Researchers at the University of Michigan (U-M) have emphasised that testing the longevity of new electric vehicle battery designs could be four times faster with a novel streamlined approach.
Reducing electric vehicle (EV) battery testing time can be significantly reduced by 75% by utilising a new streamlined approach, according to researchers at the University of Michigan. This optimisation framework 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,” explained Wei Lu, U-M Professor of Mechanical Engineering and leader of the research team behind the framework, published in Patterns–Cell Press. “It takes such a long time to evaluate.”
Creating improved EV batteries
With EV battery manufacturers grappling with range anxiety and concerns surrounding charging availability, the optimisation system developed by Lu’s team could cut the time for both simulation and physical testing of new and improved batteries by approximately 75%. 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 can include the materials used, the thickness of the electrodes, the size of the particles in the electrode, and more. Testing each configuration typically 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 process through the huge number of possible battery designs to discover the optimal configurations.
Reducing EV battery testing time
“Our approach not only reduces testing time, but it 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. We have 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.
The benefits of this new EV battery testing framework
The framework halts cycling tests that do not get off to promising starts in order to save resources using the mathematical techniques known as, Asynchronous Successive Halving Algorithm and Hyperband. Meanwhile, it utilises data from previous tests and suggests new sets of favourable parameters to investigate using the 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 parallelisation. 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 mobile phones. However, EV batteries may represent the most pressing use of this technology.
Incorporating a performance prediction model
“This framework can be tuned to be more efficient when a performance prediction model is incorporated,” explained Changyu Deng, first author and U-M Doctoral Student in Mechanical Engineering. “We expect this work to inspire improved methods that lead us to optimal batteries to make better EVs and other life-improving devices.”
A recent survey conducted by the Mobility Consumer Index revealed that 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.
Therefore, battery performance has a central role in bringing EVs to the masses as a means of offsetting the impacts of climate change.
“By significantly reducing the testing time, we hope our system can help speed up the development of better batteries, accelerate the adoption or certification of batteries for various applications, and expedite the quantification of model parameters for battery management systems,” Lu concluded.
