A Data-Driven Prediction Method for Lithium-ion Batteries based on Variational Bayes and Particle Filtering

The remaining useful life (RUL) prediction and state of health (SOH) assessment of lithium-ion batteries are the key technologies for achieving equipment prediction and health management (PHM). Accurate RUL prediction results can serve as evaluation indicators for battery management systems(BMS), ensuring their safe and stable operation. This paper proposes a model parameter estimation method based on particle filtering (PF) and variational Bayesian (VB) to address the issues of high degradation noise and small data sample size in lithium-ion batteries, which lead to large prediction model errors and poor real-time performance. Considering the impact of uncertainty in noise, this paper uses a hybrid model to describe the data degradation process of lithium-ion batteries at different time intervals and probabilistically describes the measurement noise and model parameters. On this basis, the variational Bayes method is used to perform posterior estimation on each parameter, and the particle filter optimization method is used to dynamically update the values of the model parameters. This paper conducts experimental verification using publicly available datasets from NASA. The results indicate that this method can achieve good predictive performance.