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

被引：5

作者：

Yang, Yanru ^{[1
,2
]}

Wen, Jie ^{[1
]}

Liang, Jianyu ^{[3
]}

Shi, Yuanhao ^{[1
]}

Tian, Yukai ^{[1
]}

Wang, Jiang ^{[1
]}

机构：

[1] North Univ China, Sch Elect & Control Engn, Taiyuan 030051, Peoples R China

[2] Tongji Univ, Shanghai Res Inst Intelligent Autonomous Syst, Shanghai 200092, Peoples R China

[3] North Univ China, Sch Data Sci & Technol, Taiyuan 030051, Peoples R China

来源：

SUSTAINABILITY | 2023年 / 15卷 / 02期

基金：

中国国家自然科学基金;

关键词：

lithium-ion battery; remaining useful life; state of health; voltage; temperature; INTELLIGENT PROGNOSTICS; VECTOR REGRESSION; HEALTH; DEGRADATION; FRAMEWORK; CAPACITY; MODEL; STATE;

D O I：

10.3390/su15021602

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页数：21

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