Battery state of health modeling and remaining useful life prediction through time series model. (1st October 2020) - Lin, Chun-Pang; Cabrera, Javier; Yang, Fangfang; Ling, Man-Ho; Tsui, Kwok-Leung; Bae, Suk-Joo
Highlights: A model considering stochasticity instead of deterministic degradation shape. Predict degradation by constructing segments iteratively with autoregressive nature. Produce remaining useful life confidence intervals from bootstrap distribution. The proposed model is applicable to two different lithium-ion batteries. Comparison shows the proposed model can predict better when plunge is not present. Abstract: While most existing degradation modeling methods for rechargeable batteries consider a deterministic degradation model such as exponential model, this paper presents a time series model for battery degradation paths resembling experimental data on cycle aging. This model is based on breaking down the degradation path into segments by fitting a multiple-change-point linear model, which accounts for the degradation structure by regressing the segment lengths and the slope changes. These two variables are modeled by two sub-models: an autoregressive model with covariates for the slope changes at the change points and a survival regression model for the segment lengths that allows for censored data caused by interruptions during battery cycling. The combined model is able to predict a full battery degradation path based on historical paths, and predict the remaining degradation path even based merely on the partial path. The proposed model can also be used to produce confidence intervals for battery's useful life by applying the method of parametric bootstrap to generate the empirical bootstrap distribution. The application of the proposed model is demonstrated with data from lithium iron phosphate and lithium nickel manganese cobalt oxide batteries. The comparison on prediction mean between proposed model, deterministic models with particle filter and recurrent neural network shows that the proposed model can make better prediction when capacity plunge is not present. The validation with simulations shows that the proposed model is reliable when complete historical paths are available as the simulation coverage rates are close to the nominal coverage rate 90%.