The intermittent nature of solar energy results in a generation–load mismatch, posing a significant challenge to reliable power utilization. Battery Energy Storage Systems (BESS) play a crucial role in mitigating this issue. However, effective operation requires advanced control strategies. Conventional techniques, such as classical Maximum Power Point Tracking based on Constant Current/Constant Voltage, often struggle to cope with the nonlinear dynamics of PV–BESS systems, leading to reduced efficiency and accelerated battery degradation. This paper proposes a hybrid adaptive control strategy integrating fuzzy logic decision-making, Genetic Algorithm (GA) optimization, and Extended Kalman Filter (EKF)-based State of Charge (SoC) estimation. A comprehensive PV–BESS model is developed in the MATLAB/Simulink environment using real solar irradiance and realistic load profiles. Simulation results demonstrate an absolute improvement in energy efficiency of approximately 14.3%, a SoC estimation accuracy within ±5%, and an extension of battery lifetime by 18–25% compared to conventional control methods. The proposed approach offers a robust and computationally efficient solution for PV–BESS operation, making it suitable for future microgrid and renewable energy storage applications.
Keywords: Photovoltaic system, Battery Energy Storage System (BESS), Fuzzy logic control, Genetic Algorithm optimization, Extended Kalman Filter (EKF)
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