Integrated Deep Reinforcement Learning Framework for Adaptive PI Control and Multi-Objective Energy Management in Electric Vehicle Powertrains

Abstract

Electric vehicle (EV) powertrains involve complex interactions between speed regulation, energy consumption, regenerative braking, and battery thermal behavior. Most existing approaches address controller tuning and energy management separately, which may limit the overall system performance. This paper proposes an integrated deep reinforcement learning (DRL) strategy in which a single Twin Delayed Deep Deterministic Policy Gradient (TD3) agent simultaneously adjusts the proportional and integral gains of the speed controller (Kpv, Kiv), the torque modulation coefficient (Ks), and the regenerative braking factor (βreg). A multi-objective reward formulation is adopted to account for speed tracking performance, energy efficiency, regenerative energy recovery, battery thermal constraints, and driving comfort. The framework is implemented through a MATLAB R2022b/Simulink–Python 3.10 co-simulation environment that enables online interaction between the EV model and the learning agent. Performance is evaluated using the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). Compared with a conventional fixed-gain PI controller, the approach reduces gross energy consumption by 16.2%, decreases speed tracking error by 43.7%, increases regenerative energy recovery by 21.4%, limits battery temperature rise by 30.4%, and lowers RMS jerk by 33.7%. The results indicate that jointly optimizing control and energy management variables can improve both vehicle dynamic performance and energy utilization. The methodology offers a practical framework for the development of adaptive and intelligent control systems in future electric vehicles.