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Implementation of MPPT-Based Solar Charger Controller

Implementation of MPPT-Based Solar Charger Controller

Overview of the Solar Charger Controller

The MPPT solar charger controller is designed to maximize the power extracted from solar photovoltaic (PV) arrays. It comprises several essential elements, including:

  • Solar PV Array: Captures solar energy.

  • DC Converter: Converts and regulates the voltage for charging the battery.

  • MPPT Control System: Adjusts operating conditions to optimize power extraction.

  • Battery Charging Control: Manages the charging process based on battery conditions.

By utilizing these components, the system can charge a battery efficiently based on the state of charge (SoC) and voltage levels.

Charging Conditions

The solar charger controller operates under two primary conditions:

  1. State of Charge (SoC): Determines the current level of battery charge.

  2. Voltage Levels: Maintains the battery voltage between the float cell voltage (FCV) and the cut-off voltage (Co).

The controller dynamically adjusts the duty cycle of the converter to keep the battery voltage within these parameters, ensuring effective charging.

Implementation in MATLAB

To implement this solar charger controller in MATLAB, the setup includes:

  • PV Array Configuration: The example uses a configuration of two parallel strings, each with four series connections. This setup helps optimize power output and manage the voltage effectively.

  • Performance Metrics: The PV array is designed for a maximum power output of 250W, with specific voltage and current ratings that are crucial for calculating efficiency.

Control Algorithm Details

The heart of the MPPT controller lies in its control algorithm. Here’s a simplified breakdown of how it operates:

  • Power and Voltage Measurement: The system continuously measures the voltage and current from the PV array to calculate power.

  • Change Detection: It monitors changes in power and voltage over time, allowing the algorithm to adjust the duty cycle accordingly.

  • Duty Cycle Adjustment: Depending on the measured conditions, the algorithm can increase or decrease the duty cycle to optimize charging efficiency.

This feedback mechanism ensures that the system adapts to varying solar conditions and maintains optimal charging performance.

Simulation Results

Upon simulating the system under specific conditions—such as solar radiation and temperature—the output reveals significant insights:

  • Charging Efficiency: The charging efficiency is observed to be around 97-98% under optimal conditions.

  • Adaptability: Changes in solar irradiation levels affect the charging current, but the system continues to charge effectively, demonstrating resilience to environmental fluctuations.

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