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008			210108s2021 gw a o |||| 0|eng d
020			_a9783658322137
024	7		_a10.1007/978-3-658-32213-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aTK2514 _b2021 EB
100	1		_aZhang, Xudong _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678011
245	1	0	_aModeling and Dynamics Control for Distributed Drive Electric Vehicles _cby Xudong Zhang
250			_aFirst edition 2021
264		1	_aWiesbaden _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XVII, 208 páginas) _b117 ilustraciones, 104 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
505	0		_aIntroduction -- Literature Review -- Distributed Drive Electric Vehicle Model -- Vehicle State and Tire Road Friction Coefficient Estimation -- Direct Yaw Moment Controller Design -- Stability Based Control Allocation Using KKT Global Optimization Algorithm -- Energy Efficient Toque Allocation for Traction and Regenerative Braking -- Simulation and Verification on the Proposed Model and Control Strategy -- Conclusions and Future Work.
520	3		_aDue to the improvements on electric motors and motor control technology, alternative vehicle power system layouts have been considered. One of the latest is known as distributed drive electric vehicles (DDEVs), which consist of four motors that are integrated into each drive and can be independently controllable. Such an innovative design provides packaging advantages, including short transmission chain, fast and accurate torque response, and so on. Based on these advantages and features, this book takes stability and energy-saving as cut-in points, and conducts investigations from the aspects of Vehicle State Estimation, Direct Yaw Moment Control (DYC), Control Allocation (CA). Moreover, lots of advanced algorithms, such as general regression neural network, adaptive sliding mode control-based optimization, as well as genetic algorithms, are applied for a better control performance. About the author Xudong Zhang received the M.S. degree in mechanical engineering from Beijing Institute of Technology, China, and the Ph.D. degree in mechanical engineering from Technical University of Berlin, Germany. Since 2017, he has joined in Beijing Institute of Technology as an Associate Research Fellow. His main research interests include vehicle dynamics control, autonomous vehicles, and power management of hybrid electric vehicles. .
988			_aSpringer_Engineering_2021
650		7	_2embne _aMotores eléctricos _9138110
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-658-32213-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2021 _dz _eb _zSI