基于机器学习的锂离子电池热管理复合相变材料多尺度建模
Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries thermal management
摘要：
在这项工作中，我们开发了一个结合卷积神经网络（CNN）和有限元方法（FEM）来研究由石蜡和泡沫铜组成的复合相变材料（CPCMs）的有效热性能。在这种方法中，首先使用FEM对CPCM微结构进行建模，然后创建具有相应热特性的图像数据集。图像数据集随后用于训练和测试CNN性能，然后将其与用于图像分类任务的流行网络架构的性能进行比较。预测的热性能被用来定义电池组的CPCM材料的性能。采用Newman电池模型对锂离子电池充放电过程中的发热和电化学响应进行了模拟。热管理是通过石蜡的潜热实现的，泡沫铜用于增强导热性。最后使用有限元方法建立了多尺度模型，以研究电池组热管理的有效性。在这些模型中，通过FEM和CNN估计的热特性被用来定义电池组的CPCM材料特性。我们的研究结果证实，在CNN的基础上开发的模型可以评估电池组热管理系统的有效性与原始的有限元模型相比，具有良好的精度。
英文摘要：
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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