Diagnosis of the powertrain systems for autonomous electric vehicles / Tunan Shen.
Shen, Tunan, author.
2022
TL220 .S54 2022
Available Online

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Title Diagnosis of the powertrain systems for autonomous electric vehicles / Tunan Shen.
Author Shen, Tunan, author.
ISBN 9783658369927 (electronic bk.)
3658369922 (electronic bk.)
9783658369910
3658369914
DOI https://doi.org/10.1007/978-3-658-36992-7
Published Wiesbaden : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource : illustrations (some color).
Other Standard Identifiers 10.1007/978-3-658-36992-7 doi
Call Number TL220 .S54 2022
Dewey Decimal Classification 629.22/93
Summary Tunan Shen aims to increase the availability of powertrain systems for autonomous electric vehicles by improving the diagnostic capability for critical faults. Following the fault analysis of powertrain systems in battery electric vehicles, the focus is on the electrical and mechanical faults of the electric machine. A multi-level diagnostic approach is proposed, which consists of multiple diagnostic models, such as a physical model, a data-based anomaly detection model, and a neural network model. To improve the overall diagnostic capability, a decision making function is designed to derive a comprehensive decision from the predictions of various operating points and different models. Contents Background and State of the Art Diagnosis of Electrical Faults in Electric Machines Diagnosis of Mechanical Faults in Electric Machines Target Groups Researchers and students of mechanical engineering, especially automotive powertrains in electric vehicles Research and development engineers in this field About the Author Tunan Shen did his PhD project at the Institute of Automotive Engineering (IFS), University of Stuttgart, Germany. Currently he is Software Developer for Cross Domain Computing Solutions at a German automotive supplier.
Bibliography, etc. Note Includes bibliographical references.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed March 10, 2022).
Series Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart. 2567-0352
Available in Other Form Print version: 9783658369910
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Background and State of the Art
Diagnosis of Electrical Faults in Electric Machines
Diagnosis of Mechanical Faults in Electric Machines.

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