Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model

Jiang Chang; Zhongbao Wei; Hongwen He

doi:10.1109/ICIEA48937.2020.9248312

Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model

Jiang Chang
, Zhongbao Wei
, Hongwen He

School of Mechanical Engineering

Beijing Institute of Technology

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

10 Citations (Scopus)

Abstract

Electric vehicles (EVs) have developed rapidly in the face of critical problems of climate change, resource scarcity and environmental pollution, while lithium-ion batteries (LIBs) have been widely used as the onboard power source of EVs. As a key state in the battery management system (BMS), state of charge (SOC) not only defines the safety margin of battery to avoid over- charge/discharge, but also underlies the system-level energy management. This paper proposes an online adaptive model-based SOC estimator. This method combines the Thevenin battery model, the recursive least squares (RLS) algorithm and the extended Kalman filter (EKF) algorithm to accomplish parameter identification and SOC estimation in a cascaded manner. Simulations and experiments are performed to evaluate the proposed method. Results suggest that the proposed method can effectively track the change of model parameters, and thus estimate the SOC accurately in real time.

Original language	English
Title of host publication	Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1490-1495
Number of pages	6
ISBN (Electronic)	9781728151694
DOIs	https://doi.org/10.1109/ICIEA48937.2020.9248312
Publication status	Published - 9 Nov 2020
Event	15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 - Virtual, Kristiansand, Norway Duration: 9 Nov 2020 → 13 Nov 2020

Publication series

Name	Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

Conference

Conference	15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020
Country/Territory	Norway
City	Virtual, Kristiansand
Period	9/11/20 → 13/11/20

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 12 Responsible Consumption and Production
SDG 13 Climate Action

Keywords

Lithium-ion battery
equivalent circuit model
online estimation
parameter identification
state of charge

Access to Document

10.1109/ICIEA48937.2020.9248312

Cite this

Chang, J., Wei, Z., & He, H. (2020). Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model. In Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 (pp. 1490-1495). Article 9248312 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA48937.2020.9248312

Chang, Jiang ; Wei, Zhongbao ; He, Hongwen. / Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model. Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1490-1495 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020).

@inproceedings{bc2f760431f04bed8cab94299b33db0f,

title = "Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model",

abstract = "Electric vehicles (EVs) have developed rapidly in the face of critical problems of climate change, resource scarcity and environmental pollution, while lithium-ion batteries (LIBs) have been widely used as the onboard power source of EVs. As a key state in the battery management system (BMS), state of charge (SOC) not only defines the safety margin of battery to avoid over- charge/discharge, but also underlies the system-level energy management. This paper proposes an online adaptive model-based SOC estimator. This method combines the Thevenin battery model, the recursive least squares (RLS) algorithm and the extended Kalman filter (EKF) algorithm to accomplish parameter identification and SOC estimation in a cascaded manner. Simulations and experiments are performed to evaluate the proposed method. Results suggest that the proposed method can effectively track the change of model parameters, and thus estimate the SOC accurately in real time.",

keywords = "Lithium-ion battery, equivalent circuit model, online estimation, parameter identification, state of charge",

author = "Jiang Chang and Zhongbao Wei and Hongwen He",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 ; Conference date: 09-11-2020 Through 13-11-2020",

year = "2020",

month = nov,

day = "9",

doi = "10.1109/ICIEA48937.2020.9248312",

language = "English",

series = "Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020",

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Chang, J, Wei, Z & He, H 2020, Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model. in Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020., 9248312, Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1490-1495, 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020, Virtual, Kristiansand, Norway, 9/11/20. https://doi.org/10.1109/ICIEA48937.2020.9248312

Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model. / Chang, Jiang; Wei, Zhongbao ; He, Hongwen.
Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1490-1495 9248312 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020).

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

TY - GEN

T1 - Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model

AU - Chang, Jiang

AU - Wei, Zhongbao

AU - He, Hongwen

PY - 2020/11/9

Y1 - 2020/11/9

N2 - Electric vehicles (EVs) have developed rapidly in the face of critical problems of climate change, resource scarcity and environmental pollution, while lithium-ion batteries (LIBs) have been widely used as the onboard power source of EVs. As a key state in the battery management system (BMS), state of charge (SOC) not only defines the safety margin of battery to avoid over- charge/discharge, but also underlies the system-level energy management. This paper proposes an online adaptive model-based SOC estimator. This method combines the Thevenin battery model, the recursive least squares (RLS) algorithm and the extended Kalman filter (EKF) algorithm to accomplish parameter identification and SOC estimation in a cascaded manner. Simulations and experiments are performed to evaluate the proposed method. Results suggest that the proposed method can effectively track the change of model parameters, and thus estimate the SOC accurately in real time.

AB - Electric vehicles (EVs) have developed rapidly in the face of critical problems of climate change, resource scarcity and environmental pollution, while lithium-ion batteries (LIBs) have been widely used as the onboard power source of EVs. As a key state in the battery management system (BMS), state of charge (SOC) not only defines the safety margin of battery to avoid over- charge/discharge, but also underlies the system-level energy management. This paper proposes an online adaptive model-based SOC estimator. This method combines the Thevenin battery model, the recursive least squares (RLS) algorithm and the extended Kalman filter (EKF) algorithm to accomplish parameter identification and SOC estimation in a cascaded manner. Simulations and experiments are performed to evaluate the proposed method. Results suggest that the proposed method can effectively track the change of model parameters, and thus estimate the SOC accurately in real time.

KW - Lithium-ion battery

KW - equivalent circuit model

KW - online estimation

KW - parameter identification

KW - state of charge

UR - https://www.scopus.com/pages/publications/85097525696

U2 - 10.1109/ICIEA48937.2020.9248312

DO - 10.1109/ICIEA48937.2020.9248312

M3 - Conference contribution

AN - SCOPUS:85097525696

T3 - Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

SP - 1490

EP - 1495

BT - Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

Y2 - 9 November 2020 through 13 November 2020

ER -

Chang J, Wei Z , He H. Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model. In Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1490-1495. 9248312. (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020). doi: 10.1109/ICIEA48937.2020.9248312

Lithium-Ion Battery Parameter Identification and State of Charge Estimation based on Equivalent Circuit Model

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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