Optimized Nonlinear Control for Efficient Power Management in Electric Vehicle Charging Using a Four-Switch Converter

Abstract

As renewable energy systems become increasingly integrated with electric vehicle (EV) infrastructure, advanced power management strategies are required to ensure system stability and efficiency. This paper presents a photovoltaic (PV)-battery-EV energy management system based on a four-switch buck-boost converter. A novel condition-based integral terminal super-twisting sliding mode controller (CBITSTSMC) is proposed to mitigate windup effects, improve transient response, and enhance robustness under varying operating conditions. An artificial neural network (ANN)-based maximum power point tracking (MPPT) scheme is employed to maximize PV energy harvesting under dynamic irradiance conditions, while controller parameters are optimized using an Improved Gray Wolf Optimization (IGWO) algorithm. The effectiveness of the proposed approach is validated through Controller-in-the-Loop (CIL) implementation using a Delfino microcontroller platform. Simulation and CIL results demonstrate superior performance compared with conventional sliding mode control (SMC) and super-twisting sliding mode control (STSMC). The proposed CBITSTSMC achieves RMSE values of $5.46\times {10}^{-4}$, $0.0054$, and $0.0094$ for the PV, battery, and EV subsystems, respectively, while reducing tracking errors by up to 95%. Furthermore, the controller exhibits fast convergence, minimal overshoot, and improved robustness, demonstrating its suitability for efficient energy management in integrated PV–battery–EV systems.