An ensemble model for predicting the remaining useful performance of lithium-ion batteries

We developed an ensemble model to characterize the capacity degradation and predict the remaining useful performance (RUP) of lithium-ion batteries. Our model fuses an empirical exponential and a polynomial regression model to track the battery’s degradation trend over its cycle life based on experimental data analysis. Model parameters are adjusted online using a particle filtering (PF) approach. Experiments were conducted to compare our ensemble model’s prediction performance with the individual results of the exponential and polynomial models. A validation set of experimental battery capacity data was used to evaluate our model. In our conclusion, we presented the limitations of our model.

► The capacity degradation of lithium battery was characterized by an ensemble model. ► The remaining useful performance was presented as probability distribution. ► The robustness of the algorithm was verified by different datasets. ► More measured data, more accurate prediction results.