A Pattern-Driven Stochastic Degradation Model for the Prediction of Remaining Useful Life of Rechargeable Batteries

Abstract

Recently, there has been a significant growth in the development of rechargeable battery-powered devices such as electric vehicles, leading to an urgent need for reliable and safe batteries. The remaining useful life (RUL) is a critical health indicator of battery, which is defined as the remaining number of charge and recharge cycles before the state-of-health falls below a user-specified threshold under certain operating settings. Substantially, the RUL can be estimated by adaptive stochastic processes or advanced machine learning techniques. However, the existing approaches either assume over-simplified degradation pattern in accordance with physics laws leading to poor generalizability or act as a black box offering no interpretation. To address these limitations, in this article, we develop a pattern-driven degradation process by integrating a recursive Gaussian distribution with its mean learnt from a gated recurrent unit (GRU) driven degradation pattern to capture degradation fluctuation into the model. Due to the non-Markovian state transitions, a joint-learning sampling-based expectation maximization algorithm was developed to estimate model parameters based on historical observations. Finally, numerical studies using real battery data showed that the proposed method achieves over 3% and 40% higher accuracy in RUL prediction than the GRU and adaptive Wiener process, respectively.