Machine learning offers EV charge time and battery life hack

A new algorithm can help electric vehicle (EV) drivers get the most out of their ride, reducing running costs and increasing maximum range.

Researchers at the University of Cambridge have devised a machine learning algorithm that can help bring EV charge times down, while also extending battery life. 

The programme essentially predicts how different driving styles and patterns affect battery performance, while also suggesting routes that minimise hardware degradation, in turn improving the safety and reliability of EV models. Those behind the work believe this will prove useful to drivers, manufacturers, and businesses retaining fleets of owned or leased vehicles. 

‘Battery health, like human health, is a multi-dimensional thing, and it can degrade in lots of different ways,’ said first author Penelope Jones, from Cambridge’s Cavendish Laboratory. ‘Most methods of monitoring battery health assume that a battery is always used in the same way. But that’s not how we use batteries in real life. If I’m streaming a TV show on my phone, it’s going to run down the battery a whole lot faster than if I’m using it for messaging. It’s the same with electric cars – how you drive will affect how the battery degrades.’

‘Most of us will replace our phones well before the battery degrades to the point that it’s unusable, but for cars, the batteries need to last for five, ten years or more… Battery capacity can change drastically over that time, so we wanted to come up with a better way of checking battery health,’ added Dr Alpha Lee. 

Want to know why lithium-ion batteries degrade over time? Find out in this long-read by Beatrice Browning at the Faraday Institution. 

Image credit: Markus Spiske