Remaining Useful Life Prediction for Lithium-Ion Batteries Based on the Partial Voltage and Temperature

Yanru Yang, Jie Wen (), Jianyu Liang, Yuanhao Shi, Yukai Tian and Jiang Wang
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Yanru Yang: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Jie Wen: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Jianyu Liang: School of Data Science and Technology, North University of China, Taiyuan 030051, China
Yuanhao Shi: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Yukai Tian: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China
Jiang Wang: School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China

Sustainability, 2023, vol. 15, issue 2, 1-21

Abstract: Remaining useful life (RUL) prediction is vital to provide accurate decision support for a safe power system. In order to solve capacity measurement difficulties and provide a precise and credible RUL prediction for lithium-ion batteries, two health indicators (HIs), the discharging voltage difference of an equal time interval (DVDETI) and the discharging temperature difference of an equal time interval (DTDETI), are extracted from the partial discharging voltage and temperature. Box-Cox transformation, which is data processing, is used to improve the relation grade of HIs. In addition, the Pearson correlation is employed to evaluate the relationship degree between HIs and capacity. On this basis, a local Gaussian function and a global sigmoid function are utilized to improve the multi-kernel relevance vector machine (MKRVM), whose weights are optimized by applying a whale optimization algorithm (WOA). The availability of the extracted HIs as well as the accuracy of the RUL prediction are verified with the battery data from NASA.

Keywords: lithium-ion battery; remaining useful life; state of health; voltage; temperature (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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