Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep learning model
Lisheng Zhang,
Wentao Wang,
Hanqing Yu,
Zheng Zhang,
Xianbin Yang,
Fengwei Liang,
Shen Li,
Shichun Yang,
Xinhua Liu
Affiliations
Lisheng Zhang
School of Transportation Science and Engineering, Beihang University, Beijing 102206, China
Wentao Wang
School of Transportation Science and Engineering, Beihang University, Beijing 102206, China
Hanqing Yu
School of Transportation Science and Engineering, Beihang University, Beijing 102206, China
Zheng Zhang
Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR 999077, China; Corresponding author
Xianbin Yang
College of Automotive Engineering, Jilin University, Changchun 130022, China
Fengwei Liang
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China
Shen Li
Department of Mechanical Engineering, Imperial College London, London SW7 2AZ, UK
Shichun Yang
School of Transportation Science and Engineering, Beihang University, Beijing 102206, China; Corresponding author
Xinhua Liu
School of Transportation Science and Engineering, Beihang University, Beijing 102206, China; Corresponding author
Summary: The accurate estimation of battery health conditions is a crucial challenge for development of battery management systems due to the degradation of cathode and anode materials. In this paper, a fusion of deep learning model and feature analysis methods is employed to approach accurate estimation for state of health (SOH) and remaining useful life (RUL). The differential thermal voltammetry (DTV) signal analysis is executed to pre-process the datasets from Oxford University. A deep learning model is constructed with LSTM network as the core, combined with Bayesian optimization and dropout technique. This work shows that the deep learning model could approach the SOH and RUL early estimation with the mean absolute error of RUL maintained around 0.5%. It is potential that this deep learning model, combined with DTV signal analysis methods, could approach early prediction and estimation of battery SOH and RUL, contributing to the development of the next-generation high-energy-density and highly safety commercial batteries.
