Abstract

Solid oxide fuel cell (SOFC) stack internal temperature distribution is an important index for stack performance management. Due to the strict gas sealing requirement of SOFC, temperature measuring devices cannot be installed within an acceptable cost and designing an observer to estimate stack inner temperature is an efficient way. However, as for the hydrocarbon fueled SOFC system, the dimension of system input and state variables is very high due to the complex fuel composition, which challenges the observer design. In this work, from the perspective of practical application, a novel method that quickly obtain the stack temperature distribution by extracting spatial-temporal stack temperature distribution features under different operating conditions is proposed. Firstly, the temperature distribution data is generated by a validated mechanism SOFC system model, and then the spatial and temporal features of the temperature distribution are grasped by employing Multivariable Linear Regression (MLR) algorithm. Fusing the information of SOFC center node temperature and the spatial- temporal temperature distribution laws, the SOFC temperature distribution can be obtained. Simulation results show that the MLR model can accurately describe the temperature distribution characteristics under various operation conditions, which paves the way for quickly and accurately obtaining of SOFC stack internal temperature distribution.

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