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001 978-3-031-79206-9
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020 _a9783031792069
_9978-3-031-79206-9
024 7 _a10.1007/978-3-031-79206-9
_2doi
050 4 _aTK1-9971
072 7 _aTHR
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTHR
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082 0 4 _a621.3
_223
100 1 _aLi, Yeuching.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982242
245 1 0 _aDeep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles
_h[electronic resource] /
_cby Yeuching Li, Hongwen He.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXI, 123 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 _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 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.
650 0 _aElectrical engineering.
_982243
650 0 _aMechanical engineering.
_95856
650 0 _aAutomotive engineering.
_982244
650 1 4 _aElectrical and Electronic Engineering.
_982245
650 2 4 _aMechanical Engineering.
_95856
650 2 4 _aAutomotive Engineering.
_982246
700 1 _aHe, Hongwen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982247
710 2 _aSpringerLink (Online service)
_982248
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031792182
776 0 8 _iPrinted edition:
_z9783031791949
776 0 8 _iPrinted edition:
_z9783031792304
830 0 _aSynthesis Lectures on Advances in Automotive Technology,
_x2576-8131
_982249
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79206-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c85326
_d85326