Modeling and simulation of solar battery charge controller using adaptive particle swarm optimization MPPT algorithm

Implementing an effective Maximum Power Point Tracking method is crucial for optimizing solar energy harvesting against environmental fluctuations like solar radiation and temperature. This paper introduces a novel approach for modeling and simulating a solar battery charge controller, using a modified Particle Swarm Optimization algorithm. The power stage of the system is based on a SEPIC converter, which is employed to manage the power conversion and improve the energy transfer to the battery. The developed circuit model is evaluated under various radiation levels at a constant temperature, as well as under different temperature levels at a constant radiation. Simulations are performed using MATLAB/Simulink. In traditional Maximum Power Point Tracking methods using Particle Swarm Optimization, tracking performance significantly decreases under rapid environmental variations. Their fixed particle search strategy leads to suboptimal performance, as the system fails to adapt quickly to sudden changes. The proposed reset-triggered approach addresses these limitations by reinitializing particle positions and velocities. This enables continuous and efficient power tracking throughout the charging process. The simulation results demonstrate that the proposed circuit model adapts to dynamic environmental variations with an average efficiency of approximately 96.7\%. The presented solar battery charge controller circuit is designed for Level 1 electric vehicle charging stations and it can provide potential solutions for the relevant applications.