  In this work, we develop a combined convolutional neural networks (CNNs) and
finite element method (FEM) to examine the effective thermal properties of
composite phase change materials (CPCMs) consisting of paraffin and copper
foam. In this approach, first the CPCM microstructures are modeled using FEM
and next the image dataset with corresponding thermal properties is created.
The image dataset is subsequently used to train and test the CNN performance,
which is then compared with the performance of a popular network architecture
for image classification tasks. The predicted thermal properties are employed
to define the properties of the CPCM material of a battery pack. The heat
generation and electrochemical response of a Li-ion cell during the
charging/discharging is simulated by applying Newman battery model. Thermal
management is achieved by the latent heat of paraffin, with copper foam for
enhancing the thermal conductivity. The multiscale model is finally developed
using FEM to investigate the effectiveness of the thermal management of the
battery pack. In these models the thermal properties estimated by the FEM and
the CNN are employed to define the CPCM materials properties of a battery pack.
Our results confirm that the model developed on the basis of a CNN can evaluate
the effectiveness of the battery packs thermal management system with an
excellent accuracy in comparison with the original FEM models.
