Remaining useful life prediction for lithium-ion batteries based on an integrated health indicator

• Published in: Microelectronics and reliability Vol. 88-90; pp. 1189 - 1194
• Main Authors: Sun, Yongquan, Hao, Xueling, Pecht, Michael, Zhou, Yapeng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2018
• Subjects: Integrated health indicator; Lithium-ion battery; Particle filter; Remaining useful life; State of health; Lithium-ion battery; Remaining useful life; Integrated health indicator; State of health; Particle filter
• ISSN: 0026-2714; 1872-941X
• DOI: 10.1016/j.microrel.2018.07.047

State of health estimation and remaining useful life prediction of lithium-ion batteries is challenging due to various health indicators characterizing battery degradation. This paper develops an integrated health indicator to predict remaining useful life by incorporating capacitance, resistance, and constant current charge time with the help of a beta distribution function, based on the correlation analysis between parameter variations and aging mechanisms. A three-order polynomial model is employed to fit the battery health degradation process, remaining useful life is predicted using a particle filter algorithm, and the probability density function for the battery remaining useful life is then provided. A case study is conducted to validate the health degradation model and battery remaining useful life prediction. The results show that the constant voltage charge time is not a good health indicator, and a threshold of 0.85 is recommended as the end-of-life criterion based on the integrated health indicator. The developed method provides a reference for battery remaining useful life prediction when sufficient energy and power are required. •An integrated health indicator is developed using capacity, resistance, and CCCT.•The constant voltage charge time is not a good health indicator.•A threshold of 0.85 is recommended as the end-of-life criterion.•Battery remaining useful life is predicted using a particle filter algorithm.