Artificial Intelligence-Based Optimized Nonlinear Control for Multi-Source Direct Current Converters in Hybrid Electric Vehicle Energy Systems

The integration of multiple renewable and storage units in electric vehicle (EV) hybrid energy systems presents significant challenges in stability, dynamic response, and disturbance rejection, limitations often encountered with conventional sliding mode control (SMC) and super-twisting SMC (STSMC) schemes. This paper proposes a condition-based integral terminal super-twisting sliding mode control (CBITSTSMC) strategy, with gains optimally tuned using an improved gray wolf optimization (I-GWO) algorithm, for coordinated control of a multi-source DC–DC converter system comprising photovoltaic (PV) arrays, fuel cells (FCs), lithium-ion batteries, and supercapacitors. The CBITSTSMC ensures finite-time convergence, reduces chattering, and dynamically adapts to operating conditions, thereby achieving superior performance. Compared to SMC and STSMC, the proposed controller delivers substantial reductions in steady-state error, overshoot, and undershoot, while improving rise time and settling time by up to 50%. Transient stability and disturbance rejection are significantly enhanced across all subsystems. Controller-in-the-loop (CIL) validation on a Delfino C2000 platform confirms the real-time feasibility and robustness of the approach. These results establish the CBITSTSMC as a highly effective solution for next-generation EV hybrid energy management systems, enabling precise power-sharing, improved stability, and enhanced renewable energy utilization.