Researchers Develop New Machine Learning Method to Estimate Battery Health
Written by AZoRoboticsApr 12 2021
Electrical batteries are increasingly crucial in a variety of applications, from integration of intermittent energy sources with demand, to unlocking carbon-free power for the transportation sector through electric vehicles (EVs), trains and ships, to a host of advanced electronics and robotic applications.
A key challenge however is that batteries degrade quickly with operating conditions. It is currently difficult to estimate battery health without interrupting the operation of the battery or without going through a lengthy procedure of charge-discharge that requires specialised equipment.
In work recently published by
Researchers at Edinburgh’s Heriot-Watt University have developed an AI framework to accurately predict battery health using simple voltage and current data.
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 engin
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