AI could help improve performance of lithium-ion batteries and fuel
cells

Date:
June 25, 2020
Source:
Imperial College London
Summary:
Researchers have demonstrated how machine learning could help
design lithium-ion batteries and fuel cells with better performance.



FULL STORY ==========================================================================
A new machine learning algorithm allows researchers to explore possible
designs for the microstructure of fuel cells and lithium-ion batteries,
before running 3D simulations that help researchers make changes to
improve performance.


========================================================================== Improvements could include making smartphones charge faster, increasing
the time between charges for electric vehicles, and increasing the power
of hydrogen fuel cells running data centres.

The paper is published today in npj Computational Materials.

Fuel cells use clean hydrogen fuel, which can be generated by wind and
solar energy, to produce heat and electricity, and lithium-ion batteries,
like those found in smartphones, laptops, and electric cars, are a popular
type of energy storage. The performance of both is closely related to
their microstructure: how the pores (holes) inside their electrodes are
shaped and arranged can affect how much power fuel cells can generate,
and how quickly batteries charge and discharge.

However, because the micrometre-scale pores are so small, their specific
shapes and sizes can be difficult to study at a high enough resolution
to relate them to overall cell performance.

Now, Imperial researchers have applied machine learning techniques to
help them explore these pores virtually and run 3D simulations to predict
cell performance based on their microstructure.

The researchers used a novel machine learning technique called "deep convolutional generative adversarial networks" (DC-GANs). These algorithms
can learn to generate 3D image data of the microstructure based on
training data obtained from nano-scale imaging performed synchrotrons
(a kind of particle accelerator the size of a football stadium).

Lead author Andrea Gayon-Lombardo, of Imperial's Department of Earth
Science and Engineering, said: "Our technique is helping 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 analysing images at this scale." When running
3D simulations to predict cell performance, researchers need a large
enough volume of data to be considered statistically representative of
the whole cell. It is currently difficult to obtain large volumes of microstructural image data at the required resolution.

However, the authors found they could train their code to generate either
much larger datasets that have all the same properties, or deliberately generate structures that models suggest would result in better performing batteries.

Project supervisor Dr Sam Cooper, of Imperial's Dyson School of Design Engineering, said: "Our team's findings will help researchers from the
energy community to design and manufacture optimised electrodes for
improved cell performance. It's an exciting time for both the energy
storage and machine learning communities, so we're delighted to be
exploring the interface of these two disciplines." By constraining
their algorithm to only produce results that are currently feasible
to manufacture, the researchers hope to apply their technique to
manufacturing to designing optimised electrodes for next generation cells.


========================================================================== Story Source: Materials provided by Imperial_College_London. Original
written by Caroline Brogan. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Andrea Gayon-Lombardo, Lukas Mosser, Nigel P. Brandon, Samuel
J. Cooper.

Pores for thought: generative adversarial networks for stochastic
reconstruction of 3D multi-phase electrode microstructures with
periodic boundaries. npj Computational Materials, 2020; 6 (1) DOI:
10.1038/s41524- 020-0340-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200625080942.htm

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