Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries thermal management

TL;DR

This paper presents a combined CNN and FEM approach to model and evaluate the thermal management of Li-ion batteries using composite phase change materials, demonstrating high accuracy in predicting thermal properties and system performance.

Contribution

It introduces a novel multiscale modeling framework that integrates CNNs with FEM to efficiently predict thermal properties of CPCMs for battery thermal management.

Findings

CNN-based predictions match FEM results with high accuracy.

The model effectively simulates thermal management in battery packs.

The approach reduces computational cost compared to pure FEM modeling.

Abstract

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.