Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles

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Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart

ISBN: 3658369914
ISBN 13: 9783658369910
Autor: Shen, Tunan
Verlag: Springer Vieweg
Umfang: xxxii, 120 S., 57 s/w Illustr., 4 farbige Illustr., 120 p. 61 illus., 4 illus. in color.
Erscheinungsdatum: 03.03.2022
Auflage: 1/2022
Produktform: Kartoniert
Einband: KT

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 AuthorTunan 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.

Artikelnummer: 5071999 Kategorie:

Beschreibung

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.

Autorenporträt

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.

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