| 書目名稱 | Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles | | 編輯 | Teng Liu | | 視頻video | http://file.papertrans.cn/826/825946/825946.mp4 | | 叢書名稱 | Synthesis Lectures on Advances in Automotive Technology | | 圖書封面 |  | | 描述 | .Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles. Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems foundedin artificial intelligence and their real-time evaluation and application...In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement | | 出版日期 | Book 2019 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01503-8 | | isbn_softcover | 978-3-031-00375-2 | | isbn_ebook | 978-3-031-01503-8Series ISSN 2576-8107 Series E-ISSN 2576-8131 | | issn_series | 2576-8107 | | copyright | Springer Nature Switzerland AG 2019 |
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