The block diagram of the solar-powered EV charging station comprises several key components:
PV Array: Represents the solar photovoltaic array responsible for generating power.
DC-DC Converter: Converts the output from the PV array to the required voltage level.
MPPT Algorithm: An Adaptive Neuro-Fuzzy Inference System (ANFIS) MPPT algorithm optimizes power extraction from the PV array based on irradiation and temperature inputs.
Standby Storage Element (Stationary Battery): Serves as a stationary energy storage element connected via a bi-directional DC-DC converter.
EV Battery: Represents the electric vehicle battery, also connected via a bi-directional DC-DC converter.
DC Bus Voltage Controller: Maintains the DC bus voltage at a specified level.
Grid Integration: Connects the system to the grid for power exchange.
Inverter: Converts DC power to AC for grid integration.
Grid Control: Involves a neural network-based energy management system to control power flow between the grid and the system based on PV power and battery state of charge (SOC).
System Operation
The solar-powered EV charging station operates as follows:
PV power is utilized to charge the stationary storage battery and supply power to the EV battery.
In the absence of solar power, the stationary battery supplies power to the EV battery.
If both PV and battery capacities are sufficient, excess power is supplied to the grid.
Grid power can be used to charge the stationary battery and the EV battery.
MATLAB Simulation Model
PV Array and MPPT
The simulation model includes the PV array, DC-DC converter, and ANFIS MPPT algorithm. The ANFIS MPPT adapts to changing irradiation and temperature conditions to maximize power extraction. A block scope is used to measure PV voltage, current, and power.
Battery Management
Stationary and EV batteries are connected via bi-directional DC-DC converters with voltage control. PI controllers and PWM generators maintain the DC bus voltage at 500 volts. Battery parameters, including SOC, are measured and monitored.
Grid Integration
Grid integration involves a single-phase inverter connected to the grid through an LCL filter. A neural network-based grid control system generates reference currents based on PV power and SOC of the stationary battery. These reference currents control the inverter, regulating power flow between the system and the grid.
Simulation Results
Case 1: High SOC of Stationary Battery (90%) and Low SOC of EV Battery (9%)
PV power: 2000 W
Stationary battery discharging, EV battery charging
Grid supplies power to the DC bus
SOC of batteries changes accordingly
Case 2: Medium SOC of Stationary Battery (40%) and Low SOC of EV Battery (9%)
PV power varies
Stationary battery discharging, EV battery charging
Grid supplies power to the DC bus
SOC of batteries changes based on power conditions
Case 3: Low SOC of Stationary Battery (10%) and Low SOC of EV Battery (10%)
PV power fluctuates
Grid supplies power to charge both batteries
SOC of batteries increases due to charging
Conclusion
The MATLAB simulation showcases the dynamic behavior of a solar PV-powered EV charging station. It demonstrates the adaptability of the system to varying solar conditions, efficient battery management, and grid integration. The inclusion of ANFIS MPPT and neural network-based energy management enhances the system's performance and flexibility.