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Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection

机译：基于卷积神经网络和随机林特征选择的质子交换膜燃料电池性能预测

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摘要

For optimizing the performance of the proton exchange membrane fuel cells (PEMFCs), the I-V polarization curve is generally used as an important evaluation metric, which can represent many important properties of PEMFCs such as current density, specific power, etc. However, a vast number of experiments for achieving I-V polarization curves are conducted, which consumes a lot of resources, since the membrane electrode assembly (MEA) in PEMFCs involves complex electrochemical, thermodynamic, and hydrodynamic processes. To solve the issues, this paper utilizes deep learning (DL) to design a performance prediction method based on the random forest algorithm (RF) and convolutional neural networks (CNN), which can reduce unnecessary experiments for MEA development. In the proposed method, to improve the high quality of the training dataset, the RF algorithm is adopted to select the important factors as the input feature of the model, and the selected factors are further verified by the previous studies. CNN is used to construct the performance prediction model which outputs the IV polarization curve. In particular, batch normalization and dropout methods are applied to enhance model generalization. The effectiveness of the CNN-based prediction model is evaluated on the real I-V polarization curve dataset. Experiment results indicate that the prediction curves of the proposed model have good agreement with the real curves. Our study demonstrates the deep learning technologies are powerful complements for optimizing the PEMFCs.
机译：为了优化质子交换膜燃料电池（PEMFC）的性能，所述IV极化曲线，一般使用作为一个重要的评估度量，其可以表示的PEMFC的许多重要性质如电流密度，比功率，等等。然而，一个广阔实验用于实现IV极化曲线数量进行的，这会消耗大量的资源，因为在PEMFC中的膜电极组件（MEA）涉及到复杂的电化学，热力学，流体力学和过程。为了解决上述问题，本文利用深度学习（DL）来设计基于随机森林算法（RF）和卷积神经网络（CNN），它可以减少不必要的实验为MEA发展业绩预测方法。在所提出的方法，提高训练数据集的高品质，RF算法来选择的重要因素，作为模型的输入功能，以及所选择的因素由先前的研究进一步验证。 CNN用于构造它输出IV极化曲线的性能预测模型。特别地，批标准化和漏失方法应用于增强模型概括。基于CNN预测模型的有效性上的真正的I-V极化曲线数据集进行评估。实验结果表明，该模型的预测曲线，并与实际曲线吻合。我们的研究表明深学习技术是优化PEMFC的强大补充。

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  • 来源
    《Energy Conversion & Management》 |2021年第9期|114367.1-114367.10|共10页
  • 作者

    Huo Weiwei; Li Weier; Zhang Zehui; Sun Chao; Zhou Feikun; Gong Guoqing;

  • 作者单位

    Beijing Informat Sci & Technol Univ Sch Mech & Elect Beijing 100101 Peoples R China;

    Beijing Informat Sci & Technol Univ Sch Mech & Elect Beijing 100101 Peoples R China;

    Nankai Univ Coll Software Tianjin 300071 Peoples R China;

    Beijing Inst Technol Engn Lab Elect Vehicles Beijing 100081 Peoples R China;

    Guangzhou Automobile Grp Co Ltd Automot Engn Res Inst Guangzhou 510006 Peoples R China;

    Beijing Informat Sci & Technol Univ Sch Mech & Elect Beijing 100101 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Performance prediction; Fuel cell; Deep learning; Random forest;

    机译：性能预测;燃料电池;深受学习;随机森林;

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