Machine learning has allowed researchers to forecast battery lives.

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Machine learning has allowed researchers to forecast battery lives.

 The technique might lower the cost of battery development.

Consider a clairvoyant informing your parents how long you would live on the day you were born. Battery chemists who use new computational models to calculate battery lifetimes based on as little as a single cycle of experimental data may have a similar experience.

In a recent study, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory used machine learning to forecast the lives of several battery chemistries. The scientists can properly calculate how long various batteries will continue to cycle by analysing experimental data collected at Argonne from a collection of 300 batteries representing six distinct battery chemistries.

Scientists educate a computer programme to make inferences on an initial collection of data, and then use what it has learnt from that training to make conclusions on another set of data in a machine learning process.

"Battery lifespan is of vital relevance for every user for every different sort of battery application, from cell phones to electric cars to grid storage," said Argonne computational scientist Noah Paulson, one of the study's authors. "It can take years to cycle a battery thousands of times until it dies; our technology offers a type of computational test kitchen where we can quickly determine how different batteries will perform."

"Right now, the only method to evaluate how a battery's capacity diminishes is to actually cycle the battery," said Argonne electrochemist Susan "Sue" Babinec, another research author. "It's incredibly expensive and time-consuming."

According to Paulson, determining a battery's lifetime might be difficult. "The fact is that batteries do not live forever, and how long they last is determined on how we use them, as well as their design and chemistry," he added. "Until recently, there hasn't been a good method to predict how long a battery will survive. People will want to know how long it will be before they have to spend money on a new battery."

The study was notable for relying on substantial experimental work done at Argonne on a range of battery cathode materials, including Argonne's proprietary nickel-manganese-cobalt (NMC)-based cathode. "We had batteries that represented different chemistries, and they degraded and failed in different ways," Paulson explained. "The benefit of this study is that it provided us with indications that are typical of how different batteries function."

More research in this area, according to Paulson, has the potential to shape the future of lithium-ion batteries. "One of the things we can do is train the algorithm on a known chemical and then have it predict an unknown chemistry," he explained. "Basically, the algorithm may push us in the path of new and improved chemistries with longer lives."

Paulson believes that the machine learning method might speed up the creation and testing of battery materials in this way. "Assume you have a fresh material that you have cycled a few times. You may use our method to anticipate its lifespan and then decide whether or not to continue cycling it experimentally."

"If you're a researcher in a lab, you can find and test many more compounds in a shorter period of time because you can analyse them faster," Babinec noted.

The publication, "Feature engineering for machine learning enabled early prediction of battery lifespan," published in the online issue of the Journal of Power Sources on February 25.

Other authors of the study include Argonne's Joseph Kubal, Logan Ward, Saurabh Saxena, and Wenquan Lu, in addition to Paulson and Babinec.

An Argonne Laboratory-Directed Research and Development (LDRD) grant supported the research.

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