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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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 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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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.
The authors declare no competing interests.
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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
Energy and AI (2021)