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Reinforcement learning-enabled intel...

Liu, Teng.

Reinforcement learning-enabled intelligent energy management for hybrid electric vehicles /

紀錄類型:	書目-語言資料,印刷品 : 單行本
正題名/作者:	Reinforcement learning-enabled intelligent energy management for hybrid electric vehicles // Teng Liu.
作者:	Liu, Teng.
出版者:	[San Rafael, California] :Morgan & Claypool,c2019.
面頁冊數:	ix, 89 p. :col. ill., col. port. ;24 cm.
標題:	Electric vehicles - Design and construction. -
ISBN:	9781681736204 (bound)
ISBN:	1681736209 (bound)
ISBN:	9781681736181 (pbk.) :
ISBN:	1681736187 (pbk.)
ISBN:	9781681736198 (ebk.)
ISBN:	1681736195 (ebk.)

Reinforcement learning-enabled intelligent energy management for hybrid electric vehicles /
Liu, Teng.

Reinforcement learning-enabled intelligent energy management for hybrid electric vehicles /Teng Liu. - [San Rafael, California] :Morgan & Claypool,c2019. - ix, 89 p. :col. ill., col. port. ;24 cm. - Synthesis lectures on advances in automotive technology,#92576-8107 ;. - Synthesis lectures on advances in automotive technology ;#9..

Includes bibliographical references (p. 79-88).

'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 founded in 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 learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed."--

ISBN: 9781681736204 (bound)Subjects--Topical Terms:

169668
Electric vehicles
--Design and construction.

LC Class. No.: TL220 / .L587 2019

Dewey Class. No.: 629.22/93

Reinforcement learning-enabled intelligent energy management for hybrid electric vehicles /
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245 1 0 $a Reinforcement learning-enabled intelligent energy management for hybrid electric vehicles / $c Teng Liu.
260 # $a [San Rafael, California] : $b Morgan & Claypool, $c c2019.
300 $a ix, 89 p. : $b col. ill., col. port. ; $c 24 cm.
490 1 $a Synthesis lectures on advances in automotive technology, $x 2576-8107 ; $v #9
504 $a Includes bibliographical references (p. 79-88).
520 # $a '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 founded in 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 learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed."-- $c Provided by publisher.
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