Researchers from the Smart Systems Group at Heriot-Watt University in Edinburgh, UK, working together with researchers from the CALCE group at the University of Maryland in the US, have developed a new method to estimate battery health irrespective of operating conditions and battery design or chemistry, by feeding artificial intelligence (AI) algorithms with the raw battery voltage and current operational data.

A paper describing the method is published in the journal Nature Machine Intelligence.

In the reported study, the team designed and evaluated 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 state of health (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%.

To date, the progress of data-driven models for battery degradation relies on the development of algorithms that carry out inference faster. Whilst researchers often spend a considerable amount of time on model or algorithm development, very few people take the time to understand the engineering context in which the algorithms are applied. By contrast, our work is built from the ground up. We first understand battery degradation through collaborations with the CALCE group at the University of Maryland, where in-house degradation testing of batteries was carried out. We then concentrate on the data, where we engineer features that capture battery degradation, we select the most important features and only then we deploy the AI techniques to estimate battery health.

—Darius Roman, corresponding author

In addition, the researchers found that current data-driven models for battery health estimation do not consider model confidence. However, this is often critical for decision making to understand how the AI model came to a certain conclusion and whether the model can be trusted. In their work, the proposed AI model is capable of quantifying uncertainty in its predictions to better support operating decisions.

The developed framework scales up with new chemistries, including the new upcoming solid-state batteries, battery designs and operating conditions and has the potential to unlock new strategies of how batteries can and should be used.

This research was supported by the EPSRC Centre for Doctoral Training in Embedded Intelligence, the UK Robotics and Artificial Intelligence Hub for Offshore Energy Asset Integrity Management (ORCA Hub) and Responsive Flexibility (ReFlex), one of UK’s largest smart energy demonstration projects, based on the Orkney Islands in the UK.

Resources

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