What causes rechargeable batteries to deteriorate?
Factors that cause battery degradation change over time, according to a new study.
At first, the decay appears to be driven by the properties of the individual electrode particles, but after several dozen charge cycles, it’s how those particles are put together that matters most, the researchers report.
Rechargeable lithium-ion batteries don’t last forever – after enough charge and recharge cycles, they will eventually fail, so researchers are constantly looking for ways to squeeze a little more life out of their battery designs.
“The fundamental building blocks are these particles that make up the battery electrode, but when you zoom out, these particles interact with each other,” says Yijin Liu, a researcher at Stanford University’s Synchrotron Radiation Lightsource. , a scientist at SLAC in the Department of Energy. National Accelerator Laboratory and lead author of the new paper.
Therefore, “if you want to build a better battery, you have to look at how to put the particles together,” Liu says.
The new study by Science builds on previous research in which Liu and his colleagues used computer vision techniques to study how the individual particles that make up a rechargeable battery electrode break down over time. This time, the goal was to study not just individual particles, but also how they work together to extend or degrade battery life.
Keije Zhao, a Purdue University mechanical engineering professor who, along with Liu and Virginia Tech chemistry professor Feng Lin, was a lead author, likened the problem to people working in groups.
“Battery particles are like people – we all start our own way,” Zhao says. “But eventually we meet other people, and we end up in groups, going in the same direction. To understand maximum efficiency, we need to study both individual particle behavior and how those particles behave in groups. .
To explore this idea, co-first authors Jizhou Li, an SSRL postdoctoral fellow, and Nikhil Sharma, a Purdue graduate student, teamed up with Liu, Lin, and Zhao and other colleagues to study battery cathodes with X-rays. They used X-ray tomography to reconstruct three-dimensional images of the cathodes after they had gone through 10 or 50 load cycles. They sliced these 3D images into a series of 2D slices and used computer vision methods to identify the particles.
In the end, they identified more than 2,000 individual particles, for which they calculated not only individual characteristics such as size, shape and surface roughness, but also more global characteristics, such as the frequency at which the particles came into direct contact with each other and how the shapes of the particles varied.
Then they looked at how each of these properties contributed to particle breakdown, and a striking pattern emerged. After 10 cycles of charging, the most important factors were the properties of the individual particles, including the sphericalness of the particles and the ratio of particle volume to surface area. After 50 cycles, however, pair and group attributes – such as the distance between two particles, the variety of their shapes, and whether more elongated football-shaped particles were oriented the same – resulted in the decay particles.
“It’s not just the particle itself anymore. It’s the particle-particle interactions” that matter, says Liu. This is important, he says, because it means manufacturers could develop techniques to control these properties. For example, they might be able to use magnetic or electric fields to align elongated particles with each other, which the new findings suggest will result in longer battery life.
The findings could be applied beyond the details of current research, Lin says.
“This study really sheds light on how we can design and manufacture battery electrodes to achieve long battery life,” Lin says. “We are excited to implement the understanding of next-generation, low-cost, fast-charging batteries.”
Funding for the research came from the DOE’s laboratory-led research and development program at SLAC and the National Science Foundation. SSRL is a user installation of the DOE Office of Science.
Source: Stanford University