Adaptive Remaining Useful Life Prediction Model for Lithium-Ion Batteries Based on Polynomial Fitting

The accurate remaining useful life (RUL) prediction results are of great significance for ensuring the smooth operation of lithium-ion batteries (LIBs). Due to the advantages of solid flexibility, high efficiency, and the uncertain description of prediction results, the Wiener process has been widely applied in RUL prediction for LIBs. However, selecting the appropriate type of degradation trend function for different LIBs is a considerable challenge. In addition, the existing Wiener process models with fixed type of degradation trend functions are difficult to accurately adapt to the dynamic nonlinear degradation characteristics of different LIBs. To solve these problems, this article proposes a polynomial fitting-based Wiener process model to fit the complex and time-varying degradation features of LIBs adaptively. The model parameters are estimated online using Bayesian algorithm, and the explicit probability density function of RUL prediction is derived. The effectiveness and feasibility of the proposed model for predicting RUL of LIBs in real-world engineering have been demonstrated through comparative experiments based on a simulated dataset and two practical capacity datasets.