基于多个来源信息的基础上,研究连接范围扩大电动汽车的智能能源管理策略
Xuewen Zhai1, Hanwu Liu2, Wencai Sun1
1Transportation College of Jilin University, Changchun, 130022, Jilin, China.
Scientific reports
|April 14, 2025
概括
本研究介绍了连接自动化范围扩展电动汽车 (CAR-EEV) 的智能能源管理策略 (EMS). 它通过使用深度强化学习集成多源交通数据来提高能源效率和电池寿命.
科学领域:
- 汽车工程 汽车工程
- 人工智能的人工智能
- 能源系统 能源系统
背景情况:
- 在电动汽车中优化能源分配是具有挑战性的,因为它依赖于有限的车辆特定数据,忽视了关键的多源交通信息.
- 连接自动化扩展范围的电动汽车 (CAR-EEV) 需要先进的战略,以在复杂的交通环境中高效地管理能源.
研究的目的:
- 为CAR-EEVs提出一个智能能源管理策略 (EMS),该策略集成了多来源的交通信息,以提高多目标优化 (MOO).
- 通过考虑复杂的道路条件和车辆动态来提高能源分配的及时性和有效性.
主要方法:
- 开发了一个联合的交通模拟平台 (SUMO/MATLAB) 和一个数据驱动的CAR-EEV模型.
- 将物质化的多源流量信息转化为2D矩阵,使用网格灰度地图进行速度预测.
- 采用基于深度强化学习 (DRL) 的多目标智能EMS与深度决定性政策梯度 (DDPG) 算法.
主要成果:
- 拟议的EMS显著提高了车辆动态和电池寿命.
- 实时性能和能源管理的有效性得到了显著提高.
- DDPG算法有效地平衡了车辆动态,能源节约和电池退化.
结论:
- 为CAR-EEVs开发的端到端智能EMS框架有效地利用多源流量数据.
- 这一战略为优化互联自动化电动汽车的能源管理提供了有前途的解决方案.
- 与传统方法相比,DRL和多源流量数据的整合带来了更高的性能.
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