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Batteries 2017, 3(3), 21; doi:10.3390/batteries3030021

On-Line Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Optimized Gray Model GM(1,1)

1
State Key laboratory of Virtual Reality Technology and System, Beijing 100191, China
2
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Academic Editor: James Marco
Received: 7 April 2017 / Revised: 30 May 2017 / Accepted: 26 June 2017 / Published: 8 July 2017
(This article belongs to the Special Issue Battery Management Systems)
View Full-Text   |   Download PDF [1967 KB, uploaded 8 July 2017]   |  

Abstract

Lithium-ion battery on-line remaining useful life (RUL) prediction has become increasingly popular. The capacity and internal resistance are often used as the batteries’ health indicator (HI) for quantifying degradation and predicting the RUL. However, the capacity and internal resistance are too difficult to measure on-line due to the batteries’ internal state variables being inaccessible to sensors under operational conditions. In addition, measuring these variables requires accurate measurement devices, which can be expensive, and have limited applicability in practice. In this paper, a novel HI is extracted from the operating parameters of lithium-ion batteries for degradation models and RUL prediction. Moreover, the Box–Cox transformation is applied to improve the correlation between the extracted HI and the battery’s real capacity. Then, Pearson and Spearman correlation analyses are utilized to assess the similarity between the real capacity and the estimated capacity derived from the HI. An optimized gray model GM(1,1) is employed to predict the RUL based on the presented HI. The experimental results show that the proposed method is effective and accurate for battery degradation modeling and RUL prediction. View Full-Text
Keywords: lithium-ion battery; on-line; health indicator; remaining useful life prediction; optimized gray model GM(1,1) lithium-ion battery; on-line; health indicator; remaining useful life prediction; optimized gray model GM(1,1)
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Zhou, D.; Xue, L.; Song, Y.; Chen, J. On-Line Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Optimized Gray Model GM(1,1). Batteries 2017, 3, 21.

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