Testing the longevity of new electric vehicle battery designs could be four times faster with a streamlined approach. A new optimization framework created by researchers at University of Michigan could drastically reduce the cost of assessing how battery configurations will perform over the long haul.
“The goal is to design a better battery and, traditionally, the industry has tried to do that using trial and error testing,” said Wei Lu, U-M Professor of mechanical engineering and leader of the research team behind the framework. “It takes such a long time to evaluate.”
With electric vehicle (EV) battery manufacturers grappling with range anxiety and concerns of charging availability, the optimization system developed by Lu’s team could cut the time for both simulation and physical testing of new and better batteries by about 75 percent. That speed could provide a major boost to battery developers searching for the right combination of materials and configurations to ensure that consumers always have enough capacity to reach their destinations.
Parameters involved in battery design include everything from the materials used to the thickness of the electrodes to the size of the particles in the electrode and more.
Testing each configuration usually means several months of fully charging and then fully discharging — or cycling the battery — 1,000 times to mimic a decade of use. It is extremely time-consuming to repeat this test through the huge number of possible battery designs to discover the better ones.
“Our approach not only reduces testing time, but it also automatically generates better designs,” Lu said. “We use early feedback to discard unpromising battery configurations rather than cycling them till the end. This is not a simple task since a battery configuration performing mediocrely during early cycles may do well later on, or vice versa.”
The team has formulated the early-stopping process systematically and enabled the system to learn from the accumulated data to yield new promising configurations. To get a sizable reduction in the time and cost, U-M engineers harnessed the latest in machine learning to create a system that knows both when to quit and how to get better as it goes.
U-M’s framework is effective in testing designs of all battery types, from those used for decades to run internal combustion automobiles, to the smaller products that power our watches and cell phones. But EV batteries may represent the most pressing use of the technology.
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