Machine Learning Technology Enhances Battery Performance
Machine learning technology analyzes known electrolyte additives and predicts combinations that could improve battery performance. Illustration courtesy Argonne National Laboratory
LEMONT, IL—Engineers at Argonne National Laboratory are using machine learning technology to analyze known electrolyte additives and predict combinations that could improve battery performance. They have trained models to forecast key battery metrics, like resistance and energy capacity, and applied these models to suggest new additive combinations for testing.
Finding the right electrolyte additive for a battery is much like prescribing the right medicine. With hundreds of possibilities to consider, identifying the best additive for each battery is a challenge due to the vast number of possibilities and the time-consuming nature of traditional experimental methods.
“Think of an additive like medicine,” says Chen Liao, Ph.D., an Argonne chemist working on the project. “It makes the battery better.”
By combining machine learning with experimental testing, Liao and her colleagues quickly identified effective electrolyte additives, accelerating the discovery process compared with traditional methods, which are costly as well as time-consuming. This R&D approach successfully found new additive combinations that outperformed existing ones, showing the power of data-driven techniques in advancing battery technology and paving the way for high-performance, efficient batteries.
Liao is using the technology to develop lithium, nickel, manganese and oxygen (LNMO) batteries that operate at a high voltage and offer significant advantages to traditional batteries. They have a higher energy capacity and eliminate the need for cobalt.
The machine learning model is designed to establish a connection between the chemical structure of additives and their impact on battery performance, much like how humans make connections based on experience.
The Argonne engineers were able to predict the performance of 125 new combinations of additives. The model successfully identified several promising additives that improved battery performance, outperforming additives from the initial data.
This method not only saved time and resources but also demonstrated how machine learning can accelerate the discovery of new materials with desired properties for better batteries. By avoiding 125 traditional experiments, which would have taken approximately four to six months and required significant equipment costs, the engineers showed how machine learning can streamline discovery using a small experimental dataset.
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