An uncertainty-weighted Bayesian-fusion SVGMR framework for robust battery SOH estimation under partial charging conditions
Papers|更新时间:2026-10-02
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An uncertainty-weighted Bayesian-fusion SVGMR framework for robust battery SOH estimation under partial charging conditions
面向部分充电条件的不确定性加权贝叶斯融合SVGMR鲁棒电池健康状态估计框架
Journal of Reliability Science and EngineeringVol. 2, Issue 3, Pages: 1-15(2026)
作者机构:
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, People’s Republic of China
Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, People’s Republic of China
Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, People’s Republic of China
作者简介:
Hongjie Lv received a BE degree in Automation from Taiyuan University of Technology, Taiyuan, China, in 2025. He is currently pursuing an MS degree in Instrument Science and Technology at Hebei University of Technology, Tianjin, China. His research interests include health prognostics of batteries.
Xingchen Liu received a BE degree in Measurement and Control Technology and Instruments from Hunan University, Changsha, China, in 2014, an MS degree in Instrument Science and Technology from the University of Science and Technology of China, Hefei, China, in 2017, and a PhD degree in Industrial Systems Engineering and Management from the National University of Singapore, Singapore, in 2021. He is currently a Research Assistant Professor at the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China. His research interests include machine learning, process monitoring, reliability engineering, and prognostics and health management.
Dong Wang received his PhD degree from the City University of Hong Kong, Hong Kong, in 2015. He then held positions as a Senior Research Assistant, a Postdoctoral Fellow, and a Research Fellow at the same university from 2015 to 2018. He is currently a Tenured Associate Professor in the Department of Industrial Engineering and Management School of Mechanical Engineering, at Shanghai Jiao Tong University, Shanghai, China, where he is also affiliated with the State Key Laboratory of Mechanical System and Vibration. His research interests include sparsity and complexity measures, signal processing, statistical learning, probability models, optimization, and their applications to condition monitoring, fault diagnosis, prognostics and health management, and nondestructive testing.
Bingchang Hou received his PhD degree in Mechanical Engineering, Industrial Engineering, from Shanghai Jiao Tong University, Shanghai, China, in 2024. During his PhD study, he was a visiting student at the City University of Hong Kong for one year. He is currently with the State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, China. His research interests include machine fault diagnosis and prognostics, intelligent autonomous systems, and robotics. Dr Hou has received five Outstanding Reviewer Awards from IEEE Transactions on Instrumentation and Measurement and Mechanical Systems and Signal Processing.
Dong Zhen received BS and MS degrees from Shandong University of Science and Technology, Qingdao, China, in 2006 and 2009, respectively, and a PhD degree from the University of Huddersfield, Huddersfield, UK, in 2012, all in Mechanical Engineering. He is currently a Professor of the School of Mechanical Engineering, Hebei University of Technology, Tianjin, China. His main research interests include machinery fault diagnostics, signal processing, and intelligent algorithms development.
Jinzhen Kong received a BS degree from Harbin Engineering University, Harbin, China, in 2017, and a PhD degree from Shanghai Jiao Tong University, Shanghai, China, in 2023. She is currently an Associate Professor in School of Mechanical Engineering, Hebei University of Technology, Tianjin, China. Her research interests are prognostics and health management, degradation modeling, and health prognostics of batteries.