MODELLING OF SOFC STACK TEMPERATURE FIELD BASED ON DEEP NEURAL NETWORK

Wu Xin; Wu Wanzhe; Bai Hao; Wang Qi; Xiong Xingyu

doi:10.19912/j.0254-0096.tynxb.2022-0306

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (7) : 55-60. DOI: 10.19912/j.0254-0096.tynxb.2022-0306

MODELLING OF SOFC STACK TEMPERATURE FIELD BASED ON DEEP NEURAL NETWORK

Wu Xin, Wu Wanzhe, Bai Hao, Wang Qi, Xiong Xingyu

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Abstract

Based on the quartz fiber temperature sensor and the Cartesian coordinate manipulator, this paper designs and builds the SOFC stack temperature field measurement system. Then, the temperature data of the cathode port in the emulation SOFC stack are measured through the built measurement system. Based on the collected data, the emulation stack temperature field model is established based on deep neural network method, and compared with the stack temperature field model based on support vector machines （SVM）. The results show that the stack temperature field model with deep neural network owns shorter training time and higher prediction accuracy. Its mean absolute prediction error and root mean square error are 45.2% and 47.4% respectively of the stack temperature field model of support vector machines, which is more convenient for the application of SOFC stack temperature field modelling in this paper.

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solid oxide fuel cell / deep neural networks / support vector machine / temperature sensor / stack temperature field measurement

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Wu Xin, Wu Wanzhe, Bai Hao, Wang Qi, Xiong Xingyu. MODELLING OF SOFC STACK TEMPERATURE FIELD BASED ON DEEP NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2023, 44(7): 55-60 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0306

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