Contactless Li-ion battery voltage detection by using Walabot and machine learning

Yanan Wang; Haoyu Niu; Tiebiao Zhao; Xiaozhong Liao; Lei Dong; Yangquan Chen

doi:10.1115/DETC2019-97668

Contactless Li-ion battery voltage detection by using Walabot and machine learning

Yanan Wang
, Haoyu Niu
, Tiebiao Zhao
, Xiaozhong Liao
, Lei Dong
, Yangquan Chen^*

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology
University of California Merced

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

9 Citations (Scopus)

Abstract

This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.

Original language	English
Title of host publication	15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791859292
DOIs	https://doi.org/10.1115/DETC2019-97668
Publication status	Published - 2019
Event	ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019 - Anaheim, United States Duration: 18 Aug 2019 → 21 Aug 2019

Publication series

Name	Proceedings of the ASME Design Engineering Technical Conference
Volume	9

Conference

Conference	ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
Country/Territory	United States
City	Anaheim
Period	18/08/19 → 21/08/19

UN SDGs

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

SDG 7 Affordable and Clean Energy

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10.1115/DETC2019-97668

Cite this

Wang, Y., Niu, H., Zhao, T., Liao, X., Dong, L., & Chen, Y. (2019). Contactless Li-ion battery voltage detection by using Walabot and machine learning. In 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (Proceedings of the ASME Design Engineering Technical Conference; Vol. 9). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC2019-97668

@inproceedings{90b12b875eeb4db08e2644d2a6363729,

title = "Contactless Li-ion battery voltage detection by using Walabot and machine learning",

abstract = "This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.",

author = "Yanan Wang and Haoyu Niu and Tiebiao Zhao and Xiaozhong Liao and Lei Dong and Yangquan Chen",

note = "Publisher Copyright: Copyright {\textcopyright} 2019 ASME.; ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019 ; Conference date: 18-08-2019 Through 21-08-2019",

year = "2019",

doi = "10.1115/DETC2019-97668",

language = "English",

series = "Proceedings of the ASME Design Engineering Technical Conference",

publisher = "American Society of Mechanical Engineers (ASME)",

booktitle = "15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications",

address = "United States",

}

Wang, Y, Niu, H, Zhao, T, Liao, X, Dong, L & Chen, Y 2019, Contactless Li-ion battery voltage detection by using Walabot and machine learning. in 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications. Proceedings of the ASME Design Engineering Technical Conference, vol. 9, American Society of Mechanical Engineers (ASME), ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019, Anaheim, United States, 18/08/19. https://doi.org/10.1115/DETC2019-97668

Contactless Li-ion battery voltage detection by using Walabot and machine learning. / Wang, Yanan; Niu, Haoyu; Zhao, Tiebiao et al.
15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications. American Society of Mechanical Engineers (ASME), 2019. (Proceedings of the ASME Design Engineering Technical Conference; Vol. 9).

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

TY - GEN

T1 - Contactless Li-ion battery voltage detection by using Walabot and machine learning

AU - Wang, Yanan

AU - Niu, Haoyu

AU - Zhao, Tiebiao

AU - Liao, Xiaozhong

AU - Dong, Lei

AU - Chen, Yangquan

PY - 2019

Y1 - 2019

N2 - This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.

AB - This paper has proposed a contactless voltage classification method for Lithium-ion batteries (LIBs). With a three-dimensional radio-frequency based sensor called Walabot, voltage data of LIBs can be collected in a contactless way. Then three machine learning algorithm, that is, principal component analysis (PCA), linear discriminant analysis (LDA), and stochastic gradient descent (SGD) classifiers, have been employed for data processing. Experiments and comparison have been conducted to verify the proposed method. The colormaps of results and prediction accuracy show that LDA may be most suitable for LIBs voltage classification.

UR - https://www.scopus.com/pages/publications/85076416354

U2 - 10.1115/DETC2019-97668

DO - 10.1115/DETC2019-97668

M3 - Conference contribution

AN - SCOPUS:85076416354

T3 - Proceedings of the ASME Design Engineering Technical Conference

BT - 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications

PB - American Society of Mechanical Engineers (ASME)

T2 - ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019

Y2 - 18 August 2019 through 21 August 2019

ER -

Wang Y, Niu H, Zhao T, Liao X, Dong L, Chen Y. Contactless Li-ion battery voltage detection by using Walabot and machine learning. In 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications. American Society of Mechanical Engineers (ASME). 2019. (Proceedings of the ASME Design Engineering Technical Conference). doi: 10.1115/DETC2019-97668

Contactless Li-ion battery voltage detection by using Walabot and machine learning

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