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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Information, Communication and Engineering
Subtitle of host publicationInformation and Innovation for Modern Technology, ICICE 2017
EditorsArtde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages53-56
Number of pages4
ISBN (Electronic)9781538632024
DOIs
Publication statusPublished - 2018 Oct 1
Event2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017 - Xiamen, Fujian, China
Duration: 2017 Nov 172017 Nov 20

Publication series

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

Other

Other2017 IEEE International Conference on Information, Communication and Engineering, ICICE 2017
CountryChina
CityXiamen, Fujian
Period17-11-1717-11-20

Fingerprint

Linear regression
Lithium-ion batteries
Charge
Monitoring
Electric potential
Battery management systems
Management system

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

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).
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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.",
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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 proceedingConference contribution

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

AU - Wu, Shing Lih

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AU - Chen, Liang-Rui

PY - 2018/10/1

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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.

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PB - Institute of Electrical and Electronics Engineers Inc.

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