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Machine-learning techniques used to accurately predict battery life

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  • Maitane Berecibar

Abstract

Highly reliable methods for predicting battery lives are needed to develop safe, long-lasting battery systems. Accurate predictive models have been developed using data collected from batteries early in their lifetime.

Suggested Citation

  • Maitane Berecibar, 2019. "Machine-learning techniques used to accurately predict battery life," Nature, Nature, vol. 568(7752), pages 325-326, April.
    • Handle: RePEc:nat:nature:v:568:y:2019:i:7752:d:10.1038_d41586-019-01138-1
    DOI: 10.1038/d41586-019-01138-1
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    Citations

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    Cited by:

    1. Fujin Wang & Zhi Zhai & Zhibin Zhao & Yi Di & Xuefeng Chen, 2024. "Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Dapai Shi & Jingyuan Zhao & Zhenghong Wang & Heng Zhao & Chika Eze & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health," Energies, MDPI, vol. 16(9), pages 1-19, April.
    3. Yanling Zheng & Liping Zhang & XiXun Zhu & Gang Guo, 2020. "A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-12, June.
    4. Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
    5. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.

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    Keywords

    Energy; Mathematics and computing;

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