AC impedance based online state-of-charge estimation for Li-ion battery

Shing Lih Wu; Hung Cheng Chen; Ming Yang Tsai; Tong Chou Lin; Liang-Rui Chen

doi:10.1109/ICICE.2017.8479183

AC impedance based online state-of-charge estimation for Li-ion battery

Shing Lih Wu, Hung Cheng Chen, Ming Yang Tsai, Tong Chou Lin, Liang-Rui Chen

Department of Electrical Engineering

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

2 Citations (Scopus)

Abstract

The accurate estimation of state-of-charge (SOC) is one of the most important core functions of a battery management system (BMS), which provides the essential information needed for battery management, monitoring, and status analysis. In this paper, an AC impedance based method is proposed for the accurate estimation of the SOC of a Li-ion battery. First, a 1kHz sinusoidal ripple was injected into a 18650 Li-ion battery and the AC impedance values were measured at 0% to 100% charging status at 10% increments. The correlation between SOC and AC impedance was then determined by linear regression. When it comes to practical application, a sinusoidal ripple is simply superposed into the charge/discharge source. The AC voltage and current of the battery is used to calculate the AC impedance, and this allows a fairly accurate estimation of the battery SOC. Final validation using a power analyzer and actual measurement showed the average error of SOC estimation to be within 4%. This encouraging result showed the proposed method can provide a simple and practical solution for online estimation of the SOC of Li-ion batteries.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering
Subtitle of host publication	Information and Innovation for Modern Technology, ICICE 2017
Editors	Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	53-56
Number of pages	4
ISBN (Electronic)	9781538632024
DOIs	https://doi.org/10.1109/ICICE.2017.8479183
Publication status	Published - 2018 Oct 1
Event	2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 - Xiamen, Fujian, China Duration: 2017 Nov 17 → 2017 Nov 20

Publication series

Name	Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017

Other

Other	2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017
Country	China
City	Xiamen, Fujian
Period	17-11-17 → 17-11-20

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Information Systems and Management

Access to Document

10.1109/ICICE.2017.8479183

Fingerprint Dive into the research topics of 'AC impedance based online state-of-charge estimation for Li-ion battery'. Together they form a unique fingerprint.

Cite this

Wu, S. L., Chen, H. C., Tsai, M. Y., Lin, T. C., & Chen, L-R. (2018). AC impedance based online state-of-charge estimation for Li-ion battery. In A. D. K-T. Lam, S. D. Prior, & T-H. Meen (Eds.), Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017 (pp. 53-56). [8479183] (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICE.2017.8479183

Wu, Shing Lih ; Chen, Hung Cheng ; Tsai, Ming Yang ; Lin, Tong Chou ; Chen, Liang-Rui. / AC impedance based online state-of-charge estimation for Li-ion battery. Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. editor / Artde Donald Kin-Tak Lam ; Stephen D. Prior ; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 53-56 (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017).

@inproceedings{c80cb04d92a544669a3fbe8ef5c11bb8,

title = "AC impedance based online state-of-charge estimation for Li-ion battery",

abstract = "The accurate estimation of state-of-charge (SOC) is one of the most important core functions of a battery management system (BMS), which provides the essential information needed for battery management, monitoring, and status analysis. In this paper, an AC impedance based method is proposed for the accurate estimation of the SOC of a Li-ion battery. First, a 1kHz sinusoidal ripple was injected into a 18650 Li-ion battery and the AC impedance values were measured at 0% to 100% charging status at 10% increments. The correlation between SOC and AC impedance was then determined by linear regression. When it comes to practical application, a sinusoidal ripple is simply superposed into the charge/discharge source. The AC voltage and current of the battery is used to calculate the AC impedance, and this allows a fairly accurate estimation of the battery SOC. Final validation using a power analyzer and actual measurement showed the average error of SOC estimation to be within 4%. This encouraging result showed the proposed method can provide a simple and practical solution for online estimation of the SOC of Li-ion batteries.",

author = "Wu, {Shing Lih} and Chen, {Hung Cheng} and Tsai, {Ming Yang} and Lin, {Tong Chou} and Liang-Rui Chen",

year = "2018",

month = oct,

day = "1",

doi = "10.1109/ICICE.2017.8479183",

language = "English",

series = "Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017",

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

pages = "53--56",

editor = "Lam, {Artde Donald Kin-Tak} and Prior, {Stephen D.} and Teen-Hang Meen",

booktitle = "Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering",

address = "United States",

note = "2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 ; Conference date: 17-11-2017 Through 20-11-2017",

