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Titlebook: Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles; Yuecheng Li,Hongwen He Book 2022 Springer Nature Switzer

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发表于 2025-3-21 19:28:56 | 显示全部楼层 |阅读模式
书目名称Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles
编辑Yuecheng Li,Hongwen He
视频video
丛书名称Synthesis Lectures on Advances in Automotive Technology
图书封面Titlebook: Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles;  Yuecheng Li,Hongwen He Book 2022 Springer Nature Switzer
描述The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not onlybeing capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the
出版日期Book 2022
版次1
doihttps://doi.org/10.1007/978-3-031-79206-9
isbn_softcover978-3-031-79194-9
isbn_ebook978-3-031-79206-9Series ISSN 2576-8107 Series E-ISSN 2576-8131
issn_series 2576-8107
copyrightSpringer Nature Switzerland AG 2022
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发表于 2025-3-21 21:45:57 | 显示全部楼层
发表于 2025-3-22 04:01:09 | 显示全部楼层
Learning of EMSs in Continuous State Space-Discrete Action Space, and efficient learning algorithm in discrete action spaces. Therefore, to address energy management problems with continuous state—discrete action spaces, this chapter describes an energy management method based on deep Q-learning, and further conduct research on its learning stability, optimizatio
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2576-8107 ut also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the978-3-031-79194-9978-3-031-79206-9Series ISSN 2576-8107 Series E-ISSN 2576-8131
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Michael Hubbard,Marisol Smith,Renu Kohliion for the DRL-based EMS is described in this chapter. Because all DRL-based EMSs described in this book are represented by DNNs, they share the same hardware deployment procedure. The DRL-based EMS in Chapter 3 is utilized here for the illustration.
发表于 2025-3-23 02:49:26 | 显示全部楼层
Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles
发表于 2025-3-23 06:40:26 | 显示全部楼层
2576-8107 cern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-b
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