Voltage Balancing Circuit Simulation Model for Supercapacitors in Photovoltaic Microgrids

In photovoltaic microgrid systems, supercapacitor banks are typically composed of multiple individual capacitors connected in series or parallel. Due to variations in manufacturing processes and operating conditions, voltage imbalances may occur among individual capacitors, leading to over-voltage or under-voltage situations that compromise system performance and lifespan. Voltage balancing circuits address this issue by actively or passively equalizing capacitor voltages to ensure stable system operation. Simulation models for balancing circuits are commonly built using power electronics simulation tools like Matlab/Simulink, PLECS, or PSIM. Key components include: Supercapacitor Equivalent Model: Implemented using RC series models or more complex dynamic models to accurately represent charging/discharging characteristics. In code implementations, this typically involves creating state-space equations or transfer functions to simulate voltage-current relationships. Voltage Monitoring Module: Continuously tracks individual capacitor voltages as input signals for balancing control. Programming-wise, this requires ADC (Analog-to-Digital Converter) simulations and sensor calibration algorithms to ensure measurement accuracy. Control Strategies: Common approaches include switching resistor methods (passive balancing), DC-DC converter techniques (active balancing), or SOC (State of Charge)-based intelligent algorithms. Code implementation often involves PID controllers, hysteresis control loops, or fuzzy logic algorithms for dynamic response optimization. Power Device Simulation: Models switching components like MOSFETs and IGBTs, accounting for conduction losses and switching losses that impact balancing efficiency. This requires incorporating device datasheet parameters and thermal models into the simulation. During simulation, key performance metrics include balancing speed, energy loss, and dynamic response characteristics. After parameter optimization through iterative testing, validated models can be extended to practical hardware designs such as Buck-Boost circuit topologies or flying capacitor configurations.