Most existing technologies for tracking the temperature of lithium-ion batteries are inadequate, the researcher says.
A researcher from the University of Kansas is developing a machine learning technology to monitor and prevent overheating in lithium-ion batteries.
Huazhen Fang will be supported by new five-year, $500,000-grant from the National Science Foundation to develop a foundational framework for characterizing and monitoring LiB packs’ spatially and temporally distributed thermal behavior.
The KU researcher noted that lithium batteries, which are now widely used for energy storage, are vulnerable to thermal events.
He explained :–
They can easily catch fire or have thermal explosions when ambient temperatures are high or when some internal failures occur. This is because the lithium metal is highly reactive, and the commonly used electrolyte is flammable.
According to him, most existing technologies for tracking the temperature of lithium-ion batteries are inadequate because sensors only can read the outside surface temperature of the batteries.
To know more about the state of the cell, it is important to get the internal temperature. He is using artificial intelligence and machine learning to predict temperatures inside the cell.
More accurate way to calculate thermal runaway
Fang said his computer-learning technique could predict variations in internal temperatures inside a battery.
He said: –
The data from a lithium-ion battery fed into artificial intelligence to deduce internal temperatures could be processed in the device powered by the battery or linked to cloud computing. If a battery undergoes thermal runaway, the device would be programmed to shut down or disconnect the battery before it becomes hot enough to catch fire or trigger an explosion.
With these innovations, lithium-ion batteries could be scaled up to more industrial levels via cells that bundle hundreds of batteries together, he added.
(With inputs from Green Car Congress)