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008			230504s2022 sz | s |||| 0|eng d
020			_a9783031792069
024	7		_a10.1007/978-3-031-79206-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTL221.15 _b2022 EB
100	1		_aYeuching, Li _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688341
245	1	0	_aDeep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles _cby Li Yeuching, He Hongwen
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XI, 123 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		_aIntroduction -- 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			_aThe 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 only being 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.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9667309 _aAutomóviles híbridos
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aHongwen, He _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688342
776	0	8	_iPrinted edition: _z9783031792182
776	0	8	_iPrinted edition: _z9783031791949
776	0	8	_iPrinted edition: _z9783031792304
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79206-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eIG _zSI