Deep Reinforcement Learning-Driven Robust Adaptive Control for Hybrid Energy Storage in SynRM-Based Light Electric Vehicle Drives

Abstract

To advance the efficiency and dynamic responsiveness of Light Electric Vehicles(LEVs), this study introduces a comprehensive intelligent control and energy-management framework that couples a superconducting magnetic energy storage (SMES) assisted hybrid energy storage system (HESS) with a synchronous reluctance motor (SynRM) drive. The proposed configuration exploits the high power density and instantaneous response of SMES to enhance performance during transient load changes. To mitigate nonlinear behaviors, parameter deviations, and external disturbances within both HESS and SynRM subsystems, two resilient nonlinear controllers are designed: an Adaptive Barrier Function based Super-Twisting Sliding Mode Controller (ABC-STSMC) and a Synergetic Controller. The adaptive gains of the ABC-STSMC are tuned automatically through the Whale Optimization Algorithm (WOA) to ensure optimal robustness. Moreover, a deep reinforcement learning–driven energy management system (RL-EMS) governs real-time power allocation among the SMES, supercapacitor, and fuel cell, thereby maximizing overall efficiency and component longevity across variable driving conditions. The framework is validated through MATLAB/Simulink simulations and Hardware-in-the-Loop (HIL) experimentation. Quantitative comparisons with conventional Sliding Mode Control (SMC) and Integral Sliding Mode Control (ISMC) demonstrate that the WOA-optimized ABC-STSMC improves transient response by 28%, enhances tracking accuracy by 35%, and strengthens disturbance rejection. Simultaneously, the RL-EMS lowers switching losses and ensures balanced energy utilization. Collectively, the integrated control and management scheme offers higher reliability, robustness, and adaptability, positioning it as a promising solution for future high-performance electric mobility systems.