Researchers now able to predict battery lifetimes with machine learning
Date:
May 5, 2022
Source:
DOE/Argonne National Laboratory
Summary:
Scientists have used machine learning algorithms to predict how
long a lithium-ion battery will last.
FULL STORY ========================================================================== Technique could reduce costs of battery development.
========================================================================== Imagine a psychic telling your parents, on the day you were born,
how long you would live. A similar experience is possible for battery
chemists who are using new computational models to calculate battery
lifetimes based on as little as a single cycle of experimental data.
In a new study, researchers at the U.S. Department of Energy's
(DOE) Argonne National Laboratory have turned to the power of machine
learning to predict the lifetimes of a wide range of different battery chemistries. By using experimental data gathered at Argonne from a set
of 300 batteries representing six different battery chemistries, the
scientists can accurately determine just how long different batteries
will continue to cycle.
In a machine learning algorithm, scientists train a computer program
to make inferences on an initial set of data, and then take what it has
learned from that training to make decisions on another set of data.
"For every different kind of battery application, from cell phones to
electric vehicles to grid storage, battery lifetime is of fundamental importance for every consumer," said Argonne computational scientist
Noah Paulson, an author of the study. "Having to cycle a battery
thousands of times until it fails can take years; our method creates a
kind of computational test kitchen where we can quickly establish how
different batteries are going to perform." "Right now, the only way to evaluate how the capacity in a battery fades is to actually cycle the
battery," added Argonne electrochemist Susan "Sue" Babinec, another
author of the study. "It's very expensive and it takes a long time."
According to Paulson, the process of establishing a battery lifetime can
be tricky. "The reality is that batteries don't last forever, and how long
they last depends on the way that we use them, as well as their design and their chemistry," he said. "Until now, there's really not been a great way
to know how long a battery is going to last. People are going to want to
know how long they have until they have to spend money on a new battery."
==========================================================================
One unique aspect of the study is that it relied on extensive experimental
work done at Argonne on a variety of battery cathode materials, especially Argonne's patented nickel-manganese-cobalt (NMC)-based cathode. "We had batteries that represented different chemistries, that have different
ways that they would degrade and fail," Paulson said. "The value of this
study is that it gave us signals that are characteristic of how different batteries perform." Further study in this area has the potential to guide
the future of lithium-ion batteries, Paulson said. "One of the things
we're able to do is to train the algorithm on a known chemistry and have
it make predictions on an unknown chemistry," he said. "Essentially,
the algorithm may help point us in the direction of new and improved chemistries that offer longer lifetimes." In this way, Paulson believes
that the machine learning algorithm could accelerate the development
and testing of battery materials. "Say you have a new material, and
you cycle it a few times. You could use our algorithm to predict its
longevity, and then make decisions as to whether you want to continue
to cycle it experimentally or not." "If you're a researcher in a lab,
you can discover and test many more materials in a shorter time because
you have a faster way to evaluate them," Babinec added.
A paper based on the study, "Feature engineering for machine learning
enabled early prediction of battery lifetime," appeared in the Feb. 25
online edition of the Journal of Power Sources.
In addition to Paulson and Babinec, other authors of the paper include Argonne's Joseph Kubal, Logan Ward, Saurabh Saxena and Wenquan Lu.
The study was funded by an Argonne Laboratory-Directed Research and
Development (LDRD) grant.
========================================================================== Story Source: Materials provided by
DOE/Argonne_National_Laboratory. Original written by Jared Sagoff. Note: Content may be edited for style and length.
========================================================================== Journal Reference:
1. Noah H. Paulson, Joseph Kubal, Logan Ward, Saurabh Saxena,
Wenquan Lu,
Susan J. Babinec. Feature engineering for machine learning enabled
early prediction of battery lifetime. Journal of Power Sources,
2022; 527: 231127 DOI: 10.1016/j.jpowsour.2022.231127 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/05/220505114658.htm
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