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. Ordinarily, AI would need data from after a battery had started to degrade in order to predict how it would perform in the future. It might take months to cycle the battery enough times to get that data. But the researchers’ AI could predict lifetime performance after only hours of data collection, while the battery was still at its peak. “Prior to our work, nobody thought that was possible,” says William Chueh, a materials scientist at Stanford and one of the lead authors of the 2019 paper. And earlier this year, Chueh and his colleagues did it again. In a paper published in Nature in February, Chueh and his colleagues described an experiment in which an AI was able to discover the optimal method for 10-minute fast-charging a lithium-ion battery.
Many experts think fast-charging batteries will be critical for electric vehicle adoption, but dumping enough energy to recharge a cell in the same amount of time it takes to fill up a tank of gas can quickly kill its performance. To get fast-charging batteries out of the lab and into the real world means finding the sweet spot between charge speed and battery lifetime. The problem is that there is effectively an infinite number of ways to deliver charge to a battery; Chueh compares it to searching for the best way to pour water into a bucket. Experimentally sifting through all those possibilities to find the best one is a slow and arduous task—but that’s where AI can help.
In their research, Chueh and his colleagues managed to optimize a fast-charging protocol for a lithium-ion battery in less than a month; to achieve those same results without the aid of AI would usually take around two years. “At the end of the day, we see our job as accelerating the pace of battery R&D,” says Chueh. “Whether it’s discovering new chemistry or finding a way to make a safer battery, it’s all very time consuming. We’re trying to save time.”
Over the past decade or so, the performance of batteries has skyrocketed and their cost has plummeted. Given that many experts see the electrification of everything as key to decarbonizing our energy systems, this is good news. But for researchers like Chueh, the pace of battery innovation isn’t happening fast enough. The reason is simple: batteries are extremely complex. To build a better battery means ruthlessly optimizing at every step in the production process. It’s all about using less expensive raw materials, better chemistry, more efficient manufacturing techniques. But there are a lot of parameters that can be optimized. And often an improvement in one area—say, energy density—will come at a cost of making gains in another area, like charge rate.