Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method

Zuolu Wang; Kaibo Lu; Xun Chen; Dong Zhen; Fengshou Gu; Andrew D. Ball

doi:10.1109/ICAC55051.2022.9911094

Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method

Zuolu Wang, Kaibo Lu, Xun Chen, Dong Zhen, Fengshou Gu, Andrew D. Ball

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

4 Citations (Scopus)

Abstract

Lithium-ion batteries have widely used as the power sources of electric vehicles (EVs). Accurate and rapid state of health (SOH) estimation in the battery management system (BMS) plays an essential part in improving the reliability and safety of electric systems. This paper develops an active acoustic emission (AE) sensing technology for nonintrusive and rapid battery SOH estimation. The proposed method takes consideration into the changing internal battery material properties under different levels of degradation. In this method, the power ultrasound is used to propagate into the layered battery and excite different AE events of the battery under different cycles. The AE transducer from the opposite side of the battery can actively sense the elastic waves that reflect the life status. This allows more state information to be captured in a wide frequency band for effective SOH estimation. The results indicate that the RMS of the AE signal can be indicative of battery SOH, and the frequency band 270-300 kHz can provide a more linear SOH estimation under various discharging stages. It is validated that the developed technique can achieve rapid and reliable SOH estimation of lithium-ion batteries.

Original language	English
Title of host publication	2022 27th International Conference on Automation and Computing
Subtitle of host publication	Smart Systems and Manufacturing, ICAC 2022
Editors	Chenguang Yang, Yuchun Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	9781665498074
ISBN (Print)	9781665498081
DOIs	https://doi.org/10.1109/ICAC55051.2022.9911094
Publication status	Published - 10 Oct 2022
Event	27th International Conference on Automation and Computing - Bristol, United Kingdom Duration: 1 Sep 2022 → 3 Sep 2022 Conference number: 27

Conference

Conference	27th International Conference on Automation and Computing
Abbreviated title	ICAC 2022
Country/Territory	United Kingdom
City	Bristol
Period	1/09/22 → 3/09/22

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

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Wang, Z., Lu, K., Chen, X., Zhen, D., Gu, F., & Ball, A. D. (2022). Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method. In C. Yang, & Y. Xu (Eds.), 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022 [9911094] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAC55051.2022.9911094

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title = "Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method",

abstract = "Lithium-ion batteries have widely used as the power sources of electric vehicles (EVs). Accurate and rapid state of health (SOH) estimation in the battery management system (BMS) plays an essential part in improving the reliability and safety of electric systems. This paper develops an active acoustic emission (AE) sensing technology for nonintrusive and rapid battery SOH estimation. The proposed method takes consideration into the changing internal battery material properties under different levels of degradation. In this method, the power ultrasound is used to propagate into the layered battery and excite different AE events of the battery under different cycles. The AE transducer from the opposite side of the battery can actively sense the elastic waves that reflect the life status. This allows more state information to be captured in a wide frequency band for effective SOH estimation. The results indicate that the RMS of the AE signal can be indicative of battery SOH, and the frequency band 270-300 kHz can provide a more linear SOH estimation under various discharging stages. It is validated that the developed technique can achieve rapid and reliable SOH estimation of lithium-ion batteries.",

keywords = "active acoustic emission, Lithium-ion battery, material properties, power ultrasound, state of health",

author = "Zuolu Wang and Kaibo Lu and Xun Chen and Dong Zhen and Fengshou Gu and Ball, {Andrew D.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 27th International Conference on Automation and Computing, ICAC 2022 ; Conference date: 01-09-2022 Through 03-09-2022",

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doi = "10.1109/ICAC55051.2022.9911094",

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Wang, Z, Lu, K, Chen, X, Zhen, D, Gu, F & Ball, AD 2022, Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method. in C Yang & Y Xu (eds), 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022., 9911094, Institute of Electrical and Electronics Engineers Inc., 27th International Conference on Automation and Computing, Bristol, United Kingdom, 1/09/22. https://doi.org/10.1109/ICAC55051.2022.9911094

Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method. / Wang, Zuolu; Lu, Kaibo; Chen, Xun et al.

2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022. ed. / Chenguang Yang; Yuchun Xu. Institute of Electrical and Electronics Engineers Inc., 2022. 9911094.

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

TY - GEN

T1 - Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method

AU - Wang, Zuolu

AU - Lu, Kaibo

AU - Chen, Xun

AU - Zhen, Dong

AU - Gu, Fengshou

AU - Ball, Andrew D.

N1 - Conference code: 27

PY - 2022/10/10

Y1 - 2022/10/10

N2 - Lithium-ion batteries have widely used as the power sources of electric vehicles (EVs). Accurate and rapid state of health (SOH) estimation in the battery management system (BMS) plays an essential part in improving the reliability and safety of electric systems. This paper develops an active acoustic emission (AE) sensing technology for nonintrusive and rapid battery SOH estimation. The proposed method takes consideration into the changing internal battery material properties under different levels of degradation. In this method, the power ultrasound is used to propagate into the layered battery and excite different AE events of the battery under different cycles. The AE transducer from the opposite side of the battery can actively sense the elastic waves that reflect the life status. This allows more state information to be captured in a wide frequency band for effective SOH estimation. The results indicate that the RMS of the AE signal can be indicative of battery SOH, and the frequency band 270-300 kHz can provide a more linear SOH estimation under various discharging stages. It is validated that the developed technique can achieve rapid and reliable SOH estimation of lithium-ion batteries.

AB - Lithium-ion batteries have widely used as the power sources of electric vehicles (EVs). Accurate and rapid state of health (SOH) estimation in the battery management system (BMS) plays an essential part in improving the reliability and safety of electric systems. This paper develops an active acoustic emission (AE) sensing technology for nonintrusive and rapid battery SOH estimation. The proposed method takes consideration into the changing internal battery material properties under different levels of degradation. In this method, the power ultrasound is used to propagate into the layered battery and excite different AE events of the battery under different cycles. The AE transducer from the opposite side of the battery can actively sense the elastic waves that reflect the life status. This allows more state information to be captured in a wide frequency band for effective SOH estimation. The results indicate that the RMS of the AE signal can be indicative of battery SOH, and the frequency band 270-300 kHz can provide a more linear SOH estimation under various discharging stages. It is validated that the developed technique can achieve rapid and reliable SOH estimation of lithium-ion batteries.

KW - active acoustic emission

KW - Lithium-ion battery

KW - material properties

KW - power ultrasound

KW - state of health

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U2 - 10.1109/ICAC55051.2022.9911094

DO - 10.1109/ICAC55051.2022.9911094

M3 - Conference contribution

AN - SCOPUS:85141156527

SN - 9781665498081

BT - 2022 27th International Conference on Automation and Computing

A2 - Yang, Chenguang

A2 - Xu, Yuchun

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 27th International Conference on Automation and Computing

Y2 - 1 September 2022 through 3 September 2022

ER -

Wang Z, Lu K, Chen X, Zhen D, Gu F , Ball AD. Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method. In Yang C, Xu Y, editors, 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. 9911094 doi: 10.1109/ICAC55051.2022.9911094

Rapid State of Health Estimation of Lithium-ion Batteries based on An Active Acoustic Emission Sensing Method

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