多代理深度增强学习与移动充电车辆调度的进化战略
IEEE transactions on neural networks and learning systems
|January 12, 2026
概括
移动充电车 (MCV) 为电动汽车 (EV) 充电提供了灵活的解决方案. 一个新的多代理深度强化学习与进化战略 (MARL-ES) 框架优化了MCV调度,以提高效率和利能力.
科学领域:
- 智能运输系统 智能运输系统
- 运营研究 运营研究
- 人工智能的人工智能是人工智能.
背景情况:
- 固定充电基础设施限制了电动汽车 (EV) 服务.
- 移动充电车 (MCV) 的动态调度是一个有前途的解决方案.
- 随机需求和高运营成本挑战了当前的MCV调度.
研究的目的:
- 制定一个适应性和高效的MCV调度策略.
- 为了解决静态和传统MCV调度方法的局限性.
- 在实时电动汽车充电服务中改善供需平衡.
主要方法:
- 制定了MCV调度问题作为马尔科夫决策过程 (MDP).
- 提出了一个新的多代理深度强化学习 (MARL) 框架与进化战略 (ES) (MARL-ES).
- 利用分散执行的集中培训 (CTDE) 和集成的行动空间ES,包括突变和基于细分的交叉运营商.
主要成果:
- 马尔-ES显著优于静态优化和传统的马尔方法.
- 在总利方面取得了明显的改善,降低了运营成本,并最大限度地减少了MCV的移动距离.
- 在不同的车队大小和不确定的条件下展示了强大的可扩展性和适应性.
结论:
- 马尔-ES为智能移动充电服务 (MCS) 提供了一种实用且适应性的调度解决方案.
- 该框架有效地处理电动汽车需求和MCV状态的时空变化.
- 这种方法提高了电动汽车充电服务的效率和经济可行性.
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