A data-driven method for extracting aging features to accurately predict the battery health

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Xiong, R.Joint Laboratory for Advanced Energy Storage and Application, School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China (author)

Data-driven methods have been widely used for estimating the state of health (SOH) of lithium-ion batteries (LiBs). The aging process can be characterized by degrading features. To achieve high accuracy, a novel method combining four algorithms, i.e. the correlation coefficient, least absolute shrinkage and selection operator regression, neighborhood component analysis, and ReliefF algorithm, is proposed to select the most important features, which are derived from the measured and calculated parameters. To demonstrate the effectiveness of the proposed method, it is adopted to estimate the SOH of two types of LiBs: i.e. NCA and LFP batteries. Compared to the case using all features, using the selected features can improve the accuracy of SOH estimation by 63.5% and 71.1% for the NCA and LFP batteries, respectively. The method can also enable the use of data obtained in partial voltage ranges, based on which the minimum root mean square errors on SOH estimation are 1.2% and 1.6% for the studied NCA and LFP batteries, respectively. It demonstrates the capability for onboard applications.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering hsv//eng
Battery degradation
Feature selection
Lithium-ion battery
Machine learning
State of health
Battery management systems
Mean square error
Ageing features
Ageing process
Battery health
Data-driven methods
Features selection
High-accuracy
Machine-learning
Novel methods
Lithium-ion batteries

Sun, Y.Joint Laboratory for Advanced Energy Storage and Application, School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China (author)
Wang, C.Joint Laboratory for Advanced Energy Storage and Application, School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China (author)
Tian, J.Joint Laboratory for Advanced Energy Storage and Application, School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China (author)
Chen, X.Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China (author)
Li, Hailong,1976-Mälardalens universitet,Framtidens energi(Swepub:mdh)hli01 (author)
Zhang, Q.Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China (author)
Joint Laboratory for Advanced Energy Storage and Application, School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, ChinaBeijing Key Laboratory of Green Chemical Reaction Engineering and Technology, Department of Chemical Engineering, Tsinghua University, Beijing, 100084, China (creator_code:org_t)

By the author/editor: Xiong, R.; Sun, Y.; Wang, C.; Tian, J.; Chen, X.; Li, Hailong, 197 ...; show more...; Zhang, Q.; show less...

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