Remaining Useful Life Prediction of Battery Using a Novel Indicator and Framework With Fractional Grey Model and Unscented Particle Filter

• Published in: IEEE transactions on power electronics Vol. 35; no. 6; pp. 5850 - 5859
• Main Authors: Chen, Lin, Chen, Jing, Wang, Huimin, Wang, Yijue, An, Jingjing, Yang, Rong, Pan, Haihong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Aging; Algorithms; Batteries; Control algorithms; Degradation; Discharge voltage; Discharges (electric); Electric potential; Electric vehicles; fractional grey model (FGM); Integrated circuit modeling; Life prediction; Lithium; Lithium-ion batteries; Management systems; Power management; Power supplies; Predictive models; Rechargeable batteries; remaining useful life (RUL); unscented particle filter (UPF); Useful life; Voltage; wavelet packet energy entropy (WPEE)
• ISSN: 0885-8993; 1941-0107
• DOI: 10.1109/TPEL.2019.2952620

The lithium-ion battery plays a crucial role in the power supply of the electric vehicles (EVs). Battery remaining useful life (RUL) is critically vital to ensure the vehicles' safety and reliability. Due to the complicated aging mechanism, predicting RUL for the battery management systems (BMSs) is challenging. In this article, a novel degradation indicator was constructed using the information extracted from the discharge voltage. The indicator reflected the complete and effective energy information from the voltage signals to reveal battery degradation characteristics. Additionally, an innovative fractional grey model (FRGM) unscented particle filter (UPF) framework was developed for RUL prediction in this article. To improve the accuracy and traceability of prediction, the framework adopted a novel FRGM to update the state transition equation in UPF. Meanwhile, the UPF was employed to extrapolate trends of the indicator and achieve the RUL prediction. The performances of FRGM-UPF with the degradation indicator were synthetically verified by the data from various types of batteries under different aging tests. The experimental results indicated that the proposed method could achieve precise prediction results and had a wide range of practicability and universality. The developed technologies could be incorporated with the other control algorithms for application in BMS of EVs.