Artificial intelligence could help to create long-lasting electric vehicle (EV) batteries that charge faster.
William Chueh at Stanford University and his colleagues have developed an AI that optimises EV battery recharging while also maximising the battery鈥檚 lifespan.
鈥淚f you want to charge a battery quickly there is an infinite number of ways you can do so,鈥� says Chueh. Standard EV batteries tend to be recharged quickly at first, and then more slowly, for example.
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How a battery is recharged is crucial because a sub-optimal charging pattern has the potential to significantly reduce a battery鈥檚 lifetime, but reducing charging time is also important.
While a gasoline car takes only a few minutes to refuel, electric cars can take between 45 minutes and 2 hours to fully charge 鈥� and decreasing this time would improve the driving experience for electric vehicle users, says Chueh.
The researchers set out to explore how best to recharge an EV battery to 80 per cent within 10 minutes. They trained an AI on existing information from 41 EV batteries that had been used to the point of failure, which took about 1000 charge cycles.
The AI was only given data from the first 100 cycles of these batteries, and needed to find early indicators to predict how long a battery would last for. It achieved a close correlation between the predicted and actual battery performance. The team then used the AI to identify which of 224 potential battery charging patterns was most efficient.
It took the AI 16 days to determine which charge patterns were most efficient. Given that the battery in an electric vehicle is supposed to last up to 1000 charge cycles, or around 10 years of regular driving, evaluating how charging patterns affect battery lifetime was previously a lengthy process: fully exhausting a battery in testing took more than 500 days.
The research was done in collaboration with car manufacturer Toyota and the team is now in the process of scaling up the project, testing different charging patterns on a greater number of batteries.
Journal reference: Nature,
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