Smart Energy Management in Solar Systems Using Fuzzy Logic and Advanced Sliding Mode Control

This study proposes a fuel cell and electrolyzer-based energy storage system as an alternative to conventional battery storage for solar energy applications. The system integrates a proton-exchange membrane electrolyzer, high-pressure gas storage, and a fuel cell, with the photovoltaic array operating at its maximum power point using an artificial neural network-generated reference voltage. A fuzzy logic-based energy management system ensures efficient power distribution under varying load conditions. To address chattering and switching gain overestimation in conventional sliding mode controllers, a double integral-based super twisting sliding mode controller (ST-SMC) has been proposed. Lyapunov stability analysis ensures stability, while controller gains have been optimized using the grey wolf algorithm. Other controllers, such as sliding mode control, integral sliding mode controller, and double integral sliding mode controller, have also been proposed for performance comparison. The proposed double integral-based ST-SMC controller demonstrates superior performance compared to other nonlinear controllers. It achieves a rise time of 0.0169 seconds, an overshoot of 0.2840\%, and a settling time of 0.0228 seconds. These results show significant improvements over conventional sliding mode control (SMC), Integral sliding mode control (ISMC), and double integral sliding mode control (DISMC), particularly in terms of reduced overshoot and settling time, ensuring enhanced dynamic performance and system stability. These findings demonstrate enhanced dynamic performance under different operating conditions, validated through MATLAB/Simulink and hardware-in-loop testing.