Suggestions of better routes, driving patterns to improve vehicle’s performance
A machine learning algorithm may be able to help you get the most out of your electric car by reducing charging times and prolonging battery life.
The algorithm has been designed by researchers from the University of Cambridge. Better routes and driving patterns suggested by the algorithm could minimise battery degradation and charging times for the batteries.
The research was reported in the journal Nature Communications. The experiments focused on lithium cobalt oxide cells — widely used in rechargeable batteries. However, the method is generalisable across the different types of battery chemistries in electric vehicles.
The algorithm will also improve the safety and reliability of the vehicles, according to the researchers. The team developed a non-invasive way to probe batteries and get a holistic view of battery health.
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These results were then fed into the algorithm that can predict how different driving patterns will affect the future health of the battery. The algorithm could recommend routes that get drivers from point to point in the shortest time without degrading the battery if developed commercially, the researchers suggested.
If developed commercially, the algorithm could be used to recommend routes that get drivers from point to point in the shortest time without degrading the battery, for example, or recommend the fastest way to charge the battery without causing it to degrade.
Be it a smartphone or a car, the health of a battery is far more complex than a single number on a screen, according to the first author, Penelope Jones, from Cambridge’s Cavendish Laboratory. Battery health is a multi-dimensional thing like human health and it can degrade in lots of different ways.
Jones said:
Most methods of monitoring battery health assume that a battery is always used in the same way. It’s the same with electric cars — how you drive will affect how the battery degrades.
A non-invasive probe that sends high-dimensional electrical pulses into a battery and measures the response was developed by the researchers. These responses are bio-markers of the battery’s health. This method is gentle on the battery and doesn’t cause it to degrade any further.
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The electrical signals from the battery were converted into a description of the battery’s state, which was fed into a machine learning algorithm.
The algorithm was able to predict how the battery would respond in the next charge-discharge cycle, depending on how quickly the battery was charged and how fast the car was driven.
Tests with 88 commercial batteries showed that the algorithm did not require any information about the previous usage of the battery to make an accurate prediction.
This method could also be useful for businesses that operate large fleets of electric vehicles, such as logistics companies.
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