Data-driven prediction of temperature variations in an open cathode proton exchange membrane fuel cell stack using Koopman operator

作     者：Da Huo Carrie M.Hall 

作者机构：MechanicalMaterialsand Aerospace Engineering DepartmentIllinois Institute of Technology10 West 35th StreetChicago60616ILUnited States 

出 版 物：《Energy and AI》 (能源与人工智能（英文）)

年 卷 期：2023年第14卷第4期

页      面：439-450页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：This material is based upon work supported by the National Science Foundation United States under Grant No.2135735 

主　　题：Proton exchange membrane fuel cell(PEMFC) Data-driven modeling Koopman operator Dynamic modeling Control-oriented modeling Physics-based modeling 

摘      要：In this study,a novel application of the Koopman operator for control-oriented modeling of proton exchange membrane fuel cell(PEMFC)stacks is *** 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 *** 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 *** 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.