The hybrid-electric powertrain presents an immediate solution to energy and environmental challenges encountered within the realm of transportation. Targeting the optimization of hybrid-electric powertrains, deep reinforcement learning (DRL) has been intensively and increasingly investigated to develop intelligent energy management systems in the context of augmented vehicular and traffic information. After a brief introduction to the Markov Decision Process and DRL, this paper presents a comprehensive survey of the recent advancements in DRL-based energy management. The survey categorizes the progress based on the various roles that DRL plays in energy management systems, highlighting the flexibility and advantages of integrating DRL for achieving energy efficiency, safety, and reliable performance. Furthermore, the study concludes with an analysis of open issues and future prospects, including the learning and application of DRL-based energy management strategies, development of novel DRL algorithms, and integration of DRL-based energy management in intelligent and sustainable transportation contexts.

中文翻译：

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混合电动动力系统智能能量管理系统的深度强化学习：最新进展、未解决的问题和前景


混合电动动力系统为交通领域遇到的能源和环境挑战提供了直接的解决方案。针对混合电动动力系统的优化，深度强化学习（DRL）已得到越来越多的深入研究，以在增强的车辆和交通信息的背景下开发智能能源管理系统。在简要介绍马尔可夫决策过程和 DRL 之后，本文对基于 DRL 的能源管理的最新进展进行了全面的调查。该调查根据 DRL 在能源管理系统中发挥的各种作用对进展进行了分类，强调了集成 DRL 以实现能源效率、安全性和可靠性能的灵活性和优势。此外，该研究最后还分析了未解决的问题和未来前景，包括基于 DRL 的能源管理策略的学习和应用、新型 DRL 算法的开发以及基于 DRL 的能源管理在智能和可持续交通环境中的集成。