By AI Trends Staff
A team of researchers from Stanford University, MIT and the Toyota Research Institute have used AI to dramatically speed up the time required to test and optimally charge batteries for electric vehicles (EVs).
As recently reported in Nature, Stanford professors Stefano Ermon and William Chueh sought ways to charge an EV battery more quickly while maximizing the overall battery life. The study showed how a patented AI program could predict different ways batteries would react to charging methods.
The software also decided in real time what charging approaches to focus on or ignore. The researchers cut the testing process from two years to 16 days by reducing the length and number of trials.
The machine learning system was trained on data of batteries that failed. It was able to detect patterns for predicting how long batteries would last.
This resulted in a new fast-charging protocol, which showed how to optimize battery life. Using AI in battery testing is a new approach, according to the researchers.
“When talking to material scientists and people who work in batteries for a living, we realized that nobody was actually using more sophisticated AI in this space, so we thought it was promising,” stated Ermon, a professor of computer science at Stanford, in an interview published in TechRepublic.
He described the many ways to charge a battery. “You can apply different voltages, different currents, different intensities––they may all charge the battery in the same amount of time, but some might harm the internal components of the battery,” he stated. “Depending on what kind of charging protocol you use, that can significantly affect the life of the battery.”
Major EV manufacturers may take an interest, Ermon predicted.
“We figured out how to greatly accelerate the testing process for extreme fast charging,” stated Peter Attia, who participated in the study as a graduate student, in an interview with SciTechDaily. “What’s really exciting, though, is the method. We can apply this approach to many other problems that, right now, are holding back battery development for months or years.”
“Machine learning is trial-and-error, but in a smarter way,” stated Aditya Grover, a graduate student in computer science who also participated in the study. “Computers are far better than us at figuring out when to explore – try new and different approaches – and when to exploit, or zero in, on the most promising ones.”
Ermon stated, “It gave us this surprisingly simple charging protocol – something we didn’t expect. That’s the difference between a human and a machine: The machine is not biased by human intuition, which is powerful but sometimes misleading.”
Wider Application Seen
The approach has the potential to accelerate every piece of the battery development pipeline, from designing the chemistry of a batter, to determining its size and shape, to finding better systems for manufacturing and storing, the researchers suggested. This has implications not only for EV battery charging but for other types of energy storage, such as for wind and solar power.
“This is a new way of doing battery development,” stated Patrick Herring, a co-author of the study and a scientist at the Toyota Research Institute. “Having data that you can share among a large number of people in academia and industry, and that is automatically analyzed, enables much faster innovation.”
The researchers intend to make the study’s machine learning and data collection system available for future battery scientists to freely use.
Ermon suggested other big data testing problems, from drug development to optimizing the performance of X-rays and lasers, could be revolutionized by the use of machine learning optimization.
Private industry has been working on applying AI to battery charging as well. Researchers at battery company StoreDot have been using machine learning to extend its capabilities, wrote Dr. Doron Myersford, CEO of StoreDot, in a recent account in Engineering and Technology.
“An initial foray into this technique has achieved remarkable results,” he stated, resulting in a decision to dedicate an R&D team to building capabilities in machine learning. The plan is to apply the lessons learned to the company’s next generation of EV batteries. He cautioned, “Ultra-fast charging presents a very complex issue,” involving innovative data science combined with expertise in electrochemistry, cell structure, anodes, cathodes and electrolytes, so more complex conclusions can be reached.
In other battery research efforts, the search is on for new materials that can store more energy than the graphite anode in modern lithium-ion batteries, according to a recent account in Battery Power Online. Rechargeable batteries with lithium metal anodes could represent the ultimate limit in energy density; however, they face major technical and safety hurdles. The high energy density means they are prone to react with other components in a battery cell to break down through large volume changes. They also run the risk of short circuiting, causing rapid heat generation and potential fire or explosion. Research is continuing.