Modeling and Dynamics Control for Distributed Drive Electric Vehicles (Record no. 78221)

000 -LEADER
fixed length control field 03528nam a22005055i 4500
001 - CONTROL NUMBER
control field 978-3-658-32213-7
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801220121.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210108s2021 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783658322137
-- 978-3-658-32213-7
082 04 - CLASSIFICATION NUMBER
Call Number 621.4
100 1# - AUTHOR NAME
Author Zhang, Xudong.
245 10 - TITLE STATEMENT
Title Modeling and Dynamics Control for Distributed Drive Electric Vehicles
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2021.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVII, 208 p. 117 illus., 104 illus. in color.
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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 ## - SUMMARY, ETC.
Summary, etc 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. .
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-658-32213-7
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Wiesbaden :
-- Springer Fachmedien Wiesbaden :
-- Imprint: Springer Vieweg,
-- 2021.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engines.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automotive engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Vehicles.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engine Technology.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automotive Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Vehicle Engineering.
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-- ZDB-2-ENG
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-- ZDB-2-SXE

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