SLAC, MIT, TRI researchers advance machine learning to accelerate battery development; insights on fast-charging – Green Car Congress

Scientists have made a major advance in harnessing machine learning to accelerate the design for better batteries. Instead of using machine learning just to speed up scientific analysis by looking for patterns in dataas typically donethe researchers combined it with knowledge gained from experiments and equations guided by physics to discover and explain a process that shortens the lifetimes of fast-charging lithium-ion batteries.

It was the first time this approachknown as scientific machine learninghas been applied to battery cycling, said Will Chueh, an associate professor at Stanford University and investigator with the Department of Energys SLAC National Accelerator Laboratory who led the study. He said the results overturn long-held assumptions about how lithium-ion batteries charge and discharge and give researchers a new set of rules for engineering longer-lasting batteries.

The research, reported in Nature Materials, is the latest result from a collaboration between Stanford, SLAC, the Massachusetts Institute of Technology and Toyota Research Institute (TRI). The goal is to bring together foundational research and industry know-how to develop a long-lived electric vehicle battery that can be charged in 10 minutes.

Battery technology is important for any type of electric powertrain. By understanding the fundamental reactions that occur within the battery we can extend its life, enable faster charging and ultimately design better battery materials. We look forward to building on this work through future experiments to achieve lower-cost, better-performing batteries.

Patrick Herring, senior research scientist for Toyota Research Institute

The new study builds on two previous advances where the group used more conventional forms of machine learning to accelerate both battery testing and the process of winnowing down many possible charging methods to find the ones that work best.

While these studies allowed researchers to make much faster progressreducing the time needed to determine battery lifetimes by 98%, for examplethey didnt reveal the underlying physics or chemistry that made some batteries last longer than others, as the latest study did.

Combining all three approaches could potentially slash the time needed to bring a new battery technology from the lab bench to the consumer by as much as two-thirds, Chueh said.

In this case, we are teaching the machine how to learn the physics of a new type of failure mechanism that could help us design better and safer fast-charging batteries. Fast charging is incredibly stressful and damaging to batteries, and solving this problem is key to expanding the nations fleet of electric vehicles as part of the overall strategy for fighting climate change.

Will Chueh

The team observed the behavior of cathode particles made of nickel, manganese and cobalt (NMC). Stanford postdoctoral researchers Stephen Dongmin Kang and Jungjin Park used X-rays from SLACs Stanford Synchrotron Radiation Lightsource to get an overall look at particles that were undergoing fast charging. Then they took particles to Lawrence Berkeley National Laboratorys Advanced Light Source to be examined with scanning X-ray transmission microscopy, which homes in on individual particles.

An animation shows two contrasting views of how electrode particles release their stored lithium ions during battery charging. Red particles are full of lithium and green ones are empty. Scientists had thought ions flowed out of all the particles at once and at roughly the same speed (left). But a new study by SLAC and Stanford researchers paints a different picture (right): Some particles release a lot of ions immediately and a fast clip, while others release ions slowly or not at all. This uneven pattern stresses the battery and reduces its lifetime. (Hongbo Zhao/MIT)

The data from those experiments, along with information from mathematical models of fast charging and equations that describe the chemistry and physics of the process, were incorporated into scientific machine learning algorithms.

Until now, scientists had assumed that the differences between particles were insignificant and that their ability to store and release ions was limited by how fast lithium could move inside the particles, Kang said. In this way of seeing things, lithium ions flow in and out of all the particles at the same time and at roughly the same speed.

But the new approach revealed that the particles themselves control how fast lithium ions move out of cathode particles when a battery charges, he said. Some particles immediately release a lot of their ions while others release very few or none at all. And the quick-to-release particles go on releasing ions at a faster rate than their neighborsa positive feedback, or rich get richer, effect that had not been identified before.

We now have a pictureliterally a movieof how lithium moves around inside the battery, and its very different than scientists and engineers thought it was. This uneven charging and discharging puts more stress on the electrodes and decreases their working lifetimes. Understanding this process on a fundamental level is an important step toward solving the fast charging problem.

Stephen Kang

The scientists say their new method has potential for improving the cost, storage capacity, durability and other important properties of batteries for a wide range of applications, from electric vehicles to laptops to large-scale storage of renewable energy on the grid.

This research was funded by Toyota Research Institute. The Stanford Synchrotron Radiation Lightsource and Advanced Light Source are DOE Office of Science user facilities, and work there was supported by the DOE Office of Science and the DOE Advanced Battery Materials Research Program.

Resources

Park, J., Zhao, H., Kang, S.D. et al. (2021) Fictitious phase separation in Li layered oxides driven by electro-autocatalysis. Nat. Mater. doi: 10.1038/s41563-021-00936-1

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SLAC, MIT, TRI researchers advance machine learning to accelerate battery development; insights on fast-charging - Green Car Congress