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Machine learning pipeline for battery state-of-health estimation

Nature Machine Intelligence volume 3, pages 447–456 (2021)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Lithium-ion batteries are ubiquitous in applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this Article, we design and evaluate a machine learning pipeline for estimation of battery capacity fade—a metric of battery health—on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root-mean-squared error of 0.45%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasizing the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and could be applied to other critical components that require real-time estimation of SOH.

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Fig. 1: The constant current−constant voltage (CC−CV) charge protocol and extracted ageing segment of the curves for a Li-ion pouch cell.
Fig. 2: Prediction results with dNNe Group I cell number 38.
Fig. 3
Fig. 4: Prediction results with dNNe Group II cell number 1.
Fig. 5: Prediction results with dNNe Group III cell no. 5.

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Data availability

The datasets used in this study are available at, for Group 1, https://web.calce.umd.edu/batteries/data.htm and https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/, for Group 2, https://data.matr.io/1/projects/5c48dd2bc625d700019f3204, and for Group 3, https://ora.ox.ac.uk/objects/uuid:03ba4b01-cfed-46d3-9b1a-7d4a7bdf6fac.

Code availability

Code for the data processing is available from the corresponding authors upon request. Code for the modelling work is available at https://doi.org/10.5281/zenodo.4390152.

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Acknowledgements

This work was supported by the Lloyd’s Register Foundation (grant number AtRI_100015), The Engineering and Physical Sciences Research Council (EPSRC), the Center for Doctoral Training in Embedded Intelligence, and Baker Hughes (grant number EP/L014998/1). The work was further supported by the EPSRC through the UK National Centre for Energy Systems Integration (CESI) (grant number EP/P001173/1), and by InnovateUK through the Responsive Flexibility (ReFlex) (project reference 104780). We thank the more than 150 companies and organizations that support research activities at the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland annually.

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Author notes

  1. Saurabh Saxena

    Present address: Argonne National Laboratory, Lemont, USA

  2. Valentin Robu

    Present address: Intelligent and Autonomous Systems Group, CWI, National Research Centre for Mathematics and Computer Science, Amsterdam, the Netherlands

Authors and Affiliations

  1. Smart Systems Group, School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, UK

    Darius Roman, Valentin Robu & David Flynn

  2. Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD, USA

    Saurabh Saxena & Michael Pecht

  3. Algorithmics Group, TU Delft, Delft, the Netherlands

    Valentin Robu

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Contributions

D.R. conceived the study, analysed the experimental data, developed the machine learning pipeline and wrote the paper, while S.S. assisted with experimental data interpretation, problem statement formulation, and feature engineering. V.R. provided technical input for the machine learning method development, while D.F. and M.P. provided input for the battery SOH application. V.R., M.P. and D.F. supervised the work. All authors commented on and reviewed the manuscript.

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Correspondence to Darius Roman.

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Roman, D., Saxena, S., Robu, V. et al. Machine learning pipeline for battery state-of-health estimation. Nat Mach Intell 3, 447–456 (2021). https://doi.org/10.1038/s42256-021-00312-3

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