Berkeley Lab’s COSMIC X-ray instrument reveals key information about individual battery particles

An X-ray instrument at Berkeley Lab contributed to a battery study that used an innovative approach to machine learning to speed up the learning curve about a process that shortens the life of fast-charging lithium batteries.

Researchers used Berkeley Lab’s Advanced Light Source, a synchrotron that produces light ranging from the infrared to X-rays for dozens of simultaneous experiments, to perform a chemical imaging technique known as scanning transmission X-ray microscopy, or STXM, at a state-of-the-art ALS beamline dubbed COSMIC. 

Researchers also employed “in situ” X-ray diffraction at another synchrotron – SLAC’s Stanford Synchrotron Radiation Lightsource – which attempted to recreate the conditions present in a battery, and additionally provided a many-particle battery model. All three forms of data were combined in a format to help the machine-learning algorithms learn the physics at work in the battery.

While typical machine-learning algorithms seek out images that either do or don’t match a training set of images, in this study the researchers applied a deeper set of data from experiments and other sources to enable more refined results. It represents the first time this brand of “scientific machine learning” was applied to battery cycling, researchers noted. The study was published recently in Nature Materials.

The study benefited from an ability at the COSMIC beamline to single out the chemical states of about 100 individual particles, which was enabled by COSMIC’s high-speed, high-resolution imaging capabilities. Young-Sang Yu, a research scientist at the ALS who participated in the study, noted that each selected particle was imaged at about 50 different energy steps during the cycling process, for a total of 5,000 images. 

The data from ALS experiments and other experiments were combined with data from fast-charging mathematical models, and with information about the chemistry and physics of fast charging, and then incorporated into the machine-learning algorithms.

“Rather than having the computer directly figure out the model by simply feeding it data, as we did in the two previous studies, we taught the computer how to choose or learn the right equations, and thus the right physics,” said Stanford postdoctoral researcher Stephen Dongmin Kang, a study co-author.

Patrick Herring, senior research scientist for Toyota Research Institute, which supported the work through its Accelerated Materials Design and Discovery program, said, “By understanding the fundamental reactions that occur within the battery, we can extend its life, enable faster charging, and ultimately design better battery materials.”

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The term “Internet of Things” can be used to describe a huge range of different technologies, from sensors to gateways to back-end systems that organize data and keep machine-to-machine networks secure. Lots of attention is rightly paid to the way IoT systems gather data and how it moves from place to place. However, for some parts of the IoT, the issue of how to keep sensors powered may be just as important.

Particularly in the case of IoT systems that feature small sensors and sensors that might be far away from each other or from the rest of the system, energy usage is a critical concern, because traditional wired power may simply not be an option.

Agriculture, utilities, and transportation are among verticals where widely spaced, low-power deployment is important. Scientists studying a volcano might not be able to run a power cable all the way from the closest part of the grid to their vibration sensors. Soil moisture testers in a farmer’s field could face the same problem, and so on.

There are, however, other options, and choosing the best solution has everything to do with understanding what the desired business outcome and how to attain it with peak efficiency, according to Gartner vice president and analyst Al Velosa.

 “The fundamental question is ‘hat’s it cost to deploy the infrastructure?’” he said. “If you’re managing a couple thousand miles’ worth of assets…the bigger cost is sending a truck to that asset than anything else.”

That’s particularly important for the first and probably the most common option for powering small, remote IoT assets – battery cells. No battery lasts forever, so, eventually, they have to be swapped out. The current state of the art focuses on silver oxide cells akin to watch batteries and those used in hearing aids, according to Forrester vice president and principal analyst Frank Gillett,

“One of the problems you run into is that some battery chemistry won’t make it 10 years,” he said.

Silver oxide cells remain popular because their charge-to-weight ratio is comparatively very high. Even a small battery of this type can power a simple sensor outfitted with a low-power, infrequently used radio for years, potentially. They are not, however, so powerful as to free device makers from the responsibility of designing for maximum efficiency.

At its most basic level, an IoT sensor needs to be able to collect information, express that information in a digital format, and transmit it up the chain, whether that’s to a nearby edge device for collation and processing or directly to the back end. Each part of that process has an energy cost, and while advancing technology has dramatically increased power efficiency in both processing and transmission, energy is still one of the primary limiting factors in IoT device design.

“It’s figuring out how to maximize what a low-power device can do, and a lot of that is making the radios more efficient,” said Gillett. “The flipside is making the compute part of the IoT endpoint very power-efficient as well. Ideally you have them both fairly integrated.”

Battery technology, he added, advances comparatively slowly, compared to processors, chips and sensors. That’s part of why some companies are looking elsewhere to power their IoT devices.

One option is solar power. Increasingly efficient solar cells mean that it’s easy enough to add appropriately sized panels to small devices, and the cost of those panels has dropped of late, also.

That’s great in theory, according to Velosa, but in practice, many deployments using solar energy aren’t going to be that much more efficient than those using battery power. Solar is still dependent on the panels getting enough exposure to the sun, and what’s more, they’re far from maintenance-free. Dust and dirt can degrade their ability to generate power.

“We are still looking at normal deployment of an asset being 1-5 years, with some service call in the middle of that,” he said.

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Researchers at Imperial College London claim to have developed a new machine-learning algorithm that could improve the design and performance of lithium-ion batteries and fuel cells.

They claimed that the algorithm would also allow researchers to understand the microstructure of fuel cells better. This will help run simulations that could enable them to improve the performance of these battery technologies, they noted.

Lithium-ion batteries are the most commonly used in electronic devices and vehicles. A fuel cell, on the other hand, is a device that converts chemical energy into electrical energy using oxidizing agents through an oxidation-reduction (redox) reaction. Improvements in these technologies would have widespread implications across several product sectors.

In their paper, the researchers explained that the performance of fuel cells is dependent on their microstructure and how the pores inside their electrodes are shaped and arranged. This determines how much power fuel cells can generate and the speed at which they can be charged and discharged.

The research paper published in the npj Computational Materials journal said that the algorithm could help reduce the volume of electrochemical simulations required to test the performance of a particular microstructure design during optimization.

The technique called the “deep convolutional generative adversarial networks” (DC-GANs),” will enable researchers to visualize and explore these pores virtually through three-dimensional (3D) simulations.

“Our technique helps us zoom right in on batteries and cells to see which properties affect overall performance. Developing image-based machine learning techniques like this could unlock new ways of analyzing images at this scale,” said Andrea Gayon-Lombardo, lead author of the paper from Imperial’s Department of Earth Science and Engineering

Previously, Japanese researchers said they have developed a new electrode material that they claim will make lithium batteries cheaper, more stable, and capable of holding more charge for longer periods.

Earlier, Researchers at Penn State University claimed to have developed a lithium-ion battery that is safe and has power and can last up to one million miles. A team of researchers at the Penn State’s Battery and Energy Storage Technology (BEST) Center developed the battery.

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