Inside a lab at Stanford University's Precourt Institute for Energy, there are a half dozen refrigerator-sized cabinets designed to kill batteries as fast as they can. Each holds around 100 lithium-ion cells secured in trays that can charge and discharge the batteries dozens of times per day. Ordinarily, the batteries that go into these electrochemical torture chambers would be found inside gadgets or electric vehicles, but when they're put in these hulking machines, they aren't powering anything at all. Instead, energy is dumped in and out of these cells as fast as possible to generate reams of performance data that will teach artificial intelligence how to build a better battery. In 2019, a team of researchers from Stanford, MIT, and the Toyota Research Institute used AI trained on data generated from these machines to predict the performance of lithium-ion batteries over the lifetime of the cells before their performance had started to slip.

Startup Cuberg is working on developing lighter, safer, more energy-dense batteries, and they're using a machine learning platform developed by Aionics Technologies to do it faster. "The exciting thing we do is make batteries that are very energy dense. They are much lighter than lithium ion batteries but they have much more energy in them," said Olivia Risset, PhD, senior scientist at Cuberg. The batteries that Cuberg makes are safer than lithium ion batteries because the liquid component, the electrolyte, is nonflammable as opposed to what has been used traditionally in lithium ion batteries. "Because of that," says Risset, "electric aviation is a great place for us because they are very sensitive to weight, but also to safety."

Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology. For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times. At every stage of the battery development process, new technologies must be tested for months or even years to determine how long they will last. But now, a team led by Stanford professors Stefano Ermon and William Chueh has developed a machine learning-based method that slashes these testing times by 98 percent.

In this feature, Dr Doron Myersdorf, CEO of StoreDot, explores how AI might just hold the key to solving these issues, and the potential of this approach for the future of energy storage and EVs. By 2030, it is expected that the total sales of electric vehicles (EV) worldwide will surpass 30 million, and the cost parameters will finally come in line with the internal combustion engine. The EV represents a seismic shift in the automotive industry, and its potential impact on business and the environment is compelling. Today, the debates about adoption and longevity of EVs is rife and none more so than the potential performance of EV batteries. For scientists, many challenges still remain - from battery safety, to energy density, charging capabilities, and their performance within a car.