Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing

Akzhol Baktiyar; Young Nam Lee; Min Jae Jung; Sang Gug Lee; Kyung Sik Choi

doi:10.1109/IECON51785.2023.10311708

Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing

Akzhol Baktiyar
, Young Nam Lee
, Min Jae Jung
, Sang Gug Lee
, Kyung Sik Choi

Electronic Engineering Program

Korea Advanced Institute of Science and Technology

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

Abstract

This paper proposes the application of compressive sensing (CS) to reduce the measurement time in electrochemical impedance spectroscopy (EIS) for lithium-ion batteries. EIS is a non-destructive frequency response technique that provides valuable information on the state and degradation mechanisms occurring inside a battery. However, EIS measurement time is lengthy, making it impractical for evaluating the state of operating cells. CS is a signal-processing technique that enables the efficient acquisition and reconstruction of signals from a reduced number of measurements. The study aims to identify a suitable transform domain using dictionary learning that facilitates the adoption of CS techniques for the compression of the EIS data obtained from lithium-ion batteries. Thanks to the reduced number of EIS measurements, the proposed CS-based EIS achieves approximately 40% reduction in measurement time for open-source and in-house collected data, respectively, with minimal accuracy degradation.

Original language	English
Title of host publication	IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350331820
DOIs	https://doi.org/10.1109/IECON51785.2023.10311708
State	Published - 2023
Event	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore Duration: 16 Oct 2023 → 19 Oct 2023

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/Territory	Singapore
City	Singapore
Period	16/10/23 → 19/10/23

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Electrochemical impedance spectroscopy
compressive sensing
dictionary learning
lithium-ion batteries

Access to Document

10.1109/IECON51785.2023.10311708

Cite this

Baktiyar, A., Lee, Y. N., Jung, M. J., Lee, S. G., & Choi, K. S. (2023). Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing. In IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference)). IEEE Computer Society. https://doi.org/10.1109/IECON51785.2023.10311708

@inproceedings{9041032265de4d6d9e3c82638e655831,

title = "Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing",

abstract = "This paper proposes the application of compressive sensing (CS) to reduce the measurement time in electrochemical impedance spectroscopy (EIS) for lithium-ion batteries. EIS is a non-destructive frequency response technique that provides valuable information on the state and degradation mechanisms occurring inside a battery. However, EIS measurement time is lengthy, making it impractical for evaluating the state of operating cells. CS is a signal-processing technique that enables the efficient acquisition and reconstruction of signals from a reduced number of measurements. The study aims to identify a suitable transform domain using dictionary learning that facilitates the adoption of CS techniques for the compression of the EIS data obtained from lithium-ion batteries. Thanks to the reduced number of EIS measurements, the proposed CS-based EIS achieves approximately 40\% reduction in measurement time for open-source and in-house collected data, respectively, with minimal accuracy degradation.",

keywords = "Electrochemical impedance spectroscopy, compressive sensing, dictionary learning, lithium-ion batteries",

author = "Akzhol Baktiyar and Lee, \{Young Nam\} and Jung, \{Min Jae\} and Lee, \{Sang Gug\} and Choi, \{Kyung Sik\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 ; Conference date: 16-10-2023 Through 19-10-2023",

year = "2023",

doi = "10.1109/IECON51785.2023.10311708",

language = "English",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

booktitle = "IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society",

}

Baktiyar, A, Lee, YN, Jung, MJ, Lee, SG & Choi, KS 2023, Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing. in IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), IEEE Computer Society, 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023, Singapore, Singapore, 16/10/23. https://doi.org/10.1109/IECON51785.2023.10311708

Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing. / Baktiyar, Akzhol; Lee, Young Nam; Jung, Min Jae et al.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2023. (IECON Proceedings (Industrial Electronics Conference)).

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

TY - GEN

T1 - Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing

AU - Baktiyar, Akzhol

AU - Lee, Young Nam

AU - Jung, Min Jae

AU - Lee, Sang Gug

AU - Choi, Kyung Sik

PY - 2023

Y1 - 2023

N2 - This paper proposes the application of compressive sensing (CS) to reduce the measurement time in electrochemical impedance spectroscopy (EIS) for lithium-ion batteries. EIS is a non-destructive frequency response technique that provides valuable information on the state and degradation mechanisms occurring inside a battery. However, EIS measurement time is lengthy, making it impractical for evaluating the state of operating cells. CS is a signal-processing technique that enables the efficient acquisition and reconstruction of signals from a reduced number of measurements. The study aims to identify a suitable transform domain using dictionary learning that facilitates the adoption of CS techniques for the compression of the EIS data obtained from lithium-ion batteries. Thanks to the reduced number of EIS measurements, the proposed CS-based EIS achieves approximately 40% reduction in measurement time for open-source and in-house collected data, respectively, with minimal accuracy degradation.

AB - This paper proposes the application of compressive sensing (CS) to reduce the measurement time in electrochemical impedance spectroscopy (EIS) for lithium-ion batteries. EIS is a non-destructive frequency response technique that provides valuable information on the state and degradation mechanisms occurring inside a battery. However, EIS measurement time is lengthy, making it impractical for evaluating the state of operating cells. CS is a signal-processing technique that enables the efficient acquisition and reconstruction of signals from a reduced number of measurements. The study aims to identify a suitable transform domain using dictionary learning that facilitates the adoption of CS techniques for the compression of the EIS data obtained from lithium-ion batteries. Thanks to the reduced number of EIS measurements, the proposed CS-based EIS achieves approximately 40% reduction in measurement time for open-source and in-house collected data, respectively, with minimal accuracy degradation.

KW - Electrochemical impedance spectroscopy

KW - compressive sensing

KW - dictionary learning

KW - lithium-ion batteries

UR - https://www.scopus.com/pages/publications/85179503856

U2 - 10.1109/IECON51785.2023.10311708

DO - 10.1109/IECON51785.2023.10311708

M3 - Conference contribution

AN - SCOPUS:85179503856

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

Y2 - 16 October 2023 through 19 October 2023

ER -

Baktiyar A, Lee YN, Jung MJ, Lee SG, Choi KS. Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing. In IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society. 2023. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON51785.2023.10311708

Reduction of Electrochemical Impedance Spectroscopy Measurement Time for Lithium-ion Batteries Based on Compressive Sensing

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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