State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter

Yuntian Liu; Yigeng Huangfu; Rui Ma; Liangcai Xu; Dongdong Zhao; Jiang Wei

doi:10.1109/IAS.2019.8911931

State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter

Yuntian Liu, Yigeng Huangfu, Rui Ma, Liangcai Xu, Dongdong Zhao, Jiang Wei

School of Automation

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

14 Scopus citations

Abstract

Lithium-ion batteries as a dominant green energy are widely used in electrical vehicles (EV) due to their unique advantages. The battery modeling, parameter identification and state estimation are always the emphases of research which complement each other in a battery management system (BMS). Compared to the mainstream of the current equivalent circuit (EC) models, the rigorous electrochemical model with high complexity and tight coupling is not suitable for on-line simulation in EV. In this paper, the state of charge (SOC) estimation using extended Kalman filter (EKF) algorithm is proposed based on the simplified electrochemical model-single particle (SP) model. The battery parameters identified by the particle swarm optimization (PSO) algorithm show a higher accuracy, which can track the terminal voltage effectively. The SOC estimation results show that SP model with EKF algorithm is a computational method with a good performance of robust, accuracy and stability which can be used in energy management systems of EV.

Original language	English
Title of host publication	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538645390
DOIs	https://doi.org/10.1109/IAS.2019.8911931
State	Published - Sep 2019
Event	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 - Baltimore, United States Duration: 29 Sep 2019 → 3 Oct 2019

Publication series

Name	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Conference

Conference	2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
Country/Territory	United States
City	Baltimore
Period	29/09/19 → 3/10/19

Keywords

EKF algorithm
Lithium-ion battery
PSO algorithm
SOC estimation
SP model

UN SDGs

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

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10.1109/IAS.2019.8911931

Cite this

Liu, Y., Huangfu, Y., Ma, R., Xu, L., Zhao, D., & Wei, J. (2019). State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter. In 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 Article 8911931 (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAS.2019.8911931

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title = "State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter",

abstract = "Lithium-ion batteries as a dominant green energy are widely used in electrical vehicles (EV) due to their unique advantages. The battery modeling, parameter identification and state estimation are always the emphases of research which complement each other in a battery management system (BMS). Compared to the mainstream of the current equivalent circuit (EC) models, the rigorous electrochemical model with high complexity and tight coupling is not suitable for on-line simulation in EV. In this paper, the state of charge (SOC) estimation using extended Kalman filter (EKF) algorithm is proposed based on the simplified electrochemical model-single particle (SP) model. The battery parameters identified by the particle swarm optimization (PSO) algorithm show a higher accuracy, which can track the terminal voltage effectively. The SOC estimation results show that SP model with EKF algorithm is a computational method with a good performance of robust, accuracy and stability which can be used in energy management systems of EV.",

keywords = "EKF algorithm, Lithium-ion battery, PSO algorithm, SOC estimation, SP model",

author = "Yuntian Liu and Yigeng Huangfu and Rui Ma and Liangcai Xu and Dongdong Zhao and Jiang Wei",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 ; Conference date: 29-09-2019 Through 03-10-2019",

year = "2019",

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doi = "10.1109/IAS.2019.8911931",

language = "英语",

series = "2019 IEEE Industry Applications Society Annual Meeting, IAS 2019",

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Liu, Y, Huangfu, Y , Ma, R, Xu, L, Zhao, D & Wei, J 2019, State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter. in 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019., 8911931, 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019, Baltimore, United States, 29/09/19. https://doi.org/10.1109/IAS.2019.8911931

State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter. / Liu, Yuntian; Huangfu, Yigeng ; Ma, Rui et al.
2019 IEEE Industry Applications Society Annual Meeting, IAS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8911931 (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019).

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

TY - GEN

T1 - State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter

AU - Liu, Yuntian

AU - Huangfu, Yigeng

AU - Ma, Rui

AU - Xu, Liangcai

AU - Zhao, Dongdong

AU - Wei, Jiang

PY - 2019/9

Y1 - 2019/9

N2 - Lithium-ion batteries as a dominant green energy are widely used in electrical vehicles (EV) due to their unique advantages. The battery modeling, parameter identification and state estimation are always the emphases of research which complement each other in a battery management system (BMS). Compared to the mainstream of the current equivalent circuit (EC) models, the rigorous electrochemical model with high complexity and tight coupling is not suitable for on-line simulation in EV. In this paper, the state of charge (SOC) estimation using extended Kalman filter (EKF) algorithm is proposed based on the simplified electrochemical model-single particle (SP) model. The battery parameters identified by the particle swarm optimization (PSO) algorithm show a higher accuracy, which can track the terminal voltage effectively. The SOC estimation results show that SP model with EKF algorithm is a computational method with a good performance of robust, accuracy and stability which can be used in energy management systems of EV.

AB - Lithium-ion batteries as a dominant green energy are widely used in electrical vehicles (EV) due to their unique advantages. The battery modeling, parameter identification and state estimation are always the emphases of research which complement each other in a battery management system (BMS). Compared to the mainstream of the current equivalent circuit (EC) models, the rigorous electrochemical model with high complexity and tight coupling is not suitable for on-line simulation in EV. In this paper, the state of charge (SOC) estimation using extended Kalman filter (EKF) algorithm is proposed based on the simplified electrochemical model-single particle (SP) model. The battery parameters identified by the particle swarm optimization (PSO) algorithm show a higher accuracy, which can track the terminal voltage effectively. The SOC estimation results show that SP model with EKF algorithm is a computational method with a good performance of robust, accuracy and stability which can be used in energy management systems of EV.

KW - EKF algorithm

KW - Lithium-ion battery

KW - PSO algorithm

KW - SOC estimation

KW - SP model

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M3 - 会议稿件

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T3 - 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

BT - 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 29 September 2019 through 3 October 2019

ER -

Liu Y, Huangfu Y , Ma R, Xu L, Zhao D, Wei J. State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter. In 2019 IEEE Industry Applications Society Annual Meeting, IAS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8911931. (2019 IEEE Industry Applications Society Annual Meeting, IAS 2019). doi: 10.1109/IAS.2019.8911931

State of Charge Estimation of Lithium-ion Batteries Electrochemical Model with Extended Kalman Filter

Abstract

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Keywords

UN SDGs

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