}

Wu, SL, Chen, HC, Tsai, MY, Lin, TC & Chen, L-R 2018, AC impedance based online state-of-charge estimation for Li-ion battery. in ADK-T Lam, SD Prior & T-H Meen (eds), Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017., 8479183, Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017, Institute of Electrical and Electronics Engineers Inc., pp. 53-56, 2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017, Xiamen, Fujian, China, 17-11-17. https://doi.org/10.1109/ICICE.2017.8479183

AC impedance based online state-of-charge estimation for Li-ion battery. / Wu, Shing Lih; Chen, Hung Cheng; Tsai, Ming Yang; Lin, Tong Chou; Chen, Liang-Rui.

Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. ed. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 53-56 8479183 (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017).

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

TY - GEN

T1 - AC impedance based online state-of-charge estimation for Li-ion battery

AU - Wu, Shing Lih

AU - Chen, Hung Cheng

AU - Tsai, Ming Yang

AU - Lin, Tong Chou

AU - Chen, Liang-Rui

PY - 2018/10/1

Y1 - 2018/10/1

N2 - The accurate estimation of state-of-charge (SOC) is one of the most important core functions of a battery management system (BMS), which provides the essential information needed for battery management, monitoring, and status analysis. In this paper, an AC impedance based method is proposed for the accurate estimation of the SOC of a Li-ion battery. First, a 1kHz sinusoidal ripple was injected into a 18650 Li-ion battery and the AC impedance values were measured at 0% to 100% charging status at 10% increments. The correlation between SOC and AC impedance was then determined by linear regression. When it comes to practical application, a sinusoidal ripple is simply superposed into the charge/discharge source. The AC voltage and current of the battery is used to calculate the AC impedance, and this allows a fairly accurate estimation of the battery SOC. Final validation using a power analyzer and actual measurement showed the average error of SOC estimation to be within 4%. This encouraging result showed the proposed method can provide a simple and practical solution for online estimation of the SOC of Li-ion batteries.

AB - The accurate estimation of state-of-charge (SOC) is one of the most important core functions of a battery management system (BMS), which provides the essential information needed for battery management, monitoring, and status analysis. In this paper, an AC impedance based method is proposed for the accurate estimation of the SOC of a Li-ion battery. First, a 1kHz sinusoidal ripple was injected into a 18650 Li-ion battery and the AC impedance values were measured at 0% to 100% charging status at 10% increments. The correlation between SOC and AC impedance was then determined by linear regression. When it comes to practical application, a sinusoidal ripple is simply superposed into the charge/discharge source. The AC voltage and current of the battery is used to calculate the AC impedance, and this allows a fairly accurate estimation of the battery SOC. Final validation using a power analyzer and actual measurement showed the average error of SOC estimation to be within 4%. This encouraging result showed the proposed method can provide a simple and practical solution for online estimation of the SOC of Li-ion batteries.

UR - http://www.scopus.com/inward/record.url?scp=85056253730&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056253730&partnerID=8YFLogxK

U2 - 10.1109/ICICE.2017.8479183

DO - 10.1109/ICICE.2017.8479183

M3 - Conference contribution

AN - SCOPUS:85056253730

T3 - Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017

SP - 53

EP - 56

BT - Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering

A2 - Lam, Artde Donald Kin-Tak

A2 - Prior, Stephen D.

A2 - Meen, Teen-Hang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017

Y2 - 17 November 2017 through 20 November 2017

ER -

Wu SL, Chen HC, Tsai MY, Lin TC, Chen L-R. AC impedance based online state-of-charge estimation for Li-ion battery. In Lam ADK-T, Prior SD, Meen T-H, editors, Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 53-56. 8479183. (Proceedings of the 2017 IEEE International Conference on Information, Communication and Engineering: Information and Innovation for Modern Technology, ICICE 2017). https://doi.org/10.1109/ICICE.2017.8479183