Implementation of Solar PV Based EV Charging Station with Fuzzy MPPT Algorithm
- lms editor
- Nov 15, 2025
- 4 min read
🔋 1. System Overview: Components and Connections
The proposed solar PV–battery-based EV charging system integrates renewable energy generation, energy storage, and intelligent power management through a common DC bus. The major components include:
Solar PV Array: Converts solar radiation into electrical energy.
Battery Storage: Stores excess solar energy for later use.
Bidirectional DC–DC Converter: Manages charging and discharging of the battery.
DC Bus: Acts as a common link for energy transfer between the PV, battery, and grid.
This configuration allows seamless energy exchange — the PV supplies power to the DC bus, while the bidirectional converter ensures efficient charging or discharging of the battery depending on the power balance.
☀️ 2. MPPT Algorithm: Maximizing Solar Power Extraction
At the core of the PV subsystem lies the Maximum Power Point Tracking (MPPT) algorithm, implemented to ensure optimal solar energy extraction.
The algorithm continuously measures PV voltage and current, computes the corresponding power, and monitors the slope of the power–voltage (P–V) curve.
Based on the direction of the slope (positive or negative), the duty cycle of the boost converter is adjusted dynamically.
This ensures that the PV array always operates near its maximum power point (MPP), regardless of irradiance or temperature variations.
This real-time tracking significantly enhances energy conversion efficiency and system performance.
🤖 3. Fuzzy Logic Control for Power Optimization
To improve the responsiveness of the MPPT controller, a fuzzy logic controller (FLC) is integrated into the system.
Inputs: Error (difference between current and previous power levels) and change in error.
Outputs: Correction to the converter’s duty cycle.
The fuzzy rule base uses membership functions (e.g., Negative Big, Zero, Positive Big) to map nonlinear relationships between input variations and output control actions.
By doing so, the Fuzzy MPPT adapts quickly to sudden irradiance changes, ensuring smoother power tracking and faster convergence to the optimal operating point compared to conventional MPPT methods.
⚙️ 4. Battery Charging Control: Maintaining Steady Power Flow
The battery charging unit is managed through a DC–DC converter that maintains the charging voltage at 400 V.
When solar generation exceeds demand, the PV supplies the charging power directly to the battery.
When solar output falls short, the grid compensates for the deficit, ensuring uninterrupted charging.
The control logic maintains steady-state charging at the target voltage while minimizing transient fluctuations.
This ensures that the battery remains in a healthy state of charge (SOC) while maintaining efficient utilization of available solar energy.
🔄 5. Inverter Control: Managing Grid Power Flow
The inverter subsystem acts as an interface between the DC microgrid and the AC utility grid.
The inverter current is regulated based on PV power and battery output.
When PV generation is sufficient (e.g., 2,000 W), power flows from the DC bus to the EV battery.
During low irradiance, the inverter intelligently draws supplemental power from the grid, maintaining a constant EV charging rate.
This adaptive inverter control ensures stable grid interaction, bidirectional energy flow, and minimal distortion in power exchange.
⚡ 6. Grid Power Management: Ensuring Reliable Charging
The grid functions as a backup energy source for reliable EV charging.
Under reduced solar irradiance (e.g., cloudy or evening conditions), the grid compensates for the shortfall in PV output.
The total charging power (around 2,000 W) remains stable, with the grid automatically adjusting its contribution based on PV availability.
This intelligent coordination between the PV system and the grid prevents power interruptions and guarantees uninterrupted charging service.
📈 7. Reference Current Generation for Battery Charging
To ensure precise control of charging, the system generates a reference current (I_ref).
This current serves as a command signal for the inverter to regulate the power flow into the battery.
Phase modulation techniques synchronize inverter operation with the grid, enabling smooth current transitions and power factor correction.
This approach ensures that battery charging is both efficient and harmonically clean, contributing to improved overall system stability.
🔍 8. Simulation and Power Management in Action
MATLAB/Simulink simulation validates the system’s ability to manage power dynamically under varying solar irradiance.
DC Bus Voltage: Maintained constantly at 400 V.
PV Power: Fluctuates with sunlight intensity (1,000–2,000 W).
Battery Power: Adjusts dynamically to maintain stable EV charging (1,900–2,000 W).
Grid Power: Supplements energy when PV generation decreases.
When irradiance drops, the grid power contribution increases, while the battery continues to charge at a constant rate — showcasing robust energy coordination and control performance.
🔋 9. Power Sharing Between PV, Grid, and Battery
The power-sharing mechanism dynamically balances the energy flow:
During high sunlight, PV supplies most of the load, and the battery charges.
When sunlight weakens, PV output falls (e.g., from 2,000 W to 1,000 W).
The grid seamlessly supplies the remaining 1,000 W, ensuring consistent battery charging.
This adaptive power-sharing guarantees continuous EV charging without affecting grid stability or overloading system components.
🏁 10. Conclusion: Efficient and Flexible EV Charging Solution
The MATLAB-based simulation of a fuzzy MPPT-controlled grid-connected solar PV–battery system demonstrates a reliable and intelligent EV charging strategy.
Ensures optimal solar energy extraction through fuzzy logic-enhanced MPPT.
Maintains steady battery charging using a bidirectional converter and grid support.
Offers seamless power flow management between PV, grid, and battery.
This integrated design not only improves system reliability but also promotes sustainable electric mobility by maximizing renewable energy utilization and minimizing grid dependency.
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