Prediction method for full lifetime of vehicle power battery

Abstract:

A full lifetime capacity prediction method for vehicle power batteries was proposed, so as to accurately quantify the aging degree of automotive power batteries, improve the utilization rate of batteries, and achieve accurate prediction of the remaining useful life (RUL) in the whole life cycle of batteries. By integrating the traditional empirical exponential model and the improved polynomial regression model, the proposed method could track the degradation trend of battery life cycle based on the analysis of experimental data. The particle filter (PF) was adopted to adjust the model parameters online. Experiments were carried out to predict the RUL of power batteries with different states and capacities. The model was evaluated by comparing the prediction accuracy of different models. Experimental results show that the proposed model had a stronger ability in battery capacity attenuation tracking than that of the traditional empirical exponential model and the improved polynomial regression model. Combined with particle filter algorithm, the method achieved high-precision prediction results for both in-service and retired batteries. Besides, the method could accurately predict the failure time of power batteries with different capacities, which has a wide applicability in battery cascade utilization.