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Machine learning pipeline for battery state-of-health estimation

Open access
Roman, Darius, Saxena, Saurabh, Robu, Valentin, Pecht, Michael, Flynn, David·2021·Nature Machine Intelligence
DOI 10.1038/s42256-021-00312-3
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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. Rechargeable lithium-ion batteries play a crucial role in many modern-day applications, including portable electronics and electric vehicles, but they degrade over time. To ensure safe operation, a battery’s ‘state of health’ should be monitored in real time, and this machine learning pipeline, tested on a variety of charging conditions, can provide such an online estimation of battery state of health.

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Metadata

Authors
Roman, D., Saxena, S., Robu, V., Pecht, M., & Flynn, D.
Venue
Nature Machine Intelligence
Publisher
Springer Science and Business Media LLC
Published
2021-04-05
Volume
3
Issue
5
Pages
447-456
ISSN
2522-5839 (ISSN-L)
DOI
10.1038/s42256-021-00312-3
OpenAlex
W3126452185
Cited by
660
References
24
FWCI
28.94
Type
journal article
Language
en
Open access
green
Licence
Funders
  • RCUK | Engineering and Physical Sciences Research Council (EP/L014998/1, EP/P001173/1)
  • Innovate UK (ref:104780)
  • Lloyd’s Register Foundation (AtRI 100015)

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Roman, D., Saxena, S., Robu, V., Pecht, M., & Flynn, D. (2021). Machine learning pipeline for battery state-of-health estimation. Nature Machine Intelligence. https://doi.org/10.1038/s42256-021-00312-3

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