Prediction Methods and Evaluation of the Fatigue Life for Automotive Structural Components

Prediction Methods and Evaluation of the Fatigue Life for Automotive Structural Components covers this important component that affects the performance of the entire vehicle. The light weight of automotive structural components is one of the sustainable solutions to energy and environmental issues, and the development technology of its core components and vehicle performance evaluation technology are its key development directions. To gradually replace traditional cars on a large scale, electric vehicles need to address the durability and reliability issues of the entire vehicle and key components.

• Covers fatigue life prediction and evaluation for automotive structural components based on product forward design
• Includes prediction methods of fatigue life for different structural components
• Helps readers gain an understanding of both the theoretical and practical aspects of structural components fatigue life

Dr Xintian Liu is the Vice Dean of the School of Mechanical and Automotive Engineering at Shanghai University of Engineering Science, China, and also serves as a member of the Modern Management Branch of the China Society of Automotive Engineering and the Automotive Reliability Professional Committee of Shanghai Society of Automotive Engineering. Currently, he is mainly engaged in research in automotive reliability engineering, fatigue life prediction and evaluation, structural system uncertainty, and functional safety of intelligent vehicle systems. He has published more than 100 papers as the first or corresponding author

Professor Yu Fang is currently a Professor at the School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, China. He received his PhD from Harbin Institute of Technology, China, in 2008

Dr Xu Wang received her PhD degree in pattern recognition and intelligent system in 2013 from Donghua University, China. She is currently a lecturer at the school of mechanical and automotive engineering, Shanghai University of Engineering Science, China. Her research interests include prediction methods, evolutionary computation, optimization methods, deep learning and their applications

Dr Xiaolan Wang received her M.S. and Ph.D. degrees in vehicle application engineering from Jilin University, Changchun, China, in 2009 and 2012, respectively. She is currently a lecturer with the School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China. Her active research interests include intelligent vehicles, driving assistance systems, and driver behaviour