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Predicting the state of charge and health of batteries using data-driven machine learning

Nature Machine Intelligence volume 2, pages 161–170 (2020)Cite this article

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An Author Correction to this article was published on 12 June 2020

Abstract

Machine learning is a specific application of artificial intelligence that allows computers to learn and improve from data and experience via sets of algorithms, without the need for reprogramming. In the field of energy storage, machine learning has recently emerged as a promising modelling approach to determine the state of charge, state of health and remaining useful life of batteries. First, we review the two most studied types of battery models in the literature for battery state prediction: the equivalent circuit and physics-based models. Based on the current limitations of these models, we showcase the promise of various machine learning techniques for fast and accurate battery state prediction. Finally, we highlight the major challenges involved, especially in accurate modelling over length and time, performing in situ calculations and high-throughput data generation. Overall, this work provides insights into real-time, explainable machine learning for battery production, management and optimization in the future.

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Fig. 1: A machine learning approach for SOC, SOH and RUL predictions of Li-ion batteries.
Fig. 2: Accuracy versus CPU time for ECM, SPM and P2D model.
Fig. 3: High-throughput battery fabrication and testing.

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Acknowledgements

This work was supported by the Singapore National Research Foundation (NRF-NRFF2017-04).

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Authors and Affiliations

  1. Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Man-Fai Ng

  2. School of Materials Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Jin Zhao & Qingyu Yan

  3. Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Gareth J. Conduit

  4. Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Zhi Wei Seh

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  3. Qingyu Yan
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  5. Zhi Wei Seh
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This work was written through the contributions of all authors.

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Correspondence to Qingyu Yan, Gareth J. Conduit or Zhi Wei Seh.

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G.J.C. declares a potential financial conflict of interest as chief technology officer of Intellegens, UK.

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Ng, MF., Zhao, J., Yan, Q. et al. Predicting the state of charge and health of batteries using data-driven machine learning. Nat Mach Intell 2, 161–170 (2020). https://doi.org/10.1038/s42256-020-0156-7

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