A Modified Relevance Vector Machine for PEM Fuel-Cell Stack Aging Prediction
A Modified Relevance Vector Machine for PEM Fuel-Cell Stack Aging Prediction
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DOI：
10.1109/tia.2016.2524402
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发表时间：
2016-05-01
影响因子：
4.4
通讯作者：
Miraoui, Abdellatif
中科院分区：
文献类型：
--
作者：
Wu, Yiming;Breaz, Elena;Miraoui, Abdellatif
质子交换膜燃料电池（pemfc）被认为是在不久的将来绿色能源应用的潜在候选者。与其他能源选择相比，pemfc在运行过程中只需要氢气和空气。同时，作为运行过程中的副产品，产生了水。这种能量转换过程是100%环保的，对环境完全无害。然而，pemfc容易受到氢气杂质或运行条件波动的影响，这可能会导致输出性能随着时间的推移而下降。因此，性能退化的预测对PEMFC系统至关重要。本文提出了一种基于改进相关向量机（RVM）的新型PEMFC性能预测模型，并与经典支持向量机（SVM）方法进行了比较。首先简要介绍了RVM的理论公式，然后给出了利用PEMFC堆叠输出电压老化实验数据集实现RVM的步骤。针对老化数据预测问题的具体特点，提出了一种改进的RVM公式。对改进的RVM方法的结果进行了分析，并与支持向量机的结果进行了比较。结果表明，改进后的RVM比SVM具有更好的预测性能，特别是在训练数据集相对较小的情况下。这种基于改进RVM方法的新方法在预测pemfc性能退化方面的有效性得到了验证。
Proton exchange membrane fuel cells (PEMFCs) are considered as a potential candidate in the green-energy applications in the near future. Comparing with other energy options, the PEMFCs need only hydrogen and air during operation. Meanwhile, as a by-product during operation, water is produced. This energy-conversion process is 100% eco-friendly and completely unharmful to the environment. However, PEMFCs are vulnerable to the impurities of hydrogen or fluctuation of operational condition, which could cause the degradation of output performance over time during operation. Thus, the prediction of the performance degradation is critical to the PEMFC system. In this work, a novel PEMFC performance-forecasting model based on a modified relevance vector machine (RVM) has been proposed, followed by a comparison with the approach of classic support vector machine (SVM). First, the theoretical formulation of RVM is briefly introduced, then the implementation steps of RVM using the experimental aging data sets of PEMFC stack output voltage are presented. By considering the specific feature of aging data-prediction problem, an innovative modified RVM formulation is proposed. The results of proposed modified RVM method are analyzed and compared to the results of SVM. The results have demonstrated that the modified RVM can achieve better performance of prediction than SVM, especially in the cases with relatively small training data sets. This novel method based on modified RVM approach has been demonstrated to show its effectiveness on forecasting the performance degradation of PEMFCs.