Prediction of Effective Thermal Conductivity for Lithium-Ion Battery Electrodes Using Machine Learning Techniques

This research investigates the effectiveness of implementing machine learning techniques to predict the effective thermal conductivity of the lithium-ion battery electrodes. It uses scanned images of the electrode to construct the discretized computational domain. An image analysis determines the position of active particles in the porous medium. Using a uniform grid, a conservative finite volume method finds the effective thermal conductivity of the porous medium based on different conductivities of each material. To perform the machine learning, the number of training samples is set up to be one order of magnitude more than the total number of grid points in the representative elemental volume. The training and testing groups are formed by sampling over random places in the image. Then, a deep learning network is trained to predict the effective thermal conductivity of the medium based on the geometry i.e. position and size of the active particles. The predictions are within 4% of the simulation results showing the accuracy of the machine learning method. Moreover, we show that the proposed new approach in which an image is taken as the input and the related effective thermal conductivity is obtained from the available trained network is more efficient than the simulation.