of power to voltage of the PV array [55]. Conventional methods are less effective under partial shading and have slower response times, whereas intelligent, optimization, and hybrid techniques can handle partial shading [56,57]. The optimization methods like PSO [58], Firefly Algorithm (FA), and Ant Colony Optimization (ACO) are best for extracting power from PV under partial shading conditions as they are more accurate, but require expensive hardware [59,60]. Among all these methods, the perturb and observe (P&O) method is the most popular, and widely used, while artificial intelligence-based methods are fast and stable but require digital applications and multiple sensors [61]. Thus, in this design a modified P&O method is used due to less complexity and comparatively faster tracking capability. Many modified versions of P&O have been developed, which use converter duty cycle as the perturbed signal or adaptive perturbation values [62]. The fundamental concept of the P&O algorithm is to adjust the perturbation amplitude according to the present operational conditions. In the case of the PV controller in DC nano grid it takes the PV voltage and current as an input and generates the optimal duty cycle for the converter.

2.3.2. Battery Controller Design for Bidirectional Converter

The battery controller determines the charging and discharging modes based on the bus voltage and the SOC of the battery. Any surplus energy in the grid increases the bus voltage, indicating a charging state, and vice versa. The SOC or battery voltage indicates the charging state. Depending on this, the battery can be charged in constant current mode or constant voltage mode, as shown in Figure 4.

2.3.3. Load Controller Design

The load controller regulates the output voltage to a specific level using a PI controller. The controller compares the reference voltage with the actual output voltage and adjusts the duty cycle accordingly shown in Figure 5. Additionally, the load controller can disconnect any load upon receiving instructions from the master controller, based on the priority of the specific load.

2.4. Converter Design

Three different converter topologies are used in this research to design the modular DC nano grid system with a bus voltage of 48V. The boost converter topology is utilized as the PV converter, with the maximum PV input voltage kept below 48V, preferably between 15-40V. The maximum charging current capacity is chosen to be 16.66A, allowing for the connection of up to 800W of PV panels to the system using a single boost converter module. Multiple such modules can be used in a single system to scale up the PV power.

For charging and discharging the battery, a bidirectional buck-boost converter topology is employed, with the battery voltage considered to be 24V. Given that the lower voltage side is the battery side and allowing a maximum current of 16.67A, up to 400W of power can be charged or discharged using a single battery module in the system.

Lastly, a buck converter topology is used for loads of 24V, 12V, and 6V, with each rated for a maximum of 400W, 200W, and 100W, respectively, within the system. Ensuring that the bus voltage is stable at 48V allows for loads rated at 48V to be connected directly to the bus without any intermediary converters. The specifications of these modules, along with the PV, battery, and load ratings, are included in Table 2.

The components of the converter and the controller parameters are calculated and listed in Table 3. Components are chosen to exceed their critical ratings and are matched to the nearest common inductor and capacitor values. Based on these parameters, an open-source hardware nanogrid is designed and licensed under GNU GPL v3, CERN OHL v2, and OSF. All code, GUI, 3D-printed parts, and converter PCBs are uploaded to an open-source repository [63].

2.4.1. Bidirectional Battery Converter Design

The bidirectional buck-boost converter is designed using two alternately switched MOSFETs and one reverse parallel diode to reduce stress on the lower-side MOSFET when it is switched off. The duty ratio of the switching pulse is controlled to regulate both the current and the direction of current flow. With the battery connected to the source terminal of the high-side MOSFET, an IR 2104 MOSFET driver with a bootstrap capacitor keeps the gate voltage above the battery voltage during switching. The Arduino Nano manages the switching pulse and controls the charging and discharging current by monitoring four parameters: battery voltage, charging current, bus voltage, and discharge current, all through a 16-bit ADS1115 analog-to-digital converter. The ADC can have four unique addresses, which allows multiple battery converters to be connected and share data using the same I₂C bus.

The Arduino code limits the discharge current to the maximum allowable battery discharge rate. During charging, two modes are utilized for the lithium battery until the state of charge (SOC) reaches 95%. Initially, the controller charges the battery at the current supplied by the MPPT, provided it is below the maximum charging current of the battery. Once the SOC is 95%, the charging mode switches to constant voltage mode, charging the battery at a constant voltage with lower current. At this point, the digital pin dedicated to the battery controller (BS) is set high, signaling the MPPT to switch from MPPT mode to bus control mode. The schematic and PCB of the bidirectional converter is shown in Figure 6. The assembled battery converter with components on the board are shown in Figure 7.

