
A group of researchers from Stanford University, MIT and the Toyota Research Institute have used AI to radically accelerate the time necessary to test and optimally charge batteries for electric vehicles (EVs).

As recently reported in Nature, Stanford academics Stefano Ermon and William Chueh sought ways to charge an EV battery more quickly while maximizing the total battery life. The analysis demonstrated a AI program could predict to charging methods, ways batteries would respond.

The software also determined in time what charging approaches ignore or to concentrate on. By reducing the amount and length of trials the testing procedure cuts from two years.

The machine learning system has been trained on data. It was able to discover patterns for predicting batteries could last.

This resulted. Using AI in battery testing is a new strategy, according to the researchers.

“When speaking to material scientists and individuals working in batteries for a living, we realized that nobody was really using more complex AI in this area, so we thought it was promising,” said Ermon, a professor of computer science at Stanford, in an interview printed in TechRepublic.

He explained the ways to control battery. “You can apply various voltages, different currents, different intensities–they may all charge the battery at exactly the identical period of time, but a few may damage the internal components of the battery,” he said. “Depending on what sort of charging protocol you use, that can significantly influence the life span of the battery.”

An interest may be taken by Leading EV manufacturers, Ermon predicted.

“We figured out the way to significantly accelerate the testing procedure for extreme rapid charging,” said Peter Attia, who participated in the study as a graduate student, in an interview with SciTechDaily. “What is really exciting, however, is the method. We can apply this approach to many different issues that, right now, are holding back battery development for years or months.”

“Machine learning is trial-and-error, but in a smarter way,” said Aditya Grover, a graduate student in computer science that also participated in the analysis. “Computers are much better than us at figuring out when to research — try new and different strategies — and when to exploit, or zero , on the most promising ones.”

Ermon said,”It gave us this amazingly easy charging protocol — something we did not expect. That is the difference between an individual and a machine: The machine isn’t biased by human instinct, which is strong but occasionally misleading.”

Wider Application Found:

The strategy has the potential to accelerate every bit of this battery development pipeline, from designing a batter’s chemistry, to storing and determining its shape and size, to discovering systems, the researchers suggested. This has implications not just like for solar and wind energy.

“This is a new means of doing battery growth,” said Patrick Herring, a co-author of this study and a scientist in the Toyota Research Institute. “Having data which you can share among a high number of individuals in academia and business, and that’s automatically analyzed, enables much quicker innovation.”

The researchers intend to create the machine learning and data collection system available for battery scientists of the study to use.

Ermon suggested large data testing issues, to optimizing the performance of lasers and X-rays from drug development, may be revolutionized by using machine learning optimization.

Sector has been working on applying AI to battery charging. Researchers in battery firm StoreDot have been using machine learning how to expand its capabilities, wrote Dr. Doron Myersford, CEO of StoreDot, in a recent report in Engineering and Technology.

“A first foray into this technique has achieved remarkable results,” he said, leading to a decision to dedicate an R&D staff to construction capacities in machine learning. The plan is to use the lessons learned into the next generation of EV batteries of the company. He cautioned,”Ultra-fast charging presents an extremely complicated issue,” involving advanced data science together with experience in electrochemistry, cell construction, anodes, cathodes and electrolytes, more intricate decisions 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 contemporary 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 densitynonetheless, they face major technical and security hurdles. The energy density means they tend to react to break down through quantity changes. They run the risk of short circuiting, causing fire or explosion and heat generation. Research is currently continuing.