State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging

Haokai Ruan; Zhongbao Wei; Hongwen He

doi:10.1109/ECCE-Asia49820.2021.9479412

State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging

Haokai Ruan
, Zhongbao Wei
, Hongwen He

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

State of health (SOH) estimation has deep insights into the lithium-ion battery (LIB) life diagnostic and protection. A machine learning-based SOH estimator is established, utilizing a new set of health indicators (His) extracted from the regional constant-voltage (CV) charging. First, a thorough analysis is performed over different CV-based His to obtain the informative ones with strong correlation against the SOH. Second, an artificial neural network model is employed to construct the nonlinear mapping from the selected His to the battery SOH. The proposed SOH estimator is validated with long-term degradation experiments performed on LiNiCoAlO2 (NCA) cells. Results imply the proposed method manifests itself with high estimation accuracy, low charging integrity requirements, and a high robustness to cell inconsistency.

Original language	English
Title of host publication	Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	950-952
Number of pages	3
ISBN (Electronic)	9781728163444
DOIs	https://doi.org/10.1109/ECCE-Asia49820.2021.9479412
Publication status	Published - 24 May 2021
Event	12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 - Virtual, Singapore, Singapore Duration: 24 May 2021 → 27 May 2021

Publication series

Name	Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

Conference

Conference	12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021
Country/Territory	Singapore
City	Virtual, Singapore
Period	24/05/21 → 27/05/21

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

health indicators
lithium-ion battery
neural network
state of health

Access to Document

10.1109/ECCE-Asia49820.2021.9479412

Cite this

Ruan, H., Wei, Z., & He, H. (2021). State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging. In Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 (pp. 950-952). Article 9479412 (Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE-Asia49820.2021.9479412

Ruan, Haokai ; Wei, Zhongbao ; He, Hongwen. / State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging. Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 950-952 (Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021).

@inproceedings{1372cd857810430aa957727d539cbed8,

title = "State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging",

abstract = "State of health (SOH) estimation has deep insights into the lithium-ion battery (LIB) life diagnostic and protection. A machine learning-based SOH estimator is established, utilizing a new set of health indicators (His) extracted from the regional constant-voltage (CV) charging. First, a thorough analysis is performed over different CV-based His to obtain the informative ones with strong correlation against the SOH. Second, an artificial neural network model is employed to construct the nonlinear mapping from the selected His to the battery SOH. The proposed SOH estimator is validated with long-term degradation experiments performed on LiNiCoAlO2 (NCA) cells. Results imply the proposed method manifests itself with high estimation accuracy, low charging integrity requirements, and a high robustness to cell inconsistency.",

keywords = "health indicators, lithium-ion battery, neural network, state of health",

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

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021 ; Conference date: 24-05-2021 Through 27-05-2021",

year = "2021",

month = may,

day = "24",

doi = "10.1109/ECCE-Asia49820.2021.9479412",

language = "English",

series = "Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021",

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address = "United States",

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Ruan, H, Wei, Z & He, H 2021, State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging. in Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021., 9479412, Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021, Institute of Electrical and Electronics Engineers Inc., pp. 950-952, 12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021, Virtual, Singapore, Singapore, 24/05/21. https://doi.org/10.1109/ECCE-Asia49820.2021.9479412

State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging. / Ruan, Haokai; Wei, Zhongbao ; He, Hongwen.
Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 950-952 9479412 (Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging

AU - Ruan, Haokai

AU - Wei, Zhongbao

AU - He, Hongwen

PY - 2021/5/24

Y1 - 2021/5/24

N2 - State of health (SOH) estimation has deep insights into the lithium-ion battery (LIB) life diagnostic and protection. A machine learning-based SOH estimator is established, utilizing a new set of health indicators (His) extracted from the regional constant-voltage (CV) charging. First, a thorough analysis is performed over different CV-based His to obtain the informative ones with strong correlation against the SOH. Second, an artificial neural network model is employed to construct the nonlinear mapping from the selected His to the battery SOH. The proposed SOH estimator is validated with long-term degradation experiments performed on LiNiCoAlO2 (NCA) cells. Results imply the proposed method manifests itself with high estimation accuracy, low charging integrity requirements, and a high robustness to cell inconsistency.

AB - State of health (SOH) estimation has deep insights into the lithium-ion battery (LIB) life diagnostic and protection. A machine learning-based SOH estimator is established, utilizing a new set of health indicators (His) extracted from the regional constant-voltage (CV) charging. First, a thorough analysis is performed over different CV-based His to obtain the informative ones with strong correlation against the SOH. Second, an artificial neural network model is employed to construct the nonlinear mapping from the selected His to the battery SOH. The proposed SOH estimator is validated with long-term degradation experiments performed on LiNiCoAlO2 (NCA) cells. Results imply the proposed method manifests itself with high estimation accuracy, low charging integrity requirements, and a high robustness to cell inconsistency.

KW - health indicators

KW - lithium-ion battery

KW - neural network

KW - state of health

UR - https://www.scopus.com/pages/publications/85114200879

U2 - 10.1109/ECCE-Asia49820.2021.9479412

DO - 10.1109/ECCE-Asia49820.2021.9479412

M3 - Conference contribution

AN - SCOPUS:85114200879

T3 - Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

SP - 950

EP - 952

BT - Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 12th IEEE Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021

Y2 - 24 May 2021 through 27 May 2021

ER -

Ruan H, Wei Z , He H. State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging. In Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 950-952. 9479412. (Proceedings of the Energy Conversion Congress and Exposition - Asia, ECCE Asia 2021). doi: 10.1109/ECCE-Asia49820.2021.9479412

State of Health Estimation of Li-ion Battery Based on Regional Constant Voltage Charging

Abstract

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UN SDGs

Keywords

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