ELeBa – Evolutionary Learning Methods for efficient Simulation of Battery Aging

Project start /

The aim of this project is to significantly accelerate the computational simulation of aging processes in batteries. This is realized by the application of machine learning methods in the control of the solution methods for linear systems of equations. This new autonomous control allows for the usage of efficient iterative methods without a need to accept risks for the robustness of the overall method. Not only existing simulations are accelerated with the improved efficiency of the linear solver, but also model resolutions are made possible that could not be practically used before.

The project is carried out in cooperation with the Fraunhofer IEE (BaSiS Batterie Simulation Studio) and is funded by the Fraunhofer Research Center for Machine Learning within the Fraunhofer Cluster of Excellence Cognitive Internet Technologies.

Further information about the autonomous solver control and its application can be found here.

Project duration: 10/2020 – 06/2021