State of Health Estimation of Lithium-ion Battery Based on Early-stage Constant-voltage charging

Zhongbao Wei; Haokai Ruan; Xiaolei Bian; Hongwen He

doi:10.46855/energy-proceedings-7124

State of Health Estimation of Lithium-ion Battery Based on Early-stage Constant-voltage charging

Zhongbao Wei, Haokai Ruan, Xiaolei Bian, Hongwen He^*

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

State of health (SOH) estimation is insightful for the lithium-ion battery (LIB) health management. This paper proposes a new set of health indicators (HIs) based on early-stage constant-voltage (CV) charging, which are easily available in practical vehicle applications. Particularly, a thorough analysis is performed over different CV-based HIs to obtain the informative ones with strong correlation against the SOH. A gaussian process regression (GPR) model is further employed to fusion the extracted HIs and to estimate the battery SOH. The proposed method is validated based on cycling experiments performed on the LiNiCoAlO₂ cells. Results suggest that the proposed method promises multifold benefits, including the high estimation accuracy, low requirement on the charging integrity, and the high robustness to cell inconsistency.

Original language	English
Journal	Energy Proceedings
Volume	9
DOIs	https://doi.org/10.46855/energy-proceedings-7124
Publication status	Published - 2020
Event	12th International Conference on Applied Energy, ICAE 2020 - Bangkok, Thailand Duration: 1 Dec 2020 → 10 Dec 2020

Keywords

constant voltage charging
gaussian process regression
health indicators
lithium-ion battery
state of health

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.46855/energy-proceedings-7124

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title = "State of Health Estimation of Lithium-ion Battery Based on Early-stage Constant-voltage charging",

abstract = "State of health (SOH) estimation is insightful for the lithium-ion battery (LIB) health management. This paper proposes a new set of health indicators (HIs) based on early-stage constant-voltage (CV) charging, which are easily available in practical vehicle applications. Particularly, a thorough analysis is performed over different CV-based HIs to obtain the informative ones with strong correlation against the SOH. A gaussian process regression (GPR) model is further employed to fusion the extracted HIs and to estimate the battery SOH. The proposed method is validated based on cycling experiments performed on the LiNiCoAlO2 cells. Results suggest that the proposed method promises multifold benefits, including the high estimation accuracy, low requirement on the charging integrity, and the high robustness to cell inconsistency.",

keywords = "constant voltage charging, gaussian process regression, health indicators, lithium-ion battery, state of health",

author = "Zhongbao Wei and Haokai Ruan and Xiaolei Bian and Hongwen He",

note = "Publisher Copyright: {\textcopyright} 2020 ICAE.; 12th International Conference on Applied Energy, ICAE 2020 ; Conference date: 01-12-2020 Through 10-12-2020",

year = "2020",

doi = "10.46855/energy-proceedings-7124",

language = "English",

volume = "9",

journal = "Energy Proceedings",

issn = "2004-2965",

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}

TY - JOUR

T1 - State of Health Estimation of Lithium-ion Battery Based on Early-stage Constant-voltage charging

AU - Wei, Zhongbao

AU - Ruan, Haokai

AU - Bian, Xiaolei

AU - He, Hongwen

PY - 2020

Y1 - 2020

N2 - State of health (SOH) estimation is insightful for the lithium-ion battery (LIB) health management. This paper proposes a new set of health indicators (HIs) based on early-stage constant-voltage (CV) charging, which are easily available in practical vehicle applications. Particularly, a thorough analysis is performed over different CV-based HIs to obtain the informative ones with strong correlation against the SOH. A gaussian process regression (GPR) model is further employed to fusion the extracted HIs and to estimate the battery SOH. The proposed method is validated based on cycling experiments performed on the LiNiCoAlO2 cells. Results suggest that the proposed method promises multifold benefits, including the high estimation accuracy, low requirement on the charging integrity, and the high robustness to cell inconsistency.

AB - State of health (SOH) estimation is insightful for the lithium-ion battery (LIB) health management. This paper proposes a new set of health indicators (HIs) based on early-stage constant-voltage (CV) charging, which are easily available in practical vehicle applications. Particularly, a thorough analysis is performed over different CV-based HIs to obtain the informative ones with strong correlation against the SOH. A gaussian process regression (GPR) model is further employed to fusion the extracted HIs and to estimate the battery SOH. The proposed method is validated based on cycling experiments performed on the LiNiCoAlO2 cells. Results suggest that the proposed method promises multifold benefits, including the high estimation accuracy, low requirement on the charging integrity, and the high robustness to cell inconsistency.

KW - constant voltage charging

KW - gaussian process regression

KW - health indicators

KW - lithium-ion battery

KW - state of health

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U2 - 10.46855/energy-proceedings-7124

DO - 10.46855/energy-proceedings-7124

M3 - Conference article

AN - SCOPUS:85202624160

SN - 2004-2965

VL - 9

JO - Energy Proceedings

JF - Energy Proceedings

T2 - 12th International Conference on Applied Energy, ICAE 2020

Y2 - 1 December 2020 through 10 December 2020

ER -

State of Health Estimation of Lithium-ion Battery Based on Early-stage Constant-voltage charging

Abstract

Keywords

UN SDGs

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