The motor drive system plays a significant role in the safety and function of electric vehicles as a bridge for power transmission. In order to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system.
AI Techniques in EV Motor and Inverter Fault Detection and Diagnosis comprehensively covers the recently-developed AI applications for solving condition monitoring and fault detection issues in EV electrical conversion systems. AI-based fault detection and diagnosis (FDD) is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarised.
Chapters systematically address condition monitoring and fault detection in EV motors and inverters. Four case studies are including, covering AI based electric motor fault diagnosis, AI based inverter/IGBT fault diagnosis, AI based bearing fault diagnosis, and AI based gearbox fault diagnosis. Alongside each case study, the authors discuss the differences between conventional methods and AI-based methods in EV applications, and the motivation, advantages, shortcomings and challenges of AI-based methods. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are explored.
Providing a systematic and thorough exploration of its field, this book is a valuable resource for researchers and students with an interest in the applications of AI in electric vehicles, and for engineers and research and development professionals in the electric automotive industry.
This book comprehensively covers the recently-developed AI techniques for solving condition monitoring and fault detection issues in EV electrical conversion systems. Chapters systematically address condition monitoring and fault detection in EV motors and inverters, with illustrative case studies.
- Chapter 1: Introduction
- Chapter 2: Feature Extraction Engineering (FEE) for EV Electric Powertrain Fault Diagnosis
- Chapter 3: AI Based Electric Motor Fault Diagnosis for Electric Powertrain in EVs
- Chapter 4: Case study 1 - AI Based Electric Motor Fault Diagnosis
- Chapter 5: Case study 2 - AI Based Inverter/IGBT Fault Diagnosis
- Chapter 6: Case study 3 - AI Based Bearing Fault Diagnosis
- Chapter 7: Case study 4 - AI Based Gearbox Fault Diagnosis
- Chapter 8: Discussion and Future Development of AI Based Fault Diagnosis for Electric Powertrain in EVs
Yihua Hu is a reader at the King's College London, UK. He was previously the head of the Electrical Engineering Group at the University of York. He is a fellow of the IET, holds a Royal Society Industry Fellowship, and is a member of the UK Young Academy. He has published 120 journal papers in IEEE Transactions journals with an H-index of 55 in Google Scholar. He is the author of 15 patents.
Xiaotian Zhang is currently pursuing his PhD in electrical and electronics engineering at the University of York, UK. He received his BSc degree in electrical engineering from Hohai University, Nanjing, China, in 2018 and received his MSc degree in electrical engineering from Kings College London, London, UK, in 2020. His research interests include AI-supported EV electric powertrain health monitoring, fault detection, and safety improvement.
Wangjie Lang is currently pursuing his PhD in electrical and electronics engineering at the University of York, UK. He received his BEng degree in electrical engineering from the University of Strathclyde, Glasgow, UK, and Lanzhou University of Technology, Lanzhou, China, in 2020. His research interests include electrical machine fault detection and diagnosis and EV powertrain break engineering-based AI techniques.
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