关于MPS-EV能源管理的强化学习的进展情况和总结
Yang Lin1, Liang Chu1, Jincheng Hu2
1College of Automotive Engineering, Jilin University, Changchun, 130022, China.
Heliyon
|January 1, 2024
概括
多功率源电动汽车 (MPS-EVs) 为内燃机提供了更清洁的替代品. 本研究分析了MPS-EV中的能源管理策略 (EMS) 的强化学习 (RL),确定差距和未来的AI方向.
科学领域:
- 汽车工程 汽车工程
- 人工智能的人工智能
- 可持续能源 可持续能源
背景情况:
- 内燃机 (ICE) 面临着环境和能源方面的挑战.
- 多功率源电动汽车 (MPS-EVs) 集成清洁能源系统,以提高效率.
- 能源管理策略 (EMS) 对于优化MPS-EV性能至关重要.
研究的目的:
- 为MPS-EV系统地分析基于强化学习的EMS (RL-EMS) 的设计元素.
- 为了弥合先进的RL解决方案和当前RL-EMS应用程序之间的差距.
- 确定人工智能在EMS中的未来发展方向.
主要方法:
- 综合审查现有关于RL对EMS的研究.
- 在五个关键方面分析RL应用程序:算法,感知,决策,奖励功能和培训.
- 对RL和RL-EMS发展轨迹的比较.
主要成果:
- 突出了先进算法的对RL-EMS培训有效性的影响.
- 详细介绍RL-EMS中的感知和控制方案.
- 将奖励功能设置分类,并解释创新的培训方法.
结论:
- 确定了先进的AI/RL和当前RL-EMS实现之间的差距.
- 建议在MPS-EV能源管理中开发先进AI解决方案的潜在开发途径.
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