Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

Xing Shu; Shiquan Shen; Jiangwei Shen; Yuanjian Zhang; Guang Li; Zheng Chen; YongGang Liu

doi:10.1016/j.xpro.2022.101272

Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

STAR Protoc. 2022 Apr 4;3(2):101272. doi: 10.1016/j.xpro.2022.101272. eCollection 2022 Jun 17.

Authors

Xing Shu¹, Shiquan Shen¹, Jiangwei Shen¹, Yuanjian Zhang², Guang Li³, Zheng Chen¹, YongGang Liu⁴

Affiliations

¹ Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China.
² Department of Aeronautical and Automotive Engineering, Loughborough University, LE11 3TU Leicestershire, UK.
³ School of Engineering and Materials Science, Queen Mary University of London, E1 4NS London, UK.
⁴ State Key Laboratory of Mechanical Transmissions & College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.

Abstract

Accurate estimates of State of Health (SoH) are critical for characterizing the aging of lithium-ion batteries. This protocol combines feature extraction and a representative machine learning algorithm (i.e., least-squares support vector machine) for SoH prediction of lithium-ion batteries. We detail the step-by-step estimation process, followed by validation of the constructed model with a maximum absolute error of 1.62%. Overall, the proposed approach can efficiently track the aging trajectory and ensure precise SoH prediction. For complete details on the use and execution of this protocol, please refer to Shu et al. (2021b).

Keywords: Computer sciences; Energy; Material sciences; Physics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Electric Power Supplies*
Ions
Lithium*
Machine Learning
Support Vector Machine

Substances

Ions
Lithium