Volume 30 Issue 1
Jan.  2021
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JIA Jianfang, WANG Keke, PANG Xiaoqiong, SHI Yuanhao, WEN Jie, ZENG Jianchao. Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model[J]. Chinese Journal of Electronics, 2021, 30(1): 26-35. doi: 10.1049/cje.2020.10.012
Citation: JIA Jianfang, WANG Keke, PANG Xiaoqiong, SHI Yuanhao, WEN Jie, ZENG Jianchao. Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model[J]. Chinese Journal of Electronics, 2021, 30(1): 26-35. doi: 10.1049/cje.2020.10.012

Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model

doi: 10.1049/cje.2020.10.012
Funds:

the Key Program of Research and Development of Shanxi Province 201703D111011

the Research Project Supported by Shanxi Scholarship Council of China 2020-114

the Natural Science Foundation of Shanxi Province 201801D121159

the Natural Science Foundation of Shanxi Province 201801D221208

the Natural Science Foundation of Shanxi Province 201801D121188

the Natural Science Foundation of Shanxi Province 201901D111164

the Graduate Science and Technology Project of NUC 20191666

More Information
  • Author Bio:

    WANG Keke   is an M.E. candidate with the School of Electrical and Control Engineering, NUC. His research interests include remaining useful life prediction of lithium-ion battery and modeling and optimization of complex systems

    PANG Xiaoqiong   received the M.E. degree from NUC, and the Ph.D. degree from South China Agricultural University. She is an associate professor with the School of Data Science and Technology, NUC. Her research interests include control of complex networks, network security, and prediction algorithm

    SHI Yuanhao   received the M.E. degree from the Taiyuan University of Science and Technology, and the Ph.D. degree from Shanghai Jiao Tong University, China. He is a lecturer with the School of Electrical and Control Engineering, NUC. His research interests include modeling and soot blowing of boiler, and prediction of remaining useful life

    WEN Jie   received the B.E. and Ph.D. degrees from the University of Science and Technology of China. He is a lecturer with the School of Electrical and Control Engineering, NUC, China. His research interests include optimization of boiler soot blowing, intelligent control, and deep learning

    ZENG Jianchao   received the B.E. degree from Taiyuan Heavy Machinery Institute, and M.E. and Ph.D. degrees from Xi'an Jiaotong University, China. He is a Professor with the School of Data Science and Technology, NUC, China. His research interests include modeling and control of complex systems, intelligent computation, swarm intelligence, and swarm robotics. He is the Vice President of NUC and the Technical Committee on System Simulation of the Chinese Association of Automation and the Director of the China Simulation Federation

  • Corresponding author: JIA Jianfang  (corresponding author) received the B.E. and M.E. degrees from North University of China (NUC), China, and the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences, China. He is an associate professor with the School of Electrical and Control Engineering, NUC. His research interests include prognostic and health management, intelligent control of wind power generation systems, and modeling and optimization of complex systems. (Email: jiajianfang@nuc.edu.cn)
  • Received Date: 2019-11-29
  • Accepted Date: 2020-07-15
  • Publish Date: 2021-01-01
  • The prediction of Remaining useful life (RUL) and the estimation of State of health (SOH) are extremely important issues for operating performance of Lithium-ion (Li-ion) batteries in the Battery management system (BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network (WNN) with Unscented particle filter (UPF) model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform (DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries' lifespan.
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