Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles (Record no. 85326)
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| fixed length control field | 04031nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-79206-9 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730164125.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2022 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031792069 |
| -- | 978-3-031-79206-9 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 621.3 |
| 100 1# - AUTHOR NAME | |
| Author | Li, Yeuching. |
| 245 10 - TITLE STATEMENT | |
| Title | Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XI, 123 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Advances in Automotive Technology, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Background: Deep Reinforcement Learning -- Learning of EMSs -- Learning of EMSs -- Learning of EMSs/ An Online Integration Scheme for DRL-Based EMSs -- Conclusions -- Bibliography -- Authors' Biographies. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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 core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller. |
| 700 1# - AUTHOR 2 | |
| Author 2 | He, Hongwen. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-79206-9 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
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| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2022. |
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| -- | online resource |
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| -- | text file |
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| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electrical engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mechanical engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Automotive engineering. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electrical and Electronic Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mechanical Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Automotive Engineering. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2576-8131 |
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| -- | ZDB-2-SXSC |
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