A machine learning driven 3D+1D model for efficient characterization of proton exchange membrane fuel cells

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuwei Pan , Haijun Ruan , Billy Wu , Yagya N. Regmi , Huizhi Wang , Nigel P. Brandon
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Abstract

The computational demands of 3D continuum models for proton exchange membrane fuel cells remain substantial. One prevalent approach is the hierarchical model combining a 2D/3D flow field with a 1D sub-model for the catalyst layers and membrane. However, existing studies often simplify the 1D domain to a linearized 0D lumped model, potentially resulting in significant errors at high loads. In this study, we present a computationally efficient neural network driven 3D+1D model for proton exchange membrane fuel cells. The 3D sub-model captures transport in the gas channels and gas diffusion layers and is coupled with a 1D electrochemical sub-model for microporous layers, membrane, and catalyst layers. To reduce computational intensity of the full 1D description, a neural network surrogates the 1D electrochemical sub-model for coupling with the 3D domain. Trained by model-generated large synthetic datasets, the neural network achieves root mean square errors of less than 0.2%. The model is validated against experimental results under various relative humidities. It is then employed to investigate the nonlinear distribution of internal states under different operating conditions. With the neural network operating at 0.5% of the computing cost of the 1D sub-model, the hybrid model preserves a detailed and nonlinear representation of the internal fuel cell states while maintaining computational costs comparable to conventional 3D+0D models. The presented hybrid data-driven and physical modeling framework offers high accuracy and computing speed across a broad spectrum of operating conditions, potentially aiding the rapid optimization of both the membrane electrode assembly and the gas channel geometry.

Abstract Image

用于质子交换膜燃料电池高效表征的机器学习驱动 3D+1D 模型
质子交换膜燃料电池三维连续模型的计算要求仍然很高。一种流行的方法是将 2D/3D 流场与催化剂层和膜的 1D 子模型相结合的分层模型。然而,现有的研究通常将一维领域简化为线性化的零维块状模型,这可能会导致在高负载时出现重大误差。在本研究中,我们提出了一种计算高效的神经网络驱动质子交换膜燃料电池 3D+1D 模型。三维子模型捕捉气体通道和气体扩散层中的传输,并与微孔层、膜和催化剂层的一维电化学子模型相结合。为了降低完整一维描述的计算强度,神经网络替代了一维电化学子模型,以便与三维领域进行耦合。通过模型生成的大型合成数据集进行训练,神经网络的均方根误差小于 0.2%。该模型根据各种相对湿度下的实验结果进行了验证。然后,它被用来研究不同工作条件下内部状态的非线性分布。神经网络的计算成本仅为一维子模型的 0.5%,混合模型保留了燃料电池内部状态的详细非线性表示,同时计算成本与传统的三维+零维模型相当。所提出的数据驱动和物理建模混合框架可在各种操作条件下提供高精度和计算速度,从而有助于快速优化膜电极组件和气体通道几何形状。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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