Dynamic temperature modeling of an SOFC using least squares support vector machines

作者：Kang, Ying Wei^*; Li, Jun; Cao, Guang Yi; Tu, Heng Yong; Li, Han; Yang, He

来源：Journal of Power Sources, 2008, 179(2): 683-692.

DOI：10.1016/j.jpowsour.2008.01.022

• solid oxide fuel cell (SOFC)
• dynamic temperature model
• least squares support vector machine (LS-SVM)
• hyperparameter tuning
• genetic algorithm (GA)

Cell temperature control plays a crucial role in SOFC operation. In order to design effective temperature control strategies by model-based control methods, a dynamic temperature model of an SOFC is presented in this paper using least squares support vector machines (LS-SVMs). The nonlinear temperature dynamics of the SOFC is represented by a nonlinear autoregressive with exogenous inputs (NARXs) model that is implemented using an LS-SVM regression model. Issues concerning the development of the LS-SVM temperature model are discussed in detail, including variable selection, training set construction and tuning of the LS-SVM parameters (usually referred to as hyperparameters). Comprehensive validation tests demonstrate that the developed LS-SVM model is sufficiently accurate to be used independently from the SOFC process, emulating its temperature response from the only process input information over a relatively wide operating range. The powerful ability of the LS-SVM temperature model benefits from the approaches of constructing the training set and tuning hyperparameters automatically by the genetic algorithm (GA), besides the modeling method itself. The proposed LS-SVM temperature model can be conveniently employed to design temperature control strategies of the SOFC.

• 出版日期2008-5-1
• 单位上海交通大学; 华中科技大学