Diagnosis of the powertrain systems for autonomous electric vehicles

Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart Tunan Shen Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart Reihe herausgegeben von Michael Bargende, Stuttgart, Deutschland Hans-Christian Reuss, Stuttgart, Deutschland Jochen Wiedemann, Stuttgart, Deutschland Das Institut für Fahrzeugtechnik Stuttgart (IFS) an der Universität Stuttgart erforscht, entwickelt, appliziert und erprobt, in enger Zusammenarbeit mit der Industrie, Elemente bzw Technologien aus dem Bereich moderner Fahrzeugkonzepte Das Institut gliedert sich in die drei Bereiche Kraftfahrwesen, Fahrzeugantriebe und Kraftfahrzeug-Mechatronik Aufgabe dieser Bereiche ist die Ausarbeitung des Themengebietes im Prüfstandsbetrieb, in Theorie und Simulation Schwerpunkte des Kraftfahrwesens sind hierbei die Aerodynamik, Akustik (NVH), Fahrdynamik und Fahrermodellierung, Leichtbau, Sicherheit, Kraftübertragung sowie Energie und Thermomanagement – auch in Verbindung mit hybriden und batterieelektrischen Fahrzeugkonzepten Der Bereich Fahrzeugantriebe widmet sich den Themen Brennverfahrensentwicklung einschließlich Regelungsund Steuerungskonzeptionen bei zugleich minimierten Emissionen, komplexe Abgasnachbehandlung, Aufladesysteme und -strategien, Hybridsysteme und Betriebsstrategien sowie mechanisch-akustischen Fragestellungen Themen der Kraftfahrzeug-Mechatronik sind die Antriebsstrangregelung/Hybride, Elektromobilität, Bordnetz und Energiemanagement, Funktions- und Softwareentwicklung sowie Test und Diagnose Die Erfüllung dieser Aufgaben wird prüfstandsseitig neben vielem anderen unterstützt durch 19 Motorenprüfstände, zwei Rollenprüfstände, einen 1:1-Fahrsimulator, einen Antriebsstrangprüfstand, einen Thermowindkanal sowie einen 1:1-Aeroakustikwindkanal Die wissenschaftliche Reihe „Fahrzeugtechnik Universität Stuttgart“ präsentiert über die am Institut entstandenen Promotionen die hervorragenden Arbeitsergebnisse der Forschungstätigkeiten am IFS Reihe herausgegeben von Prof Dr.-Ing Michael Bargende Lehrstuhl Fahrzeugantriebe Institut für Fahrzeugtechnik Stuttgart Universität Stuttgart Stuttgart, Deutschland Prof Dr.-Ing Hans-Christian Reuss Lehrstuhl Kraftfahrzeugmechatronik Institut für Fahrzeugtechnik Stuttgart Universität Stuttgart Stuttgart, Deutschland Prof Dr.-Ing Jochen Wiedemann Lehrstuhl Kraftfahrwesen Institut für Fahrzeugtechnik Stuttgart Universität Stuttgart Stuttgart, Deutschland Weitere Bände in der Reihe https://link.springer.com/bookseries/13535 Tunan Shen Diagnosis of the Powertrain Systems for Autonomous Electric Vehicles Tunan Shen Institute of Automotive Engineering (IFS), Chair of Automotive Mechatronics University of Stuttgart Stuttgart, Germany Zugl.: Dissertation Universität Stuttgart, 2021 D93 ISSN 2567-0042 ISSN 2567-0352 (electronic) Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart ISBN 978-3-658-36991-0 ISBN 978-3-658-36992-7 (eBook) https://doi.org/10.1007/978-3-658-36992-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 This work is subject to copyright All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Responsible Editor: Stefanie Eggert This Springer Vieweg imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH part of Springer Nature The registered company address is: Abraham-Lincoln-Str 46, 65189 Wiesbaden, Germany Preface I believe that the electric vehicle revolution is coming and automated driving will come soon Then, most people may not own a car in future any more, but hire self-driving, electric ride-shares to get around For those autonomous electric vehicles, which owned by ride-share companies, the reliability and availability are more important than today’s private owned cars I want to be a part of the change, so I decided to pursue my PhD in the area of diagnosis of powertrain system for autonomous electric vehicles since 2017, after three years working at Robert Bosch GmbH My deep gratitude goes to my supervisor Prof Dr.