Simplified reinforcement learning for energy management of extended-range electric vehicles based on STM32

Abstract

To address the poor adaptability of traditional rule-based control, the operational instability of basic Q-learning algorithms, and the critical difficulty of deploying complex reinforcement learning models on resource-constrained on-board embedded platforms, this paper proposes a lightweight, simplified Q-learning energy management strategy for extended-range electric vehicles (REEVs), successfully implemented on an STM32 microcontroller. The algorithm achieves significant computational reduction by simplifying the traditional 5×5 state-action space into a highly condensed 2×2 grid. Furthermore, a power cooling mechanism is introduced, a multi-dimensional reward function is reconstructed to balance competing vehicle demands, and an ε-decay exploration strategy is designed. Software-in-the-loop (SIL) simulation verification demonstrates that the proposed strategy tightly controls the state-of-charge (SOC) standard deviation within 0.09. Additionally, high-frequency power fluctuations and range extender start-stop times are drastically reduced, and overall energy efficiency is improved by 50.3% compared with traditional strategies. The optimized algorithm occupies only 72.3% of RAM and 68.7% of Flash memory, fully satisfying strict on-board embedded system constraints and providing a highly feasible solution for intelligent REEV energy management.