2.4.2. PV Boost Converter Design

The boost converter for the PV system is rated at 800W, with an input voltage range of 15-40V, and it boosts the voltage to 48V. The converter is designed with one low-side MOSFET and a diode to allow current flow from the PV to the bus. The pulse generated by the Arduino Nano is sufficient to drive the MOSFET. The system measures PV voltage, PV current, bus voltage, and current supplied to the bus using an ADS1115 16-bit ADC. The ADS1115 has four unique addresses, allowing multiple PV and battery converters to connect, each with a unique ADC address, as they all share the same I₂C bus. Jumper resistor connections (R13, R15, R14, and R12) provide the four unique addresses of 0x48, 0x49, 0x4A, and 0x4B shown in Figure 8. The PV converter can be excluded from the I₂C bus to prevent frequent interruptions by the master controller for data acquisition as they are jumper resistor to isolate from the I₂C bus. Instead, an OLED display can be connected to the PV converter to show local vital information, while overall critical system status is shared through the digital pin.

The PV converter operates in two different modes based on the BS signal provided by the battery. When the battery is in constant current (CC) mode, the PV operates in MPPT mode. The rest of the time, it operates in bus control mode. The components and the assembled boost converter are shown in Figure 9.

2.4.3. Buck Converter Design

To serve loads of different voltage levels, a DC-DC converter was designed using an open-source Arduino Nano and a PI controller to regulate the output voltage according to a predefined reference. This converter is a synchronous buck converter utilizing CSD19533KCS MOSFETs (100V,100A) [64] as switches. The IR2104 MOSFET driver IC provides complementary pulse width modulation (PWM) pulses to both the low-side and high-side MOSFETs, with a 10μF bootstrap capacitor included in the driver circuit.

Three different voltage level load converters can be implemented using the same PCB, requiring only the specific inductor for each converter level as previously calculated. Additionally, the master controller can stop the buck converter from supplying the load either by sending a signal through I₂C or by raising the INT pin high. The Buck converter schematic and 3D rendering are shown in Figure 10 and the PCBs are shown in Figure 11.

2.4.4. Master Controller Design

In the nanogrid system, all converters and the master controller share the same I₂C bus. The PV and battery converters both use ADS1115 ADCs, communicating over this shared bus. For the PV converter, which continuously monitors current and voltage while adjusting the switch pulse, it can be isolated from this I₂C communication without interrupting its operation and display its status on an OLED. Similarly, the battery converter can be isolated with an OLED display connected to it. However, in this project, the battery converter, load converter, and master controller share the I₂C bus, with the PV converter displaying its status on its own OLED.

The shared digital pins are always connected to each converter to change their mode of operation. The master controller simultaneously requests voltage, current, and battery SOC data from the battery and load converters. The corresponding converters send back the data, which the master controller then combines sequentially and serially prints to the Raspberry Pi-based GUI and data logger. The circuit and the PCB design of the master controller is shown in Figure 12 and Figure 13.

2.4.5. Communication Bus

The communication bus comprises 8 lines used for I₂C communication, shared digital pins, and power supply (+12V, GND) shown in Figure 14. It features an 8-pin socket to interface with the pin headers of the converters and master controller, facilitating information exchange among the converters and the master controller. The bus is 25mm in height and is placed inside the DIN rail where the converters communication pins clip in. Pin sockets are spaced 35mm apart, and two communication buses can be interconnected to cover the full length of the DIN rail shown in Figure 15.

Table 4. Function of each traced in the communication bus.

Table 4. Function of each traced in the communication bus.

Serial	Trace name	Function
1	PVS (D3)	PV signal from PV converter to indicate operating mode (0=MPPT, 1=Bus control)
2	BS (D4)	Battery status signal from Battery converter (0=Current control, 1=Voltage control/isolated)
3	INT (D5)	Interrupt signal by master (Normally 0)
4	SDA	Serial data line
5	SCL	Serial clock line
6	+12V	+12V supply for the control circuit
7	+12V	+12V supply for the control circuit
8	GND	Common ground

2.4.6. Inductor Design

For designing the inductors of various converters, each handling a peak current of approximately 20-24A, the toroidal inductor core 0077443A7 from Magnetics (Pittsburgh, PA, USA) is used. This core has an outer diameter of 46.74 mm and a width of 18.03 mm, with a relative magnetic permeability of 75 and an inductance factor of 169 nH per turn. The design parameters for the 47 μH, 120 μH, and 68 μH inductors are summarized in Table 5.

3. Results

4. Discussion

5. Conclusions

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