Dynamic-Adaptive Eco-driving Strategy for DDEVs via State-Aware Deep Reinforcement Learning

This paper proposes a deep reinforcement learning (DRL)-based ecodriving strategy for distributed drive electric vehicles (DDEVs) to enhance energy efficiency and dynamic adaptability in complex traffic scenarios. Conventional DRL approaches often employ fixed exploration strategies that are insensitive to dynamic environmental states, leading to suboptimal and unstable performance when handling the unique sensitivity of DDEVs to stochastic disturbances. To address this limitation, the proposed method adaptively adjusting exploration noise based on environmental states by using state-dependent exploration (SDE). A high-fidelity 7-degree-of-freedom vehicle dynamics model and a traffic simulation environment are employed to validate the method. Results demonstrate that SDE-SAC achieves superior performance compared to baseline methods, with a 0.56 kWh power consumption, and a 27.2 m/s mean velocity, alongside improved motion stability. Furthermore, the method exhibits strong generalization in high-density traffic scenarios, maintaining energy efficiency and safety. This work advances the development of adaptive energy management systems for DDEVs by bridging the gap between environmental uncertainty and vehicular control.