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

• Published in: Microelectronics and reliability Vol. 53; no. 6; pp. 811 - 820
• Main Authors: Xing, Yinjiao, Ma, Eden W.M., Tsui, Kwok-Leung, Pecht, Michael
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2013
• Subjects: Battery; Degradation; Filtering; Fuses; Lithium-ion batteries; Mathematical models; On-line systems; Regression
• ISSN: 0026-2714
• DOI: 10.1016/j.microrel.2012.12.003

► 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. 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.