New machine learning method accurately predicts battery state of health
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.