• Researchers now able to predict battery

    From ScienceDaily@1:317/3 to All on Thursday, May 05, 2022 22:30:38
    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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