使用线下强化学习的电动汽车的数据驱动能源管理
Yong Wang1,2, Jingda Wu1,2, Hongwen He3,4
1School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China.
Nature communications
|March 23, 2025
概括
本研究介绍了使用线下强化学习的电动汽车的数据驱动能源管理框架. 它通过从真实世界的数据中学习来优化性能并减少退化,超越基于模拟的方法.
科学领域:
- 可持续能源 可持续能源
- 人工智能的人工智能
- 汽车工程 汽车工程
背景情况:
- 电动汽车 (EV) 能源管理对于可持续性至关重要,但由于模拟限制,实际应用滞后.
- 当前的方法通常依赖于手动规则或高保真模拟,无法弥合理论和实践之间的差距.
- 优化电动汽车的能源消耗和系统寿命需要实际的,数据驱动的解决方案.
研究的目的:
- 为电动汽车引入一个真实的数据驱动的能源管理框架.
- 通过运营数据利用线下强化学习 (RL),消除手动规则或模拟的需要.
- 通过自适应式学习来提高EV性能并减少系统退化.
主要方法:
- 开发了一个线下强化学习框架,利用广泛的电动汽车操作数据.
- 将框架集成到现有系统中,以提高部署后的性能.
- 使用来自中国的真实数据验证了燃料电池电动汽车 (FCEV) 的方法.
主要成果:
- 数据驱动的框架证明了FCEV的优越能源优化和减少系统退化.
- 随着数据更新,性能从理论最佳值的88%提高到98.6%.
- 在超过6000万公里的数据上进行训练,使RL代理能够在多样化和未见的场景中进行概括.
结论:
- 现实世界的数据驱动方法,特别是离线RL,为优化电动汽车能源管理提供了可行的途径.
- 大规模的车辆数据利用显著提高了能源效率,并延长了车辆的寿命.
- 这种方法弥合了模拟和实践之间的差距,提高了电动汽车的性能和可持续性.
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