MAY 07, 2020 3:04 PM PDT

Machine-Learning Algorithms Explain Why Batteries Decline

WRITTEN BY: Nouran Amin

Researchers at the Department of Energy's SLAC National Accelerator Laboratory have used machine learning methodology to study how lithium-ion batteries degrade over time. Scientists used sophisticated algorithms paired with X-ray tomography data to generate a clear picture of how a particular battery component, the cathode, loses its efficiency with time.

Learn more about Lithium-Ion batteries:

Findings were published in Nature Communications and focused on the composition of cathodes made of nickel-manganese-cobalt, or NMC. Results showed that NMC cathodes are held together by a conductive carbon matrix and when they start to break away that results in the degradation process. In addition, large NMC particles have a greater capacity to decline over smaller particles.

That's important because researchers had generally assumed that by making battery particles smaller, they could make longer-lasting batteries -- something the new study suggests might not be so straightforward, says Yijin Liu, a staff scientist at SLAC and a senior author of the new paper.

To address these issues, researchers sought to seek a subfield of machine learning algorithms known as computer vision which can help scan images.

Source: Science Daily 

About the Author
  • Nouran earned her BS and MS in Biology at IUPUI and currently shares her love of science by teaching. She enjoys writing on various topics as well including science & medicine, global health, and conservation biology. She hopes through her writing she can make science more engaging and communicable to the general public.
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