Testing the lifespan of new electric vehicle battery designs could be made four-times quicker by a new streamlined process, researchers at the University of Michigan (UM) have claimed and reported by the Institute of Mechanical Engineers.
The new ‘optimisation framework’ developed by the team could also reduce the cost of assessments for new designs, they said.
“The goal is to design a better battery and, traditionally, the industry has tried to do that using trial and error testing,” said mechanical engineer and research leader Wei Lu. “It takes such a long time to evaluate.”
Parameters involved in battery design include the materials used, thickness of the electrodes, size of particles in the electrodes and more. Testing each configuration usually means several months of fully charging then fully discharging – cycling – the battery, mimicking a decade of use.
The UM engineers harnessed machine learning to create a system that ‘knows’ when to quit and how to improve over time. The framework halts cycling tests that do not have a promising start in order to save resources. It also takes data from previous tests and suggests new sets of promising parameters to investigate.
The speed of testing “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”, a research announcement said.
“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 said.
The research was funded by LG Energy Solution.
