AI-Powered Early Warning System Detects Lithium-Ion Battery Fires

Researchers Develop AI That Detects Impending Phone Battery Fires

Researchers at the National Institute of Standards and Technology (NIST) have developed an AI that detects when a lithium-ion battery is about to catch fire and sends an alert before all hell breaks loose. While the AI might not be able to prevent fires, it's intended to provide timely warnings so people can isolate the devices, cars, or bikes carrying lithium-ion batteries and reduce potential damage. Researchers Wai Cheong Tam and Anthony Putorti came up with the idea while watching videos of batteries exploding. They observed that when the safety valve of a battery slot breaks due to excessive heat, it makes a distinctive "click-hiss" sound, similar to the fizzy pop of a soda bottle. The duo decided to develop an algorithm that detects this specific noise. They began by recording the valve-breaking sound from 38 exploding batteries. After tweaking the speed and pitch of those sounds, they got a larger sample of 1,000 unique sounds. They fed these audio clips to their algorithm and helped it understand what a breaking safety valve sounds like. "The algorithm works remarkably well," according to the researchers, who secured a 94% success ratio with their model. That's not all. The AI model, relying on a microphone mounted on a camera, identified the sound "about two minutes before the battery failed catastrophically." The testing was rigorous. One of the researchers, Tam, tried to confuse the algorithm by throwing all kinds of noises, from recordings of people walking to closing doors to the opening of Coke cans. "Only a few of them confused the detector," Tam says. Once the AI detector is fully developed, it "could be used to build a new kind of fire alarm," the researchers claim. "These alarms could be installed in homes and office buildings or in places with lots of batteries like warehouses and electric vehicle parking garages. The advanced warning they provide could give people time to evacuate."

Researchers develop AI-powered early warning system for lithium-ion battery fires

Forward-looking: Researchers from the National Institute of Standards and Technology have created an AI system that could serve as an early warning system for lithium-ion battery fires, potentially providing valuable time to mitigate the catastrophic effects of a compromised battery before it catches fire. Wai Cheong "Andy" Tam, one of the researchers on the NIST team, was watching videos of exploding batteries and noticed that right before they caught fire, a distinct "click-hiss" sound could be heard. The sound Tam was hearing originated from a safety valve designed to let batteries in hard casings release pressure when a chemical reaction takes place. Tam wasn't the first to make note of the sound, and others have even studied whether or not it could be used as part of an early warning system. The problem came down to reliably recognizing the noise. The world is full of all sorts of sounds, and many - like cracking open a bottle of soda, using a stapler, or even dropping a paperclip - sound a lot like the safety valve doing its thing. How do you craft a detection system that doesn't constantly give off false alarms? With AI, of course. Tam and the team worked with a laboratory at Xi'an University of Science and Technology to explode 38 batteries, recording audio from each. They then tweaked aspects like speed and pitch to come up with more than a thousand different combinations, and used the results to train an AI on what a safety valve popping sounds like. In testing, the algorithm was able to correctly detect the sound of the safety valve 94 percent of the time. Try as they might, the team had little success when trying to trigger a false alarm. "Only a few of them confused the detector," Tam said. On average, the safety valve broke about two minutes before the battery reached the point of no return. The researchers have applied for a patent and in the meantime, plan to continue experimenting with different types of batteries and microphones.