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Modeling and dynamics control for distributed drive electric vehicles

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling and dynamics control for distributed drive electric vehiclesby Xudong Zhang.
Author:	Zhang, Xudong.
Published:	Wiesbaden :Springer Fachmedien Wiesbaden :2021.
Description:	xvii, 208 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electric motorsAutomatic control.
Online resource:	https://doi.org/10.1007/978-3-658-32213-7
ISBN:	9783658322137$q(electronic bk.)

Modeling and dynamics control for distributed drive electric vehicles
Zhang, Xudong.

Modeling and dynamics control for distributed drive electric vehicles[electronic resource] /by Xudong Zhang. - Wiesbaden :Springer Fachmedien Wiesbaden :2021. - xvii, 208 p. :ill., digital ;24 cm.

Introduction -- 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.

Due 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.

ISBN: 9783658322137$q(electronic bk.)

Standard No.: 10.1007/978-3-658-32213-7doiSubjects--Topical Terms:

184508
Electric motors
--Automatic control.

LC Class. No.: TK2851 / .Z436 2021

Dewey Class. No.: 621.46

Modeling and dynamics control for distributed drive electric vehicles
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245 1 0 $a Modeling and dynamics control for distributed drive electric vehicles $h [electronic resource] / $c by Xudong Zhang.
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505 0 $a Introduction -- 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 $a Due 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.
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