Data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator
Data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator
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DOI：
10.1016/j.egyai.2023.100289
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发表时间：
2023-07
期刊：
影响因子：
--
通讯作者：
Da Huo;Carrie M. Hall
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文献类型：
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作者：
Da Huo;Carrie M. Hall
在这项研究中，提出了一种新的应用程序的Koopman运营商面向控制建模的质子交换膜燃料电池（PEMFC）堆。本文的主要贡献是：（1）基于Koopman的燃料电池堆模型的设计，结合K折交叉验证，不同的提升维度，径向基函数（RBF）和预测范围;（2）基于Koopman的方法与更传统的基于物理的模型的性能比较。结果表明，基于Koopman的模型在预测燃料电池堆行为方面具有较高的准确性，误差小于3%。所提出的方法提供了几个优点，包括提高计算效率，减少计算负担，并提高可解释性。这项研究表明，Koopman运营商的建模和控制的PEMFC的适用性，并提供了一种新的控制为导向的建模方法，使准确和有效的预测燃料电池堆有价值的见解。
In this study, a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell (PEMFC) stacks is proposed. The primary contributions of this paper are: (1) the design of Koopman-based models for a fuel cell stack, incorporating K-fold cross-validation, varying lifted dimensions, radial basis functions (RBFs), and prediction horizons; and (2) comparison of the performance of Koopman-based approach with a more traditional physics-based model. The results demonstrate the high accuracy of the Koopman-based model in predicting fuel cell stack behavior, with an error of less than 3%. The proposed approach offers several advantages, including enhanced computational efficiency, reduced computational burden, and improved interpretability. This study demonstrates the suitability of the Koopman operator for the modeling and control of PEMFCs and provides valuable insights into a novel control-oriented modeling approach that enables accurate and efficient predictions for fuel cell stacks.