TY - JOUR

T1 - Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries’ thermal management

AU - Kolodziejczyk, Felix

AU - Mortazavi, Bohayra

AU - Rabczuk, Timon

AU - Zhuang, Xiaoying

N1 - Funding Information: B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft ( DFG, German Research Foundation ) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). Authors also acknowledge the support of the cluster system team at the Leibniz Universität of Hannover.

PY - 2021/6

Y1 - 2021/6

N2 - 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's 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 developing Newman's 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 pack's thermal management system with an excellent accuracy in comparison with the original FEM models.

AB - 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's 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 developing Newman's 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 pack's thermal management system with an excellent accuracy in comparison with the original FEM models.

KW - Finite element method

KW - Li-ion battery

KW - Machine learning

KW - Neural networks

KW - Thermal management

UR - http://www.scopus.com/inward/record.url?scp=85103123015&partnerID=8YFLogxK

U2 - 10.1016/j.ijheatmasstransfer.2021.121199

DO - 10.1016/j.ijheatmasstransfer.2021.121199

M3 - Article

AN - SCOPUS:85103123015

VL - 172

JO - International Journal of Heat and Mass Transfer

JF - International Journal of Heat and Mass Transfer

SN - 0017-9310

M1 - 121199

ER -