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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Short-Term Prognostics of PEM Fuel Cells: A Comparative and Improvement Study
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Short-Term Prognostics of PEM Fuel Cells: A Comparative and Improvement Study

机译：PEM燃料电池短期预测：比较和改进研究

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摘要

As one of the most promising types of fuel cells, the proton exchange membrane fuel cells (PEMFCs) can be utilized in many applications. However, it still faces two main challenges before large-scale industrial applications, namely short lifetime and high costs. The aim of this paper is to establish an accurate online short-term prognostics method to help users extend the lifetime and reduce the cost of PEMFCs. First, we compare the short-term prognostics accuracy and computational efficiency of several different methods including the Elman neural network, the group method of data handling, the adaptive neuro-fuzzy inference system (ANFIS) with different fuzzy inference system creation strategies, and the wavelet decomposition approach. Test results show that the ANFIS with fuzzy c-means (ANFIS-FCM) strategy has the best short-term prognostics performance. Then, we propose an automatic parameter adjustment method for ANFIS-FCM by using the particle swarm optimization (PSO) algorithm. Test results show that the PSO algorithm can effectively adjust the parameters and achieve improved prognostics results. Finally, the proposed prognostics methods are verified on a PEMFC experimental platform. Experimental results show that the proposed methods have great potential for practical applications.
机译：作为最有希望的燃料电池类型之一，质子交换膜燃料电池（PEMFC）可用于许多应用中。然而，它仍然面临大规模工业应用前的两个主要挑战，即短暂的寿命和高成本。本文的目的是建立一个准确的在线短期预测方法，以帮助用户延长寿命并降低PEMFC的成本。首先，我们比较几种不同方法的短期预测准确性和计算效率，包括ELMAN神经网络，数据处理的组方法，具有不同模糊推理系统创作策略的自适应神经模糊推理系统（ANFIS），以及小波分解方法。测试结果表明，具有模糊C-Means（ANFIS-FCM）策略的ANFI具有最佳的短期预测性能。然后，通过使用粒子群优化（PSO）算法，为ANFIS-FCM提出自动参数调整方法。测试结果表明，PSO算法可以有效调整参数，实现改进的预测结果。最后，在PEMFC实验平台上验证了所提出的预测方法。实验结果表明，该方法具有巨大的实际应用潜力。

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