A research team led by Indian researchers has developed a "smart" electric vehicle (EV) charging system that integrates photovoltaic (PV) panels, proton exchange membrane (PEM) fuel cells, battery energy storage, and supercapacitors. The system's core is a Z-source boost converter using the ANFIS algorithm to achieve maximum power point tracking (MPPT).
Unlike traditional single-PV or hybrid systems, this approach combines intelligent control and multi-energy management to ensure efficient, stable, and reliable charging of smart EVs. Future research will expand to new energy DC microgrids with vehicle-to-grid (V2G) capabilities, enabling smarter energy ecosystem integration for EVs.
The research team used MATLAB/Simulink 2021a to simulate the system, which includes two 50kW fast-charging units, a 186kW peak power PV system, a lead-acid battery system, and a hydrogen-based energy storage system consisting of a 176kVA hydrogen generator, six 66kW fuel cell modules, and a 450kg hydrogen tank.
The system integrates various devices using a Z-source converter (ZSC). An impedance network connects the PV system, battery, and grid. The converter employs two sets of synchronously controlled switches, input and output diodes, and capacitors, and can operate in either continuous or discontinuous conduction mode.
The ANFIS-based MPPT method uses the PV voltage, current, and temperature as inputs and outputs the duty cycle to control a DC-DC boost Landsman converter for maximum power point tracking. Through extensive training, ANFIS optimizes fuzzy rules, reduces errors, and is suitable for real-time control.
The experiments were validated using laboratory prototypes, including a fuel cell with a 100V output voltage and 30-40A current, a DC-DC converter with a 1000-1100V output voltage and 30A current, and a battery with a 120V output voltage. The simulated and measured errors were within 0.8%-3%.
The results show: "Simulations show that the system can boost the voltage from 110V to 150V and maintain a stable output of approximately 1100V/30A, with the PV-side current stabilized at 500A. The fuel cell output voltage remains at 110V, the current drops from 40A to 25A, and the battery maintains a 60% state of charge (SOC) at 120V output. The hardware prototype, based on the DSPIC30F4011 microcontroller, achieves an MPPT efficiency of 98.7%, a voltage regulation error of ±1.5%, a power deviation of less than 2%, and grid-side voltage and current total harmonic distortion (THD) of 500V and 13A, respectively, in compliance with IEEE 519 standards."
Compared to traditional algorithms, this ANFIS MPPT significantly improves tracking efficiency and dynamic performance under fluctuating sunlight conditions. Furthermore, the hybrid system configuration exceeds expectations by maintaining grid stability and uninterrupted charging despite fluctuations in renewable energy and varying load demand.
A research team led by Indian researchers has developed a "smart" electric vehicle (EV) charging system that integrates photovoltaic (PV) panels, proton exchange membrane (PEM) fuel cells, battery energy storage, and supercapacitors. The system's core is a Z-source boost converter using the ANFIS algorithm to achieve maximum power point tracking (MPPT).
Unlike traditional single-PV or hybrid systems, this approach combines intelligent control and multi-energy management to ensure efficient, stable, and reliable charging of smart EVs. Future research will expand to new energy DC microgrids with vehicle-to-grid (V2G) capabilities, enabling smarter energy ecosystem integration for EVs.
The research team used MATLAB/Simulink 2021a to simulate the system, which includes two 50kW fast-charging units, a 186kW peak power PV system, a lead-acid battery system, and a hydrogen-based energy storage system consisting of a 176kVA hydrogen generator, six 66kW fuel cell modules, and a 450kg hydrogen tank.
The system integrates various devices using a Z-source converter (ZSC). An impedance network connects the PV system, battery, and grid. The converter employs two sets of synchronously controlled switches, input and output diodes, and capacitors, and can operate in either continuous or discontinuous conduction mode.
The ANFIS-based MPPT method uses the PV voltage, current, and temperature as inputs and outputs the duty cycle to control a DC-DC boost Landsman converter for maximum power point tracking. Through extensive training, ANFIS optimizes fuzzy rules, reduces errors, and is suitable for real-time control.
The experiments were validated using laboratory prototypes, including a fuel cell with a 100V output voltage and 30-40A current, a DC-DC converter with a 1000-1100V output voltage and 30A current, and a battery with a 120V output voltage. The simulated and measured errors were within 0.8%-3%.
The results show: "Simulations show that the system can boost the voltage from 110V to 150V and maintain a stable output of approximately 1100V/30A, with the PV-side current stabilized at 500A. The fuel cell output voltage remains at 110V, the current drops from 40A to 25A, and the battery maintains a 60% state of charge (SOC) at 120V output. The hardware prototype, based on the DSPIC30F4011 microcontroller, achieves an MPPT efficiency of 98.7%, a voltage regulation error of ±1.5%, a power deviation of less than 2%, and grid-side voltage and current total harmonic distortion (THD) of 500V and 13A, respectively, in compliance with IEEE 519 standards."
Compared to traditional algorithms, this ANFIS MPPT significantly improves tracking efficiency and dynamic performance under fluctuating sunlight conditions. Furthermore, the hybrid system configuration exceeds expectations by maintaining grid stability and uninterrupted charging despite fluctuations in renewable energy and varying load demand.
