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Register a new userMachine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries thermal management
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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.
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Rechargeable Zn batteries with aqueous electrolytes have been considered as promising alternative energy storage technology, with various advantages such as low cost, high volumetric capacity, environmentally friendly, and high safety. However, a lack of reliable cathode materials has largely pledged their applications. Herein, we developed a machine learning (ML) based approach to predict cathodes with high capacity (>150 mAh/g) and high voltage (>0.5V). We screened over ~130,000 inorganic materials from the Materials Project database and applied the crystal graph convolutional neural network (CGCNN) based ML approach with data from the AFLOW database. The combination of these two could not only screen cathode materials that match well with the experimental data but also predict new promising candidates for further experimental validations. We hope this study could spur further interests in ML-based advanced theoretical tools for battery materials discovery.
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Zinc-air batteries offer large specific energy densities, while relying on abundant and non-toxic materials. In this paper, we present the first multi-dimensional simulations of zinc-air batteries. We refine our existing theory-based model of secondary zinc-air systems. The model comprises thermodynamically consistent multi-species transport in alkaline electrolytes, formation and dissolution of metallic zinc and passivating zinc oxide, as well as multi-phase coexistence in gas diffusion electrodes. For the first time, we simulate zinc shape-change during battery cycling by modeling convection of zinc solids. We validate our model with in-situ tomography of commercial button cells. Two-dimensional volume-averaged simulations of cell voltage and zinc electrode morphology during discharge agree with these measurements. Thus, we can study how electrolyte carbonation limits shelf-life and how zinc shape-change limits cycle-life. The charging current is found to be the major contributor to cycle-life limitations. Finally, we optimize initial anode structure and charge-discharge protocols for improved performance and cycle-ability.
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