An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter

Yipeng Yang; Mengfei Xu; Wenjie Wu; Xuerui Gong; Keyang Jing; Jun Tian

doi:10.1109/ICEMS59686.2023.10345101

An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter

Yipeng Yang^*
, Mengfei Xu
, Wenjie Wu
, Xuerui Gong
, Keyang Jing
, Jun Tian

^*Corresponding author for this work

National Key Laboratory of Electromagnetic Energy

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

2 Scopus citations

Abstract

This article introduces an algorithm for estimating the State of Energy (SOE) of lithium-ion power batteries using a particle filter (PF) and extended Kalman filter (EKF) dual filter. The algorithm is divided into three parts: real-time data measurement, particle filter algorithm to estimate SOE, and online EKF to estimate SOE. After completing the real-time data measurement, the particle filter is used to estimate the SOE. After updating the SOE state, the voltage error between the model voltage and the measured voltage is used to calculate the Kalman gain of the EKF to estimate the total energy of the battery online. In this way, the algorithm can estimate both the SOE and total energy of battery simultaneously, and The online update of total energy can enhance the accuracy of the SOE estimation.

Original language	English
Title of host publication	2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4874-4878
Number of pages	5
ISBN (Electronic)	9798350317589
DOIs	https://doi.org/10.1109/ICEMS59686.2023.10345101
State	Published - 2023
Externally published	Yes
Event	26th International Conference on Electrical Machines and Systems, ICEMS 2023 - Zhuhai, China Duration: 5 Nov 2023 → 8 Nov 2023

Publication series

Name	2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023

Conference

Conference	26th International Conference on Electrical Machines and Systems, ICEMS 2023
Country/Territory	China
City	Zhuhai
Period	5/11/23 → 8/11/23

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

PF-EKF dual filter
SOE estimation algorithm
lithium-ion batteries
particle filter

Access to Document

10.1109/ICEMS59686.2023.10345101

Cite this

Yang, Y., Xu, M., Wu, W., Gong, X., Jing, K., & Tian, J. (2023). An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter. In 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023 (pp. 4874-4878). (2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEMS59686.2023.10345101

Yang, Yipeng ; Xu, Mengfei ; Wu, Wenjie et al. / An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter. 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 4874-4878 (2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023).

@inproceedings{ac06514378ae433dbc0b45fd3af6f67e,

title = "An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter",

abstract = "This article introduces an algorithm for estimating the State of Energy (SOE) of lithium-ion power batteries using a particle filter (PF) and extended Kalman filter (EKF) dual filter. The algorithm is divided into three parts: real-time data measurement, particle filter algorithm to estimate SOE, and online EKF to estimate SOE. After completing the real-time data measurement, the particle filter is used to estimate the SOE. After updating the SOE state, the voltage error between the model voltage and the measured voltage is used to calculate the Kalman gain of the EKF to estimate the total energy of the battery online. In this way, the algorithm can estimate both the SOE and total energy of battery simultaneously, and The online update of total energy can enhance the accuracy of the SOE estimation.",

keywords = "PF-EKF dual filter, SOE estimation algorithm, lithium-ion batteries, particle filter",

author = "Yipeng Yang and Mengfei Xu and Wenjie Wu and Xuerui Gong and Keyang Jing and Jun Tian",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 26th International Conference on Electrical Machines and Systems, ICEMS 2023 ; Conference date: 05-11-2023 Through 08-11-2023",

year = "2023",

doi = "10.1109/ICEMS59686.2023.10345101",

language = "英语",

series = "2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4874--4878",

booktitle = "2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023",

address = "美国",

}

Yang, Y, Xu, M, Wu, W, Gong, X, Jing, K & Tian, J 2023, An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter. in 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023. 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023, Institute of Electrical and Electronics Engineers Inc., pp. 4874-4878, 26th International Conference on Electrical Machines and Systems, ICEMS 2023, Zhuhai, China, 5/11/23. https://doi.org/10.1109/ICEMS59686.2023.10345101

An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter. / Yang, Yipeng; Xu, Mengfei; Wu, Wenjie et al.
2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 4874-4878 (2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023).

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

TY - GEN

T1 - An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter

AU - Yang, Yipeng

AU - Xu, Mengfei

AU - Wu, Wenjie

AU - Gong, Xuerui

AU - Jing, Keyang

AU - Tian, Jun

PY - 2023

Y1 - 2023

N2 - This article introduces an algorithm for estimating the State of Energy (SOE) of lithium-ion power batteries using a particle filter (PF) and extended Kalman filter (EKF) dual filter. The algorithm is divided into three parts: real-time data measurement, particle filter algorithm to estimate SOE, and online EKF to estimate SOE. After completing the real-time data measurement, the particle filter is used to estimate the SOE. After updating the SOE state, the voltage error between the model voltage and the measured voltage is used to calculate the Kalman gain of the EKF to estimate the total energy of the battery online. In this way, the algorithm can estimate both the SOE and total energy of battery simultaneously, and The online update of total energy can enhance the accuracy of the SOE estimation.

AB - This article introduces an algorithm for estimating the State of Energy (SOE) of lithium-ion power batteries using a particle filter (PF) and extended Kalman filter (EKF) dual filter. The algorithm is divided into three parts: real-time data measurement, particle filter algorithm to estimate SOE, and online EKF to estimate SOE. After completing the real-time data measurement, the particle filter is used to estimate the SOE. After updating the SOE state, the voltage error between the model voltage and the measured voltage is used to calculate the Kalman gain of the EKF to estimate the total energy of the battery online. In this way, the algorithm can estimate both the SOE and total energy of battery simultaneously, and The online update of total energy can enhance the accuracy of the SOE estimation.

KW - PF-EKF dual filter

KW - SOE estimation algorithm

KW - lithium-ion batteries

KW - particle filter

UR - https://www.scopus.com/pages/publications/85182332908

U2 - 10.1109/ICEMS59686.2023.10345101

DO - 10.1109/ICEMS59686.2023.10345101

M3 - 会议稿件

AN - SCOPUS:85182332908

T3 - 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023

SP - 4874

EP - 4878

BT - 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th International Conference on Electrical Machines and Systems, ICEMS 2023

Y2 - 5 November 2023 through 8 November 2023

ER -

Yang Y, Xu M, Wu W, Gong X, Jing K, Tian J. An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter. In 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 4874-4878. (2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023). doi: 10.1109/ICEMS59686.2023.10345101

An SOE estimation algorithm for lithium-ion power batteries based on a PF-EKF dual filter

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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