U Mich generic battery-cycling optimization framework could reduce EV battery testing time by about 75%
Researchers at the University of Michigan have developed an optimized generic battery cycling framework that could reduce the time for both simulation and physical testing of new electric vehicle battery designs by about 75%. The optimization framework could significantly reduce the cost of assessing how battery configurations will perform over the long haul. The framework is published in an open-access paper in the journal Patterns.
The goal is to design a better battery and, traditionally, the industry has tried to do that using trial and error testing. It takes such a long time to evaluate.—Wei Lu, U-M professor of mechanical engineering and corresponding author
The reduced testing time 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—i.e., 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 automatically generates better designs. 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. We have formulated the early-stopping process systematically and enabled the system to learn from the accumulated data to yield new promising configurations.—Wei Lu
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.
The optimization framework consists of a pruner and a sampler. The pruner, using the Asynchronous Successive Halving Algorithm and Hyperband, stops unpromising cycling batteries to save the budget for further exploration. The sampler, using Tree of Parzen Estimators, predicts the next promising configurations based on query history. The framework can deal with categorical, discrete, and continuous parameters and can run in an asynchronously parallel way to allow multiple simultaneous cycling cells. Deng et al.
The framework halts cycling tests that don’t get off to promising starts in order to save resources using the mathematical techniques known as Asynchronous Successive Halving Algorithm and Hyperband. Meanwhile, it takes data from previous tests and suggests new sets of promising parameters to investigate using Tree of Parzen Estimators.
In addition to cutting off tests that lack promise, a key time-saving element in U-M’s system is the way it generates multiple battery configurations to be tested at the same time, known as asynchronous parallelization. If any configuration completes testing or is discarded, the algorithm immediately calculates a new configuration to test without the need to wait for the results of other tests.
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.
This framework can be tuned to be more efficient when a performance prediction model is incorporated. We expect this work to inspire improved methods that lead us to optimal batteries to make better EVs and other life-improving devices.—Changyu Deng, U-M doctoral student in mechanical engineering and first author
A recent survey conducted by Mobility Consumer Index showed 52% of consumers are now considering an EV for their next vehicle purchase. Despite changing attitudes, concerns remain over vehicle range (battery capacity) and the number of charging stations available to drivers. Battery performance, therefore, has a central role in bringing EVs to the masses as a means of offsetting the impacts of climate change.
The research was funded by LG Energy Solution.
Changyu Deng, Andrew Kim, Wei Lu (2022) “A generic battery-cycling optimization framework with learned sampling and early stopping strategies,” Patterns doi: 10.1016/j.patter.2022.100531