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007			cr nn 008mamaa
008			220601s2019 sz | o |||| 0|eng d
020			_a9783031015038
024	7		_a10.1007/978-3-031-01503-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTL220 _b2019 EB
100	1		_aLiu, Teng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687799
245	1	0	_aReinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles _cby Teng Liu
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (X, 90 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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 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.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9151458 _aVehículos eléctricos _xBaterías
776	0	8	_iPrinted edition: _z9783031000089
776	0	8	_iPrinted edition: _z9783031003752
776	0	8	_iPrinted edition: _z9783031026317
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01503-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eb _zSI