Zhai Xuewen, Liu Hanwu, Sun Wencai, Su Zihang
Transportation College of Jilin University, Changchun, 130022, Jilin, China.
Sci Rep. 2025 Apr 14;15(1):12758. doi: 10.1038/s41598-025-97955-8.
Reliance solely on vehicle-specific information, while neglecting multi-source information such as traffic flow and traffic light status, results in difficulties in optimizing energy allocation based on complex road conditions. To achieve the application of multi-source traffic information and enhance the timeliness in multi-objective optimization (MOO) for connected automated range-extended electric vehicles (CAR-EEV), this paper proposes an intelligent energy management strategy (EMS) from a multi-objective perspective. Firstly, a joint simulation platform for traffic scenarios is established based on SUMO and MATLAB, and a data-driven model of CAR-EEV is constructed using collected data, serving as the data foundation and operational platform for subsequent research and development of EMSs. Then, leveraging an image-like representation of traffic flow information based on grid grayscale maps, multi-source traffic information is materialized into a two-dimensional matrix. The Euclidean distance between consecutive traffic scenario matrices is used as a basis for similarity to optimize speed and predict future vehicle speeds. Moreover, a multi-objective intelligent EMS based on deep reinforcement learning (DRL) is employed, utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm to comprehensively consider vehicle dynamics, energy consumption economy, and the degradation of batteries. This establishes an end-to-end intelligent EMS framework for CAR-EEV and accelerates training convergence through prioritized experience replay. Finally, simulations and bench tests demonstrate that this intelligent EMS significantly improves vehicle dynamics and battery life, with notably enhancing real-time performance and effectiveness.
单纯依赖特定车辆信息，而忽略交通流量和交通信号灯状态等多源信息，会导致难以根据复杂路况优化能量分配。为实现多源交通信息的应用并提高联网自动驾驶增程式电动汽车（CAR-EEV）多目标优化（MOO）的及时性，本文从多目标角度提出一种智能能量管理策略（EMS）。首先，基于SUMO和MATLAB建立交通场景联合仿真平台，并利用收集的数据构建CAR-EEV的数据驱动模型，作为后续EMS研发的数据基础和运行平台。然后，基于网格灰度图利用交通流信息的图像化表示，将多源交通信息转化为二维矩阵。连续交通场景矩阵之间的欧几里得距离用作相似性基础，以优化速度并预测未来车速。此外，采用基于深度强化学习（DRL）的多目标智能EMS，利用深度确定性策略梯度（DDPG）算法综合考虑车辆动力学、能耗经济性和电池退化。这为CAR-EEV建立了端到端智能EMS框架，并通过优先经验回放加速训练收敛。最后，仿真和台架试验表明，这种智能EMS显著改善了车辆动力学和电池寿命，同时显著提高了实时性能和有效性。
