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• W3136321992 endingPage "121199" @default.
• W3136321992 startingPage "121199" @default.
• W3136321992 abstract "• Convolutional neural networks (CNNs) are trained to predict effective thermal properties of composite phase change materials. • Finite element simulations are used to prepare the dataset for CNN training. • Heat generations during a battery operation are simulated. • CNN can accurately evaluate a battery pack's thermal management system. 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." @default.
• W3136321992 created "2021-03-29" @default.
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• W3136321992 date "2021-06-01" @default.
• W3136321992 modified "2023-10-13" @default.
• W3136321992 title "Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries’ thermal management" @default.
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• W3136321992 doi "https://doi.org/10.1016/j.ijheatmasstransfer.2021.121199" @default.
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