A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve

Xingzi Qiang; Wenting Liu; Zhiqiang Lyu; Haijun Ruan; Xiaoyu Li

doi:10.1016/j.geits.2024.100169

A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve

Xingzi Qiang, Wenting Liu, Zhiqiang Lyu, Haijun Ruan, Xiaoyu Li

Centre for E-Mobility and Clean Growth

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

36 Downloads (Pure)

Abstract

The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.

Original language	English
Article number	100169
Number of pages	12
Journal	Green Energy and Intelligent Transportation
Volume	3
Issue number	5
Early online date	13 Jan 2024
DOIs	https://doi.org/10.1016/j.geits.2024.100169
Publication status	Published - Oct 2024

Bibliographical note

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

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 62303007), Doctoral Research Start-up Funding (Grant No. S020318015/028) and China Postdoctoral Science Foundation (No. 2023M741452).

Funders	Funder number
National Natural Science Foundation of China	62303007
Doctoral Research Start-up Funding	S020318015/028
China Postdoctoral Science Foundation	2023M741452

Keywords

Li-ion battery pack
State of health
Data-fusion-model method
Particle filter
Gaussian process regression
Support vector regression

ASJC Scopus subject areas

Engineering (miscellaneous)

Access to Document

10.1016/j.geits.2024.100169Licence: CC BY-NC-ND

Ruan2024VoRFinal published version, 3.46 MBLicence: CC BY-NC-ND

Cite this

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title = "A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve",

abstract = "The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5\%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.",

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author = "Xingzi Qiang and Wenting Liu and Zhiqiang Lyu and Haijun Ruan and Xiaoyu Li",

note = "This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).",

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AU - Qiang, Xingzi

AU - Liu, Wenting

AU - Lyu, Zhiqiang

AU - Ruan, Haijun

AU - Li, Xiaoyu

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PY - 2024/10

Y1 - 2024/10

N2 - The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.

AB - The estimation of State of Health (SOH) for battery packs used in Electric Vehicles (EVs) is a complex task with significant importance, accompanied by several challenges. This study introduces a data-fusion model approach to estimate the SOH of battery packs. The approach utilizes dual Gaussian Process Regressions (GPRs) to construct a data-driven and non-parametric aging model based on charging-based Aging Features (AFs). To enhance the accuracy of the aging model, a noise model is established to replace the random noise. Subsequently, the state-space representation of the aging model is incorporated. Additionally, the Particle Filter (PF) is introduced to track the unknown state in the aging model, thereby developing the data-fusion-model for SOH estimation. The performance of the proposed method is validated through aging experiments conducted on battery packs. The simulation results demonstrate that the data-fusion model approach achieves accurate SOH estimation, with maximum errors less than 1.5%. Compared to conventional techniques such as GPR and Support Vector Regression (SVR), the proposed method exhibits higher estimation accuracy and robustness.

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KW - Support vector regression

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A data-fusion-model method for state of health estimation of Li-ion battery packs based on partial charging curve

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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