State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF)

Venu Sangwan; Rajesh Kumar; Akshay Kumar Rathore

doi:10.1109/IAS.2017.8101722

State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF)

Venu Sangwan
, Rajesh Kumar
, Akshay Kumar Rathore

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

48 Citations (Scopus)

Abstract

A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.

Original language	English
Title of host publication	2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781509048946
DOIs	https://doi.org/10.1109/IAS.2017.8101722
Publication status	Published - 8 Nov 2017
Externally published	Yes
Event	2017 IEEE Industry Applications Society Annual Meeting, IAS 2017 - Cincinnati, United States Duration: 1 Oct 2017 → 5 Oct 2017

Publication series

Name	2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
Volume	2017-January

Conference

Conference	2017 IEEE Industry Applications Society Annual Meeting, IAS 2017
Country/Territory	United States
City	Cincinnati
Period	1/10/17 → 5/10/17

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Battery electric vehicle
Battery management system
Central difference Kalman filter
Extended Kalman Filter
Li-ion batteries
State of charge estimation

ASJC Scopus subject areas

Industrial and Manufacturing Engineering
Control and Optimization
Energy Engineering and Power Technology
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/IAS.2017.8101722

Cite this

Sangwan, V., Kumar, R., & Rathore, A. K. (2017). State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF). In 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017 (pp. 1-6). (2017 IEEE Industry Applications Society Annual Meeting, IAS 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAS.2017.8101722

Sangwan, Venu ; Kumar, Rajesh ; Rathore, Akshay Kumar. / State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF). 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6 (2017 IEEE Industry Applications Society Annual Meeting, IAS 2017).

@inproceedings{83a9c5a1a6804d9fb8c85443695a6181,

title = "State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF)",

abstract = "A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4\% and an absolute maximum error less than 6\% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.",

keywords = "Battery electric vehicle, Battery management system, Central difference Kalman filter, Extended Kalman Filter, Li-ion batteries, State of charge estimation",

author = "Venu Sangwan and Rajesh Kumar and Rathore, \{Akshay Kumar\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017 ; Conference date: 01-10-2017 Through 05-10-2017",

year = "2017",

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doi = "10.1109/IAS.2017.8101722",

language = "English",

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Sangwan, V, Kumar, R & Rathore, AK 2017, State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF). in 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017. 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017, Cincinnati, United States, 1/10/17. https://doi.org/10.1109/IAS.2017.8101722

State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF). / Sangwan, Venu; Kumar, Rajesh; Rathore, Akshay Kumar.
2017 IEEE Industry Applications Society Annual Meeting, IAS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6 (2017 IEEE Industry Applications Society Annual Meeting, IAS 2017; Vol. 2017-January).

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

TY - GEN

T1 - State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF)

AU - Sangwan, Venu

AU - Kumar, Rajesh

AU - Rathore, Akshay Kumar

PY - 2017/11/8

Y1 - 2017/11/8

N2 - A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.

AB - A precise estimation of the state-of-charge (SOC) is of major importance in battery electric vehicles (BEVs) for prolonging the lifetime of the battery. Firstly, an equivalent circuit using the first-order RC for describing the dynamic behavior of the battery has been developed. Parameters of the battery are identified using the Ageist Spider Monkey Optimization (ASMO) technique. The optimization method uses the anticipated terminal voltage of the battery during operation and error between the anticipated and measured voltage for identification of parameters. The focus of this paper is the implementation of recursive estimation of battery SOC using extended Kalman filter (EKF) and Central Difference Kalman Filter (CDKF) approach. The estimation has an absolute root-mean-square error (RMSE) of less than 4% and an absolute maximum error less than 6% in all circumstances. The test results indicate that CDKF has good performance compared to EKF for the estimation of battery SOC.

KW - Battery electric vehicle

KW - Battery management system

KW - Central difference Kalman filter

KW - Extended Kalman Filter

KW - Li-ion batteries

KW - State of charge estimation

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M3 - Conference contribution

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T3 - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017

SP - 1

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BT - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017

Y2 - 1 October 2017 through 5 October 2017

ER -

Sangwan V, Kumar R, Rathore AK. State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF). In 2017 IEEE Industry Applications Society Annual Meeting, IAS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6. (2017 IEEE Industry Applications Society Annual Meeting, IAS 2017). doi: 10.1109/IAS.2017.8101722

State-of-charge estimation for Li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF)

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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