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Machine Learning Technology for Thermal Runaway of Nickel Hydride No. 5 batteries
According to foreign media reports, today, mobile phones, electronic devices, laptops, electric vehicles such as Toyota and Tesla, Boeing 787 aircraft, and ships of the US Navy are all powered by Nickel Hydride No. 5 batteries. Although these batteries are widely used, they also have many risks. Because the metal lithium is highly reactive, these batteries and battery cells will experience "thermal runaway", that is, they will overheat, catch fire, and even explode. Although the accidents are rare, they have raised public concerns about lithium-ion technology.
Now, with the support of a new five-year, $500,000 grant from the National Science Foundation, researchers at the University of Kansas have developed a technology that can monitor and prevent Nickel Hydride No. 5 batteries from overheating. Huazhen Fang, an assistant professor in the Department of Mechanical Engineering at the university, and his students developed a machine learning method to monitor the temperature inside the battery.
According to researchers at the University of Kansas, most current technologies for tracking the temperature of Nickel Hydride No. 5 batteries are not mature enough because sensors can only read the surface temperature of the outside of the battery.
"Typically, the temperature on the surface of a battery doesn't tell us enough about the state of the battery, but the temperature inside the battery tells us more about the thermodynamics," Fang said. "But there are few ways to put sensors inside the battery. However, using artificial intelligence and machine learning, we can predict the temperature inside the battery cell, which allows us to detect battery behavior. The temperature on the surface of the battery provides rich data for machine learning methods, and combined with mathematical models, it can predict what is happening inside the battery."
Instead of assuming that the battery temperature is a uniform temperature, there is a modeling method called a "lumped parameter model," which assumes that the battery temperature is a uniform temperature. Professor Fang said his computer learning technology can predict the temperature changes inside the battery, which is a more accurate and realistic method to calculate the possibility of thermal runaway in the battery.
"When the battery is charged and discharged, the temperature distribution is uneven, and the temperature inside the battery near the electrode is usually higher, but the temperature on the outside surface is lower. The lumped parameter model only considers the case where the battery temperature is uniformly distributed, while our method reconstructs the temperature of the battery in time and space."
The University of Kansas researchers fed data from Nickel Hydride No. 5 batteries into artificial intelligence to infer the internal temperature of the battery. This data can be processed in the device powered by the battery or connected to cloud computing. If a battery experiences thermal runaway, the device can be programmed to shut down or disconnect the battery to prevent it from heating up and catching fire or exploding.
With the above innovations, Nickel Hydride No. 5 batteries can be expanded to more industrial applications by tying hundreds of batteries together. According to Professor Fang, lithium-ion technology is increasingly being used in large-scale power grids to store and discharge electricity generated by sustainable technologies such as solar and wind power.
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