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Diagnosis of the powertrain systems for autonomous electric vehicles / Tunan Shen.

9783658369927 (electronic bk.)
3658369922 (electronic bk.)
9783658369910
3658369914

Wiesbaden : Springer, [2022]

©2022

English

1 online resource : illustrations (some color).

10.1007/978-3-658-36992-7 doi

TL220 .S54 2022

629.22/93

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.

Includes bibliographical references.

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Online resource; title from PDF title page (SpringerLink, viewed March 10, 2022).

Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart. 2567-0352

Print version: 9783658369910

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