Reduced EV battery test time by 75%
Their optimization framework could significantly reduce the cost of evaluating the performance of battery configurations over the long term.
“The goal is to design a better battery, and traditionally the industry has tried to do that using trial and error,” said Wei Lu, a professor of mechanical engineering at UM and head of the research team behind the frame. “It takes so long to assess.”
As electric vehicle (EV) battery makers grapple with range anxiety and concerns about charging availability, the optimization system developed by Lu’s team could reduce around 75 % the simulation and physical test time of new and better batteries. This speed could give a major boost to battery developers looking for the right mix of materials and configurations to ensure 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 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 through the large number of possible battery models to discover the best ones.
“Our approach not only reduces test time, it automatically generates better designs,” said Lu. end. This is not a simple task as a battery configuration that performs poorly in the first cycles may perform well later on or vice versa.
“We systematically formulated the early termination process and allowed the system to learn from accumulated data to produce promising new configurations.”
To achieve a dramatic reduction in time and cost, UM engineers leveraged the latest advances in machine learning to create a system that knows both when to stop and how to improve over time.
The framework stops cycle tests that do not start promisingly in order to save resources by using the mathematical techniques known as the asynchronous successive halving and hyperband algorithm. During this time, it takes data from previous tests and suggests promising new parameter sets to study using Tree of Parzen estimators.
In addition to cutting out tests that lack promise, a key time-saving element in the UM system is the way it generates multiple battery configurations to test at the same time, known as asynchronous parallelization. If a configuration ends the test or is rejected, the algorithm immediately calculates a new configuration to be tested without having to wait for the results of the other tests.
UM’s framework is effective at testing designs for all types of batteries, from those used for decades to power internal combustion automobiles to the smaller products that power our watches and cell phones. But EV batteries may be the technology’s most urgent use.
“This framework can be tuned to be more efficient when a performance prediction model is incorporated,” said Changyu Deng, a UM doctoral student in mechanical engineering. other life-enhancing devices.
A recent survey conducted by Mobility Consumer Index showed that 52% of consumers are now considering an electric vehicle for their next vehicle purchase. Despite the evolution of mentalities, concerns remain about the autonomy of vehicles (battery capacity) and the number of charging stations available to drivers.
Battery performance therefore plays a central role in providing VE to the masses as a way to offset the impacts of climate change.
“By dramatically reducing test time, we hope our system can help accelerate the development of better batteries, accelerate the adoption or certification of batteries for various applications, and accelerate the quantification of model parameters for battery management systems. battery,” Lu said.