论文标题
MPS-EV能源管理的强化学习进展和摘要
Progress and summary of reinforcement learning on energy management of MPS-EV
论文作者
论文摘要
在环境法规和能源危机下，由内燃机（ICE）引起的高排放和低能效率已变得不可接受。作为有前途的替代解决方案，多功能电源电动汽车（MPS-EV）引入了不同的清洁能源系统，以提高动力总成效率。能源管理策略（EMS）是MPS-EVS最大化效率，燃油经济性和范围的关键技术。增强学习（RL）已成为EMS开发的有效方法。 RL已受到不断的关注和研究，但是仍然缺乏对基于RL的EMS设计元素的系统分析。为此，本文对当前基于RL的EMS（RL-EMS）的研究进行了深入分析，并总结了基于RL的EMS的设计元素。本文首先总结了RL在EMS中的先前应用：算法，感知方案，决策方案，奖励功能和创新培训方法。详细分析了先进算法对训练效果的贡献，文献中的感知和控制方案进行了详细分析，分类了不同的奖励功能设置，并详细介绍了具有角色的创新培训方法。最后，通过比较RL和RL-EMS的开发途径，本文确定了高级RL解决方案与现有RL-EMS之间的差距。最后，本文提出了在EMS中实施高级人工智能（AI）解决方案的潜在发展方向。
The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS.
