Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

Abstract

Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

“…Based on this two step MLE method, we first give the log-likelihood function of λ 1 , λ 2 , ..., λ n , σ 2 B as follows [16] ln…”

“…Following Gebraeel et al [11], some related issues and many variants and applications have been studied and reported [12]- [14]. Also, this strategy under Bayesian framework has been applied in Wiener processes based degradation model, which is a very popular model for modeling the degradation modeling and has been widely used to model the degradation process in systems [15], such as lithium-ion batteries [16], [17], LCDs (Liquid crystal display) [18], gyroscopes [19], bearings [20], etc. Si et al [21] did a lot of interesting works in this research area for the degradation process with three sources of variance, e.g.…”

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

“…Because of the conciseness and feasibility, the condition monitoring (CM) data–based methods are quite popular in RUL prediction. Specifically, without any demand of expert knowledge or extra experiments, data‐driven methods have occupied a significant place in the existing literature, such as the utilization of Brownian motion (BM, also refers to the Wiener process) and fractional Brownian motion (FBM) . These stochastic models possess remarkable similarities that their described degradation processes should follow the specified distribution forms, and hence would facilitate the identification of unknown parameters.…”

“…Wang et al made an adaptive improvement of the Wiener process under the assumption that the degradation rate and variation satisfy a proportional relationship. Combining the regenerated useful time model and the linear Wiener process, Xu et al interpreted the degradation characteristic of lithium‐ion batteries with the relaxation effect.…”

A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries

“…It can simulate the implicit relationship between the observations and the objective quantities by extracting valid information from the available data. Data-driven methods contain Wiener Process (WP) [16], neural network (NN) [17][18][19][20], support vector machine (SVM) [21], relevance vector machine (RVM) [22], machine learning (ML) [23], deep learning (DL) [24], autoregressive sliding model (AR) [25], and the Gaussian Process regression (GPR) [26]. For example, in [16], first, the authors introduce the Reproductive Useful Time (RUT).…”