Numerical studies have been considered as a vital method to optimize the system design and the control strategy of proton exchange membrane (PEM) fuel cells practically. Given that the engineering application of multi-dimensional physics-based simulations is very challenging in terms of efficiency, this presents a unique opportunity for modeling approaches based on the artificial neural network (ANN). As a supplement to traditional statistical methods, the ANN technique demonstrates advantages in dealing with arbitrary nonlinear relations between the independent and dependent variables. In the present study, an optimized model using a feed-forward back-propagation (BP) network has been developed. By integrating with the genetic algorithm, the risk of overfitting could be reduced. The automatic process of searching for the most suitable network structure algorithm has also been adopted. Moreover, to figure out appropriate input variables, a feature dimension reduction methodology has been implemented in the proposed input variable determination (IVD) sub-model during the pre-processing procedure. The data points required for training, validating, and testing are obtained from comprehensive sensitivity tests. The active area of the membrane electrode assembly (MEA) in the present experiment is around 220 cm2 which is the same order of magnitude as commercial products. The optimized model has been thoroughly validated against experimental measurements, results show that simulations could accurately reproduce the effect of multiple operating parameters on the fuel cell performance. This new model is applicable to both interpolation and extrapolation. Furthermore, by activating the IVD sub-model, the maximum and average relative errors of extrapolation simulation results could be reduced up to 63 % and 37 %, respectively. In addition, by reasonably selecting the input variables in the order of priority, the mean relative error remains under 1 % with fewer input variables. The number of required training data points could be reduced up to 53 %.

中文翻译：

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基于人工神经网络的优化质子交换膜燃料电池模型的开发


数值研究被认为是优化质子交换膜 （PEM） 燃料电池系统设计和控制策略的重要方法。鉴于基于多维物理的仿真的工程应用在效率方面非常具有挑战性，这为基于人工神经网络 （ANN） 的建模方法提供了独特的机会。作为传统统计方法的补充，ANN 技术在处理自变量和因变量之间的任意非线性关系方面表现出优势。在本研究中，已经开发了一种使用前馈反向传播 （BP） 网络的优化模型。通过与遗传算法集成，可以降低过拟合的风险。还采用了搜索最合适的网络结构算法的自动过程。此外，为了找出合适的输入变量，在预处理过程中，在提出的输入变量确定 （IVD） 子模型中实施了一种特征降维方法。训练、验证和测试所需的数据点是从全面的敏感度测试中获得的。本实验中膜电极组件 （MEA） 的有效面积约为 220 cm2，与商业产品相同数量级。优化后的模型已针对实验测量进行了全面验证，结果表明，仿真可以准确再现多个运行参数对燃料电池性能的影响。这个新模型适用于插值和外插。 此外，通过激活 IVD 子模型，外推模拟结果的最大和平均相对误差可以分别降低 63% 和 37%。此外，通过按优先级顺序合理选择输入变量，输入变量较少，平均相对误差保持在 1% 以下。所需的训练数据点数量最多可减少 53%。