Predicting the degradation of li-ion battery using advanced machine learning techniques

Yi Ru Li, Kuan Jung Chung

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

Abstract

In this paper, advanced machine learning techniques called ensemble learning process (ELP) are proposed to predict the performance degradation of Li-ion batteries. The ELP is to use a variety of machine learning, Artificial Intelligence, and statistical modeling techniques and approaches to achieve the best results. Prediction quality and accuracy are quantified by the Pearson Correlation and Root-Mean-Square-Error (RMSE) respectively. A commercial software named ThingWorx Analytics (PTC) was applied to the study of predicting the capacity loss of Li-ion batteries using data from a dual dynamic stress accelerated degradation test (D2SADT). The results show that the proposed algorithms are able to precisely predicting the capacity loss of batteries in terms of a low root-mean-square error (RMSE = 0.01) and a high Pearson Correlation (0.99) for one step ahead prediction. Furthermore, the ensemble learning process confirms the good performance in the two steps ahead (long-term) prediction of degradation resulting with the only little increase of RMSE (from 0.01 to 0.02) and maintaining the same Pearson Correlation (0.99).

Original languageEnglish
Title of host publicationIMPACT 2017 - 12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceedings
PublisherIEEE Computer Society
Pages258-262
Number of pages5
Volume2017-October
ISBN (Electronic)9781538647196
DOIs
Publication statusPublished - 2018 Jan 12
Event12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, IMPACT 2017 - Taipei, Taiwan
Duration: 2017 Oct 252017 Oct 27

Other

Other12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, IMPACT 2017
CountryTaiwan
CityTaipei
Period17-10-2517-10-27

Fingerprint

Mean square error
Learning systems
Degradation
Artificial intelligence
Lithium-ion batteries

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Li, Y. R., & Chung, K. J. (2018). Predicting the degradation of li-ion battery using advanced machine learning techniques. In IMPACT 2017 - 12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceedings (Vol. 2017-October, pp. 258-262). IEEE Computer Society. https://doi.org/10.1109/IMPACT.2017.8255938
Li, Yi Ru ; Chung, Kuan Jung. / Predicting the degradation of li-ion battery using advanced machine learning techniques. IMPACT 2017 - 12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceedings. Vol. 2017-October IEEE Computer Society, 2018. pp. 258-262
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Li, YR & Chung, KJ 2018, Predicting the degradation of li-ion battery using advanced machine learning techniques. in IMPACT 2017 - 12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceedings. vol. 2017-October, IEEE Computer Society, pp. 258-262, 12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, IMPACT 2017, Taipei, Taiwan, 17-10-25. https://doi.org/10.1109/IMPACT.2017.8255938

Predicting the degradation of li-ion battery using advanced machine learning techniques. / Li, Yi Ru; Chung, Kuan Jung.

IMPACT 2017 - 12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceedings. Vol. 2017-October IEEE Computer Society, 2018. p. 258-262.

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

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Li YR, Chung KJ. Predicting the degradation of li-ion battery using advanced machine learning techniques. In IMPACT 2017 - 12th International Microsystems, Packaging, Assembly and Circuits Technology Conference, Proceedings. Vol. 2017-October. IEEE Computer Society. 2018. p. 258-262 https://doi.org/10.1109/IMPACT.2017.8255938