Researchers now able to predict battery lifetimes with machine learning

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.

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