Solid oxide fuel cells (SOFCs) are an efficient, reliable and clean source of energy. Predictive modeling and analysis of their performance is becoming increasingly important, especially with the growing emphasis on sustainable development’s requirements. However, mathematical modeling is difficult due to the complexity of its internal structure. In this study, the system’s electricity generating performance and operational characteristics were analyzed using recent on-site monitoring data first. Then, based on Pearson’s correlation coefficient, some of the variables were selected to build two prediction models: an artificial neural network (ANN) model and a multiple linear regression (MLR) model. The models were evaluated on the basis of the normalized mean square error (NRMSE), which was 1.89% for the MLR model and 0.66% for the ANN model, with no overall bias. They were also compared with other existing models, and it was found that the two models used in this study have the advantage of high accuracy and low difficulty. Therefore, the models developed in this study can more accurately and effectively assess the SOFC system’s state and can better support work to improve the thermoelectric performance of SOFC systems.

The application of new energy systems for industrial production to advance air pollution prevention and control has become an irreversible trend. This development includes hybrid systems consisting of reversible solid oxide cells (RSOC) and a Li-ion battery; however, at present the energy dispatching of such systems has an unstable factor in the form of poor heat/electricity/gas controllability. Therefore, the system studied in this paper uses the Li-ion battery as the energy supply/storage case, and uses the RSOC to supply power for the Li-ion battery charge or the Li-ion battery supply power to the RSOC for hydrogen production by water electrolysis. In this hybrid system, Li-ion battery thermoelectric safety and RSOC hydrogen production stability are extremely important. However, system operation involves the switching of multiple operating conditions, and the internal thermoelectric fluctuation mechanism is not yet clear. Therefore, in this paper we propose a separate control with a dual mode for hybrid systems. Active disturbance rejection control (ADRC) with a simple structure is used to achieve Li-ion battery module thermoelectric safety and control the hydrogen production/consumption of the RSOC module in the hybrid system. The results show that the required Li-ion battery thermoelectric safety and RSOC hydrogen consumption/production requirements can be met using the proposed controller, leading to a hybrid system with high stability control.