Andrew Weng, a PhD student in mechanical engineering, and Anna Stefanopoulou, the William Clay Ford Professor of Technology.

Battery life prediction method could lower cost and time of EV production

Electric vehicle battery demand is on the rise and automakers including Ford and General Motors are partnering with battery manufacturers like LG Chem and SK Innovations to increase U.S. production capacity. Developers continue working to increase battery life and charging speed in addition to manufacturing speed. But changes to manufacturing materials and processes can affect battery life. 

With funding from the National Science Foundation, University of Michigan researchers have developed a method to predict how these changes could alter battery life without the usual process of repeated cycling — charging, discharging, and recharging the battery — to gather enough data to train a predictive algorithm. Cycling can take weeks or months to complete, and testing, therefore, is usually done on only some cells, not all that are produced.

Instead, the U-M researchers are examining a factor called internal resistance. It can be measured in just seconds at the end of the manufacturing cycle at little or no extra cost. The process also significantly reduces how long it takes to perform the aging tests.

The resistance measurement determines how much the battery fights the flow of energy inside itself. Resistance can be caused by internal components’ materials or how well ions move between the battery’s electrodes. 

The researchers say it is important to measure the resistance at low charge levels. That’s when it’s easiest to measure the amount of lithium that merged with the battery’s liquid electrolyte that moves the ions between electrodes. That layer, the solid electrolyte interphase, can protect the electrode surfaces and lengthen battery life.

The team says this way of measuring internal resistance could be a useful tool for EV battery manufacturers to make products faster and more cost-effectively. Consequently, the process also would speed overall EV production as demand grows.

“The question we’ve tried to answer is, ‘How fast can you learn about battery lifetime during the manufacturing process itself?’” said Anna Stefanopoulou, a technology professor at U-M, in a news release. “It turns out that the answer is, ‘Immediately, if you know the critical signal that can be acquired in a high-throughput testing.’ Finding such key measurable features can be simply used for continuous improvements and scaling up domestic battery manufacturing.”