State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator

Daoming Sun; Xiaoli Yu; Chongming Wang; Cheng Zhang; Rui Huang; Quan Zhou; Taz Amietszajew; Rohit Bhagat

doi:10.1016/j.energy.2020.119025

State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator

Daoming Sun, Xiaoli Yu, Chongming Wang, Cheng Zhang, Rui Huang, Quan Zhou, Taz Amietszajew, Rohit Bhagat

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

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Abstract

Adaptive extended Kalman filter (AEKF) is widely used for lithium-ion battery (LIBs) state of charge (SOC) estimation. Innovation covariance matrix (ICM) of AEKF is estimated by fixed-length error innovation sequence (EIS) (the difference between measured and estimated voltages), which doesn't consider the distribution change of EIS. However, the distribution of EIS will change due to load current dynamics or error of battery model. Failing to consider the distribution change of EIS will lead to SOC estimation inaccuracy. To address this problem, this paper proposed an intelligent adaptive extended Kalman filter (IAEKF) method that can detect the moment of distribution change of EIS by the maximum likelihood function. Then, the ICM is updated based on the EIS after that moment to improve the SOC estimation accuracy. Results show that the proposed IAEKF method improves SOC estimation accuracy. Compared to that of the AEKF, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) of SOC based on IAEKF decrease significantly by 43.34% and 55.80%, respectively, while the computation time only increases by 4.59%. In the end, the effect of initial parameters on the SOC estimation accuracy was analysed. It is found that the proposed IAEKF method is robust against parameter uncertainties.

Original language	English
Article number	119025
Journal	Energy
Volume	214
Early online date	8 Oct 2020
DOIs	https://doi.org/10.1016/j.energy.2020.119025
Publication status	Published - 1 Jan 2021

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy, 214, (2021) DOI: 10.1016/j.energy.2020.119025

© 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Funder

Natural Science Foundation of China under grant number No. 51806189 and 51476143

Keywords

Distribution change
Innovation covariance matrix
Intelligent adaptive extended kalman filter
Lithium-ion battery
State of charge

ASJC Scopus subject areas

Civil and Structural Engineering
Building and Construction
Modelling and Simulation
Renewable Energy, Sustainability and the Environment
Fuel Technology
Energy Engineering and Power Technology
Pollution
Energy(all)
Mechanical Engineering
Industrial and Manufacturing Engineering
Management, Monitoring, Policy and Law
Electrical and Electronic Engineering

UN SDGs

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

Access to Document

10.1016/j.energy.2020.119025

Post-PrintAccepted author manuscript, 2.41 MBLicence: CC BY-NC-ND

Cite this

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title = "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator",

abstract = "Adaptive extended Kalman filter (AEKF) is widely used for lithium-ion battery (LIBs) state of charge (SOC) estimation. Innovation covariance matrix (ICM) of AEKF is estimated by fixed-length error innovation sequence (EIS) (the difference between measured and estimated voltages), which doesn't consider the distribution change of EIS. However, the distribution of EIS will change due to load current dynamics or error of battery model. Failing to consider the distribution change of EIS will lead to SOC estimation inaccuracy. To address this problem, this paper proposed an intelligent adaptive extended Kalman filter (IAEKF) method that can detect the moment of distribution change of EIS by the maximum likelihood function. Then, the ICM is updated based on the EIS after that moment to improve the SOC estimation accuracy. Results show that the proposed IAEKF method improves SOC estimation accuracy. Compared to that of the AEKF, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) of SOC based on IAEKF decrease significantly by 43.34% and 55.80%, respectively, while the computation time only increases by 4.59%. In the end, the effect of initial parameters on the SOC estimation accuracy was analysed. It is found that the proposed IAEKF method is robust against parameter uncertainties.",

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author = "Daoming Sun and Xiaoli Yu and Chongming Wang and Cheng Zhang and Rui Huang and Quan Zhou and Taz Amietszajew and Rohit Bhagat",

note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy, 214, (2021) DOI: 10.1016/j.energy.2020.119025 {\textcopyright} 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ ",

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AU - Sun, Daoming

AU - Yu, Xiaoli

AU - Wang, Chongming

AU - Zhang, Cheng

AU - Huang, Rui

AU - Zhou, Quan

AU - Amietszajew, Taz

AU - Bhagat, Rohit

N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Energy, 214, (2021) DOI: 10.1016/j.energy.2020.119025 © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Adaptive extended Kalman filter (AEKF) is widely used for lithium-ion battery (LIBs) state of charge (SOC) estimation. Innovation covariance matrix (ICM) of AEKF is estimated by fixed-length error innovation sequence (EIS) (the difference between measured and estimated voltages), which doesn't consider the distribution change of EIS. However, the distribution of EIS will change due to load current dynamics or error of battery model. Failing to consider the distribution change of EIS will lead to SOC estimation inaccuracy. To address this problem, this paper proposed an intelligent adaptive extended Kalman filter (IAEKF) method that can detect the moment of distribution change of EIS by the maximum likelihood function. Then, the ICM is updated based on the EIS after that moment to improve the SOC estimation accuracy. Results show that the proposed IAEKF method improves SOC estimation accuracy. Compared to that of the AEKF, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) of SOC based on IAEKF decrease significantly by 43.34% and 55.80%, respectively, while the computation time only increases by 4.59%. In the end, the effect of initial parameters on the SOC estimation accuracy was analysed. It is found that the proposed IAEKF method is robust against parameter uncertainties.

AB - Adaptive extended Kalman filter (AEKF) is widely used for lithium-ion battery (LIBs) state of charge (SOC) estimation. Innovation covariance matrix (ICM) of AEKF is estimated by fixed-length error innovation sequence (EIS) (the difference between measured and estimated voltages), which doesn't consider the distribution change of EIS. However, the distribution of EIS will change due to load current dynamics or error of battery model. Failing to consider the distribution change of EIS will lead to SOC estimation inaccuracy. To address this problem, this paper proposed an intelligent adaptive extended Kalman filter (IAEKF) method that can detect the moment of distribution change of EIS by the maximum likelihood function. Then, the ICM is updated based on the EIS after that moment to improve the SOC estimation accuracy. Results show that the proposed IAEKF method improves SOC estimation accuracy. Compared to that of the AEKF, the Root Mean Squared Error (RMSE) and the Mean Absolute Error (MAE) of SOC based on IAEKF decrease significantly by 43.34% and 55.80%, respectively, while the computation time only increases by 4.59%. In the end, the effect of initial parameters on the SOC estimation accuracy was analysed. It is found that the proposed IAEKF method is robust against parameter uncertainties.

KW - Distribution change

KW - Innovation covariance matrix

KW - Intelligent adaptive extended kalman filter

KW - Lithium-ion battery

KW - State of charge

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VL - 214

JO - Energy

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ER -

State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator

Abstract

Bibliographical note

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Keywords

ASJC Scopus subject areas

UN SDGs

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