iEnergy 2023, 2(4): 308-313 https://doi.org/10.23919/IEN.2023.0034

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Open Access | Issue | Published: 10 November 2023

Estimating battery state of health with 10-min relaxation voltage across various charging states of charge

Show Author's Information Hide Author's Information Xinhong Feng^¹, Yongzhi Zhang^¹(

), Rui Xiong^²(

), Aihua Tang^³

1College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400030, China

2Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

3School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400054, China

Keywords:

machine learning, Battery state of health, 10-min relaxation voltage, varying charging states, physical features

Cite this article:

Feng X, Zhang Y, Xiong R, et al. Estimating battery state of health with 10-min relaxation voltage across various charging states of charge. iEnergy, 2023, 2(4): 308-313. https://doi.org/10.23919/IEN.2023.0034

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Abstract

Battery capacity assessment is a crucial research direction in the field of lithium-ion battery applications. In the previous research, a novel data-driven state of health (SOH) estimation method based on the voltage relaxation curve at full charging is developed. The experimental results have shown the evidence of the superiority of accurate battery SOH estimation based on physical features derived from equivalent circuit models (ECMs). However, the earlier research has limitations in estimating battery capacity with a diversity of battery charging states of charge. This study represents an extension of the previous work, aiming to investigate the feasibility of this technology for battery degradation evaluation under various charging states so that the application capability in practice is enhanced. In this study, six ECM features are extracted from 10-min voltage relaxation data across varying charging states to characterize the battery degradation evolution. Gaussian process regression (GPR) is employed to learn the relationship between the physical features and battery SOH. Experimental results under 10 different state of charge (SOC) ranges show that the developed methodology predicts accurate battery SOH, with a root mean square error being 0.9%.

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About this article

Estimating battery state of health with 10-min relaxation voltage across various charging states of charge

Show Author's information Hide Author's Information Xinhong Feng^¹, Yongzhi Zhang^¹(

), Rui Xiong^²(

), Aihua Tang^³

1College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400030, China

2Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

3School of Vehicle Engineering, Chongqing University of Technology, Chongqing 400054, China

Abstract

Keywords: machine learning, Battery state of health, 10-min relaxation voltage, varying charging states, physical features

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Acknowledgements

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Publication history

Received: 10 July 2023

Revised: 23 September 2023

Accepted: 29 October 2023

Published: 10 November 2023

Issue date: December 2023

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 52307234) and Beijing Natural Science Foundation (Grant No. L223013). The systemic experiments of the lithium-ion batteries were performed at the Joint Lab for Advanced Energy Storage and Applications, Beijing Institute of Technology.

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This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).