Lithium-ion battery remaining useful life prediction based on sequential Bayesian updating

When Bayesian method updates the model parameters offline, the historical degradation data isn't well applied for parameter estimation. For this problem, a new method based on sequential Bayesian was proposed to update parameters online. A nonlinear Wiener process degradation model was constructed for the lithium-ion battery capacity degradation path under variable working conditions, and the Maximum Likelihood Estimation (MLE) was used to estimate the model parameters at the initial time. Followed by it, the drift coefficients in the degradation model were updated online based on the sequence Bayesian updating method. Then, the probability density function of the lithium-ion battery's Remaining Useful Life (RUL) was derived for prediction. The proposed model was applied and demonstrated by the lithium-ion battery dataset in various conditions. Moreover, the results showed the prediction accuracy of RUL based on the proposed model was higher than those obtained from the degradation models based on the power exponent or linear function for the sequential Bayesian method realized the real-time update of parameter estimation. © 2024 CIMS. All rights reserved.