Stochastic Modeling For Tracking And Prediction Of Gradual And Transient Battery Performance Degradation

As rechargeable battery-powered devices become a pervasive part of daily life, consumer standards for quality and reliability in these devices increase. One standard of particular interest is the ability to reliably track and predict the daily and lifetime capacity degradation of the batteries, over successive charge-discharge cycles. Tracking and predicting the degradation trend over batteries' entire service lives can be challenging, not only due to the nonlinear degradation patterns subject to different usage conditions, but also because of the presence of transient regeneration events that can rapidly increase the battery's maximum capacity and affect degradation rates. Current methods in the literature commonly apply an exponential model in a Bayesian inference framework to track and predict global degradation trend, neglecting the transient behaviors and local fluctuations, hence leading to unsatisfactory performance. This paper presents a new customary model specifically designed to track the gradual battery degradation pattern, combined with a compound Poisson process-based model that aims to capture the transient behaviors. The integration of the two parts, forming a comprehensive degradation model, provides a more accurate description of capacity variation throughout battery's life cycle. During the model training phase, upon historical data, parameters involved in the two models are estimated through two Bayesian inference techniques: step-by-step estimation by a Particle Filter and batch estimation by a Markov Chain Monte Carlo algorithm, with their performance compared. The estimated parameters are then utilized for generating transient events and predicting future capacity degradation. NASA's lithium-ion battery data are analyzed to evaluate the effectiveness of the developed degradation model.