Argonne National Laboratory (ANL) scientists have adopted Machine Learning techniques to predict the lifetimes of a wide range of battery chemistries.
According to the scientists, their new process requires just as little as a single cycle of experimental data to calculate battery lifetimes which should surely reduce battery development costs in the longer run.
In the current experiment, ANL utilized data from a set of 300 battery materials—including its patented nickel-manganese-cobalt (NMC)-based cathode—representing six different battery chemistries to accurately determine just how long different batteries will continue to cycle.
“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,” notes study author and computational scientist Noah Paulson.
“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,” adds co-author and ANL electrochemist Susan Babinec. “It’s very expensive and it takes a long time.”
According to Paulson, the process of establishing a battery lifetime can be tricky as batteries dont last forever and how long they last depends on the way that one uses them, as well as their design and their chemistry.
“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.”
Talking about their new machine learning method, Paulson says: “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.”
“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,” adds Babinec.
Image and content: Manuchi-Pixabay/ANL
