High-precision and efficiency diagnosis for polymer electrolyte membrane fuel cell based on physical mechanism and deep learning

IF 15 1区 工程技术 Q1 ENERGY & FUELS Etransportation Pub Date : 2023-10-01 DOI:10.1016/j.etran.2023.100275
Zhichao Gong , Bowen Wang , Yanqiu Xing , Yifan Xu , Zhengguo Qin , Yongqian Chen , Fan Zhang , Fei Gao , Bin Li , Yan Yin , Qing Du , Kui Jiao
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Abstract

As a nonlinear and dynamic system, the polymer electrolyte membrane fuel cell (PEMFC) system requires a comprehensive failure prediction and health management system to ensure its safety and reliability. In this study, a data-driven PEMFC health diagnosis framework is proposed, coupling the fault embedding model, sensor pre-selection method and deep learning diagnosis model. Firstly, a physical-based mechanism fault embedding model of PEMFC is developed to collect the data on various health states. This model can be utilized to determine the effects of different faults on cell performance and assist in the pre-selection of sensors. Then, considering the effect of fault pattern on decline, a sensor pre-selection method based on the analytical model is proposed to filter the insensitive variable from the sensor set. The diagnosis accuracy and computational time could be improved 3.7% and 40% with the help of pre-selection approach, respectively. Finally, the data collected by the optimal sensor set is utilized to develop the fault diagnosis model based on 1D-convolutional neural network (CNN). The results show that the proposed health diagnosis framework has better diagnosis performance compared with other popular diagnosis models and is conducive to online diagnosis, with 99.2% accuracy, higher computational efficiency, faster convergence speed and smaller training error. It is demonstrated that faster convergence speed and smaller training error are reflected in the proposed health diagnosis framework, which can significantly reduce computational costs.

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基于物理机理和深度学习的聚合物电解质膜燃料电池高精度高效诊断
聚合物电解质膜燃料电池(PEMFC)系统作为一个非线性动态系统,需要一个全面的故障预测和健康管理系统来保证其安全性和可靠性。本研究提出了一种数据驱动的PEMFC健康诊断框架,将故障嵌入模型、传感器预选方法和深度学习诊断模型相结合。首先,建立了基于物理机制的PEMFC故障嵌入模型,用于采集各种健康状态数据;该模型可用于确定不同故障对电池性能的影响,并有助于传感器的预选。然后,考虑故障模式对衰落的影响,提出了一种基于解析模型的传感器预选方法,从传感器集中筛选出不敏感变量。预选方法的诊断准确率和计算时间分别提高3.7%和40%。最后,利用最优传感器集收集的数据建立基于一维卷积神经网络(CNN)的故障诊断模型。结果表明,与其他流行的诊断模型相比,所提出的健康诊断框架具有更好的诊断性能,有利于在线诊断,准确率达到99.2%,计算效率更高,收敛速度更快,训练误差更小。结果表明,该健康诊断框架具有更快的收敛速度和更小的训练误差,可以显著降低计算成本。
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
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
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