Simple Algorithm Paired with Standard Imaging Tool Could Predict Failure in Lithium Metal Batteries

Typography

Researchers at the University of California San Diego have developed a simple yet powerful method to characterize lithium metal battery performance with the help of a widely used imaging tool: scanning electron microscopy.

Researchers at the University of California San Diego have developed a simple yet powerful method to characterize lithium metal battery performance with the help of a widely used imaging tool: scanning electron microscopy. The advance could accelerate the development of safer, longer-lasting and more energy-dense batteries for electric vehicles and grid-scale energy storage.

The work was published in Proceedings of the National Academy of Sciences.

Lithium metal batteries have the potential to store twice as much energy as today’s lithium-ion batteries. That could double the range of electric cars and extend the runtime of laptops and phones. But to realize this potential, researchers must tackle a longstanding challenge: controlling lithium morphology, or how lithium deposits on the electrodes during charging and discharging.

Read More: University of California San Diego

Scanning electron microscopy (SEM) images are already a common staple of battery research. Now, they can be paired with a simple algorithm to enable better prediction of lithium metal battery performance and failure. (Photo Credit: Jenny Nicolas et al)