Electric vehicle (EV) batteries are an important component of EVs because they store the energy that powers the vehicle. As such, it is important to get the most out of the battery in terms of both its energy capacity and its lifespan. One way to do this is by using machine learning algorithms to predict how to optimize the use of the battery.
By anticipating how various driving behaviors will affect battery performance, researchers have created a machine learning system that could assist shorten charging times and increase battery life in electric vehicles, enhancing reliability and safety.
By suggesting routes and driving patterns that reduce battery degradation and charging times, the researchers from the University of Cambridge claim that their algorithm could help consumers, manufacturers, and businesses get the most out of the batteries that power electric vehicles.
The researchers created a non-invasive technique for probing batteries and obtaining a comprehensive assessment of battery condition. These outcomes were subsequently incorporated into a machine learning algorithm that can forecast how various driving styles will impact the battery’s long-term health.
If commercially developed, the algorithm might be used to suggest routes that get drivers from point A to point B in the least amount of time without wearing out the battery, or it could suggest the quickest method to charge the battery without wearing it out. The journal Nature Communications reports the findings.
A battery’s condition is much more complicated than a single number displayed on a screen, whether it be in a car or a smartphone.
“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,” said Dr. Alpha Lee, who led the research. “Battery capacity can change drastically over that time, so we wanted to come up with a better way of checking battery health.”
It’s been such an exciting framework to build, because it could solve so many of the challenges in the battery field today. It’s a great time to be involved in the field of battery research, which is so important in helping address climate change by transitioning away from fossil fuels.
Penelope Jones
The scientists created a non-invasive sensor that transmits electrical pulses of high dimensions into batteries and monitors the response to provide a number of “biomarkers” of battery health. This approach doesn’t hasten the battery’s deterioration and is kind to it.
A machine learning system was fed a description of the battery’s status created from the electrical signals from the battery. Depending on how rapidly the battery was charged and how fast the car would be moving the next time it was on the road, the computer was able to forecast how the battery will behave in the subsequent charge-discharge cycle.
Tests on 88 commercial batteries revealed that the system was capable of making accurate predictions without needing any knowledge of the battery’s prior history.
The technology can be applied to the many battery chemistries now used in electric vehicles, however the experiment concentrated on lithium cobalt oxide (LCO) cells, which are frequently used in rechargeable batteries.
“This method could unlock value in so many parts of the supply chain, whether you’re a manufacturer, an end user, or a recycler, because it allows to capture the health of the battery beyond a single number, and because it’s predictive,” said Lee. “It could reduce the time it takes to develop new type of batteries, because we’ll be able to predict how they will degrade under different operating conditions.”
The researchers claim that in addition to manufacturers and drivers, organizations that run sizable fleets of electric vehicles, such logistics firms, may find their strategy valuable.
“The framework we’ve developed could help companies optimise how they use their vehicles to improve the overall battery life of the fleet,” said Lee. “There’s so much potential with a framework like this.”
“It’s been such an exciting framework to build, because it could solve so many of the challenges in the battery field today,” said Jones. “It’s a great time to be involved in the field of battery research, which is so important in helping address climate change by transitioning away from fossil fuels.”
To hasten the creation of next-generation batteries that are safer and last longer, researchers are currently collaborating with battery producers. In order to shorten the time it takes to charge electric vehicles without compromising quality, they are also investigating how their framework might be utilized to create the best fast charging protocols.
The Winton Programme supported the research for the Physics of Sustainability, the Ernest Oppenheimer Fund, the Alan Turing Institute and the Royal Society.
In addition to predicting the optimal charging rate, machine learning algorithms can also be used to predict other factors that can affect the performance of an EV battery, such as the best ambient temperature for charging and the most effective way to discharge the battery. By using machine learning to optimize the use of the battery, it is possible to get the most out of it and extend its lifespan.
