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Anomaly Detection and Extra Tree Regression for Assessment of the Remaining Useful Life of Lithium-Ion Battery

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Book cover Advanced Information Networking and Applications (AINA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1151))

Abstract

The knowledge of the Remaining Useful Life (RUL) of Lithium-ion (Li+) battery is significant in battery management and helps in designing numerous fit-for-purpose systems. We implemented a segmentation-type anomaly detection to establish the changing characteristics of Li+ battery by using the measured voltage and temperature at different timesteps. Hence extracting useful changepoint features of the voltage and temperature transitions such as mean, variance, skewness, kurtosis and voltage for predicting the RUL of the battery with Extra Tree Regression (ETR) algorithm. The model was predicted to an accuracy of 96.25%–97.86% when the Mean Absolute Percentage Error (MAPE) of three Li+ batteries were tested. The robustness of this study makes it a very useful technique for Li+ battery prognosis, design and uncertainty estimation of the performance of critical systems depending on the Li+ battery power.

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Author information

Authors and Affiliations

  1. Faculty of Health, Arts and Design, School of Health Sciences, Department of Health and Medical Sciences, Swinburne University, John Street, Hawthorn, VIC, 3122, Australia

    Chinedu I. Ossai

  2. College of Engineering and Science, Victoria University Sydney, 160 Sussex Street, Sydney, NSW, Australia

    Ifeanyi P. Egwutuoha

Authors
  1. Chinedu I. Ossai
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  2. Ifeanyi P. Egwutuoha
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Corresponding author

Correspondence to Chinedu I. Ossai .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Electrical Engineering and Information Technology, University of Naples “Frederico II”, Naples, Italy

    Flora Amato

  3. Department of Political Science, University of Campania “Luigi Vanvitelli”, Caserta, Italy

    Francesco Moscato

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  5. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, Tokyo, Japan

    Makoto Takizawa

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Ossai, C.I., Egwutuoha, I.P. (2020). Anomaly Detection and Extra Tree Regression for Assessment of the Remaining Useful Life of Lithium-Ion Battery. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_124

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