Optimized ANFIS-Based Robust Nonlinear Control of a Solar Off-Grid Charging Station for Electric Vehicles

This paper proposes a method to improve the performance of an off-grid electric vehicle charging station powered by photovoltaic (PV) panels and batteries. To ensure optimal performance of the PV panels, maximum power point tracking (MPPT) is implemented using an adaptive neuro-fuzzy inference system. A robust control technique is designed for regulating the currents and voltages within the system. Conventional sliding mode controllers (SMCs) are prone to chattering, and while the inclusion of a super-twisting component mitigates this issue, it introduces windup problems. To address these challenges, a conditioned-based super-twisting SMC (CST-SMC) is proposed, which effectively resolves the windup issue. The controller gains are optimized using the grey wolf optimization algorithm, and global stability is demonstrated through Lyapunov stability analysis. The proposed system is simulated in MATLAB.

Keywords: MPPT, ANFIS, CST-SMC, QBBC, GWO