000			03901nam a22005535i 4500
001			978-3-031-01503-8
003			DE-He213
005			20240730164104.0
007			cr nn 008mamaa
008			220601s2019 sz | s |||| 0|eng d
020			_a9783031015038 _9978-3-031-01503-8
024	7		_a10.1007/978-3-031-01503-8 _2doi
050		4	_aTK1-9971
072		7	_aTHR _2bicssc
072		7	_aTEC007000 _2bisacsh
072		7	_aTHR _2thema
082	0	4	_a621.3 _223
100	1		_aLiu, Teng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _982044
245	1	0	_aReinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles _h[electronic resource] / _cby Teng Liu.
250			_a1st ed. 2019.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019.
300			_aX, 90 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSynthesis Lectures on Advances in Automotive Technology, _x2576-8131
505	0		_aPreface -- Introduction -- Powertrain Modeling and Reinforcement Learning -- Prediction and Updating of Driving Information -- Evaluation of Intelligent Energy Management System -- Conclusion -- References -- Author's Biography.
520			_aPowertrain 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 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.
650		0	_aElectrical engineering. _982045
650		0	_aMechanical engineering. _95856
650		0	_aAutomotive engineering. _982046
650		0	_aTransportation engineering. _93560
650		0	_aTraffic engineering. _915334
650	1	4	_aElectrical and Electronic Engineering. _982047
650	2	4	_aMechanical Engineering. _95856
650	2	4	_aAutomotive Engineering. _982048
650	2	4	_aTransportation Technology and Traffic Engineering. _932448
710	2		_aSpringerLink (Online service) _982049
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031000089
776	0	8	_iPrinted edition: _z9783031003752
776	0	8	_iPrinted edition: _z9783031026317
830		0	_aSynthesis Lectures on Advances in Automotive Technology, _x2576-8131 _982050
856	4	0	_uhttps://doi.org/10.1007/978-3-031-01503-8
912			_aZDB-2-SXSC
942			_cEBK
999			_c85288 _d85288