Inner Temperature Distribution Estimation Based on Unscented Kalman Filter Algorithm for Planar Solid Oxide Fuel Cell Stack

The uniform distribution of the internal temperature of the solid oxide fuel cell (SOFC) stack is an important index of its thermal safety and electrical performance. However, due to the strict requirements on the sealing for the stack, it is not possible to install thermocouples or other temperature detection devices to directly measure the internal temperature of the stack at an acceptable cost. With the limited and easily measured external parameter of the stack, quickly and accurately estimating the temperature distribution inside the stack is the key point for the stack internal temperature management. In this paper, the input variables and outlet temperatures of the stack are selected, and an internal temperature distribution observer based on the unscented kalman filter (UKF) algorithm is designed to achieve fast and accurate estimation of SOFC internal temperature. First of all, we build a mechanism model of SOFC stack and divides the stack into five nodes, which can reflect the temperatures inside the stack. Then, experimental data is used to verify the accuracy of the model and ensure the reliability of the subsequent model-based internal temperature estimation. Simulation results show that the designed UKF temperature observer can quickly and accurately estimate the actual temperature inside the stack, the estimated error is less than 0.05%. When the stack states change, the designed observer can accurately track the stack temperature dynamically, which indicates that the observer has good dynamic and static performance.