New Short-Long-Term Degradation Model for Precise Battery Health Prognostics

作者：Kong, Jin-Zhen; Cui, Di; Hou, Bingchang; Liu, Xingchen; Wang, Dong^*

来源：IEEE Transactions on Industrial Electronics, 2023, 70(9): 9527-9537.

DOI：10.1109/TIE.2022.3212365

• Degradation
• Batteries
• Adaptation models
• Uncertainty
• Stochastic processes
• Mathematical models
• Integrated circuit modeling
• Degradation modeling
• lithium-ion battery
• prognostics and health management

Battery health prognostic plays a crucial role in the safe operation of electrical devices. Battery degradation modeling is a crucial step for battery health prognostics. Currently, since most existing battery prognostic models only consider entire long-term degradation trends during modeling, battery degradation modeling is still not precise and lacks short-term deviations to sufficiently reflect strong complexity and volatility of degradation processes. To solve this problem, a new battery prognostic method is proposed by simultaneously considering long-term degradation tendency and short-term volatility. Because Wiener process and Ornstein-Uhlenbeck process have ability to respectively model long-term degradation tendency and short-term volatility, a short-long-term degradation model is accordingly proposed by linearly fusing two stochastic processes. Additionally, the proposed model can characterize modeling uncertainty and observation uncertainty. Besides, this article proves that the proposed model follows a log-normal distribution, which provides solid foundations for state-space modeling. Here, the purpose of formulating the proposed model to a state-space model with multiple cross-correlation hidden state variables is to better describe individual battery degradation using online battery capacity data. Health prognostics can be easily realized by extrapolating the updated model to a failure threshold. Results show that our proposed method performs well than contrast methods for battery health prognostics.

• 单位
  上海交通大学