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

Available online at Journal of Power Sources 179 (2008) 683–692 Dynamic temperature modeling of an SOFC using least squares support vector machines Ying-Wei Kang a,∗, Jun Li a , Guang-Yi Cao a , Heng-Yong Tu a , Jian Lib , Jie Yangb a Institute of Fuel Cell, Shanghai Jiao Tong University, Shanghai 200240, China b School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China Received 24 November 2007; received in revised form 4 January 2008; accepted 7 January 2008 Available online 18 January 2008 Abstract 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