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^*

^*此作品的通讯作者

自动化学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

8 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications
出版商	American Society of Mechanical Engineers (ASME)
ISBN（电子版）	9780791859292
DOI	https://doi.org/10.1115/DETC2019-97668
出版状态	已出版 - 2019
活动	ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019 - Anaheim, 美国期限: 18 8月 2019 → 21 8月 2019

出版系列

姓名	Proceedings of the ASME Design Engineering Technical Conference
卷	9

会议

会议	ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
国家/地区	美国
市	Anaheim
时期	18/08/19 → 21/08/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.1115/DETC2019-97668

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (Proceedings of the ASME Design Engineering Technical Conference; 卷 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. 在 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications. Proceedings of the ASME Design Engineering Technical Conference, 卷 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, 美国, 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 等.
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; 卷 9).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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 - http://www.scopus.com/inward/record.url?scp=85076416354&partnerID=8YFLogxK

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. 在 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

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此