-Ing Hans-Christian Reussfor his endless support, enthusiasm, knowledge and friendship I am extremely grateful to my supervisors at Robert Bosch GmbH Dr Ahmet Kilic and Dr Christian Thulfaut for the valuable support and motivation Their insights and advice give me a great help Special thanks to Dr Norbert Müller and Dr Achim Henkel for giving me the chance to carry out this work at Corporate Sector Research and Advance Engineering at Robert Bosch GmbH in Renningen My appreciation also extends to Adam Babik, Dr Andreas Vogt, Dr Andreas Schönknecht, Daniel Acs, Dr Deng Shi, Erik Hoevenaars, Dr Paul Mehringer, Rajaram Suresh and Yuping Chen for your valuable comments on this work and all my colleagues for the wonderful cooperation as well Furthermore, I want to thank my wife Shiang and my parents You believed in me when I was doubt You kept me motivated through my darkest thoughts Finally, I would like to thank my daughter Cindy Thank you for coming into my life Thank you for making me smile like crazy Thank you for making me happy Leonberg Tunan Shen Inhaltsverzeichnis Preface V Figures XI Tables XV Abbreviations XVII Symbols XIX Abstract XXI Kurzfassung XXIII Introduction 1.1 Motivation 1.2 Objectives 1.3 Contributions of the Thesis 1.4 Organization of the Thesis Fault Analysis 2.1 2.2 2.3 2.4 2.5 Battery Inverter Electric Machine Gearbox 10 Scope of the Thesis 10 Background and State of the Art 13 3.1 3.2 Fault Diagnostic Methods 13 3.1.1 Signal-based Fault Detection Methods 13 3.1.2 Model-based Fault Detection Methods 14 3.1.3 Data-based Fault Detection Methods 15 Signal Processing Techniques 16 3.2.1 Time Domain Features 16 3.2.2 Statistical Features 16 3.2.3 Frequency Domain Features 17 3.2.4 Envelope Analysis 17 VIII 3.3 Inhaltsverzeichnis 3.2.5 Time Frequency Domain Features 18 Machine Learning Algorithms 21 3.3.1 Decision Tree 21 3.3.2 Anomaly Detection 22 3.3.3 Artificial Neural Network 23 Diagnosis of Electrical Faults in Electric Machines 27 4.1 4.2 4.3 Related Works and Current Challenges 27 Contributions of the Thesis 29 Analytical Modeling of Faults 30 4.3.1 Analytical Modeling of a Healthy PMSM 30 4.3.2 Analytical Modeling of PMSM with TSC 32 4.3.3 Analytical Modeling of PMSM with PSC 32 4.3.4 Analytical Modeling of PMSM with UWR 33 4.3.5 Analytical Modeling of PMSM with Sensor Faults 33 4.4 Analysis of the Behavior of a PMSM in Different Conditions 34 4.5 Feature Engineering 38 4.5.1 Data Normalization 38 4.5.2 Feature Extraction 39 4.6 Diagnostic Concept 41 4.7 Physical Model based Diagnostic Model 42 4.8 Self Condition Monitoring (SCM) Diagnostic Model 48 4.9 Fleet Data-based Fault Diagnostic Model 52 4.10 Multi-stage Diagnostic Concept 56 4.11 Conclusion 65 Diagnosis of Mechanical Faults in Electric Machines 67 5.1 5.2 5.3 5.4 5.5 Fault Mechanisms of Bearing 67 Related Works 69 Current Challenges and Contribution of the Thesis 72 5.3.1 Current Challenges 72 5.3.2 Contributions of the Thesis 72 Data Set Description 73 Feature Engineering 75 5.5.1 De-noising 75 Inhaltsverzeichnis 5.6 5.7 5.8 IX 5.5.2 Feature Extraction 76 5.5.3 Evaluation of Features 90 Validation with Other eAxles 92 Diagnostic Concept 97 Conclusion .101 Conclusion and Outlook 103 Bibliography .107 Figures 2.1 2.2 2.3 3.1 3.2 3.3 3.4 3.5 Failure mechanism of a battery fire Distribution of fragile components Distribution of failed components in electric machines Scheme of a signal-based fault detection method 14 Scheme of a model-based fault detection method 15 Scheme of a data-based fault detection method 15 Envelope of a vibration signal 19 An example of STFT (a) Quadratic chirp signal; (b) STFT of quadratic chirp signal 20 3.6 A schematic structure of decision tree 21 3.7 (a) Distance-based anomaly detection; (b) Density-based anomaly detection 23 3.8 Diagram of an artificial neuron 24 3.9 Structure of neural network 25 4.1 Equivalent circuit of healthy and faulty stators of PMSM 31 4.2 The difference of a current sensor in healthy and faulty conditions 34 4.3 Operating area of PMSM 35 4.4 Current and torque of machine in different conditions at operating point Tre f = 100 Nm, nmech = 5000 rpm 37 4.5 Comparison of the three phase currents of healthy and faulty conditions at operating point Tre f = 40 Nm, nmech = 4000 rpm 38 4.6 Comparison of symptoms in different conditions 44 4.7 The tree structure of decision tree model 45 4.8 Normalized confusion matrices of the physical model on (a) training and (b) test data 46 4.9 Wrong predictions of the physical model 47 4.10 Distribution of distance 51 4.11 Confusion matrices of the SCM model on (a) training and validation data and (b) test data 51 4.12 Wrong 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mechanical faults in electric machines 1.4 Organization of the Thesis The rest of the thesis... fault diagnosis [46] 2.5 Scope of the Thesis In the previous sections, the most critical faults in the powertrain system of autonomous electric vehicles are summarized, and the state of the art... temperature The electrical stress caused by the dielectric material, the phenomena of tracking and corona, and the high transient voltages For the electric machines used in the powertrain system, the