道路强化振动条件下燃料电池堆力学性能预测模型

IF 0.8 Q3 ENGINEERING, MULTIDISCIPLINARY

Modelling and Simulation in Engineering Pub Date : 2021-08-03 DOI:10.1155/2021/6671547

Liying Ma, Bo Lv, Y. Hou, Xiangmin Pan

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引用次数: 2

摘要

本文采用非线性自回归外生模型(NARX)神经网络，提出了一种面向数据的模型，用于预测汽车燃料电池堆在实际工况下的力学行为。在多轴模拟台上进行了300小时的振动试验，再现了SVP道路频谱。同时，每隔50 h采集一次叠加层驱动位移和加速度响应数据。收集的所有数据用于基于NARX的模型训练和评估。结果表明，所建立的预测模型具有较好的精度，与实际情况相符。

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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Prediction Model of the Mechanical Behavior of a Fuel Cell Stack under Strengthened Road Vibrating Conditions

In this paper, a data-oriented model has been presented by nonlinear autoregressive exogenous model (NARX) neural network, which aims at predicting the mechanical behavior of a fuel cell stack for vehicle under the real-life operational conditions. A 300-hour vibration test with reproduction of SVP road spectrum was completed on a Multi-Axial Simulation Table. At the same time, data acquisition of drive displacement and acceleration response on stack was carried out in every 50 hours. All data collected were used to train and evaluate the model based on NARX. Result shows that the prediction model built is of good precision and consistent with the actual situation.

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来源期刊

Modelling and Simulation in Engineering ENGINEERING, MULTIDISCIPLINARY-

CiteScore

2.70

自引率

3.10%

发文量

审稿时长

18 weeks

期刊介绍： Modelling and Simulation in Engineering aims at providing a forum for the discussion of formalisms, methodologies and simulation tools that are intended to support the new, broader interpretation of Engineering. Competitive pressures of Global Economy have had a profound effect on the manufacturing in Europe, Japan and the USA with much of the production being outsourced. In this context the traditional interpretation of engineering profession linked to the actual manufacturing needs to be broadened to include the integration of outsourced components and the consideration of logistic, economical and human factors in the design of engineering products and services.