Solid oxide fuel cell (SOFC) performance degradation analysis and optimization studies are important prerequisites for its commercialization. Reviewing and summarizing SOFC performance degradation studies can help researchers identify research gaps and increase investment in weak areas. In this study, to help researchers purposely improve system performance, degradation mechanism analysis, degradation performance prediction, and degradation performance optimization studies are sorted out. In the review, it is found that the degradation mechanism analysis studies can help to improve the system structure. Degradation mechanism analysis studies can be performed at the stack level and system level, respectively. Degradation performance prediction can help to take measures to mitigate degradation in advance. The main tools of prediction study can be divided into model-based, data-based, electrochemical impedance spectroscopy-based, and image-based approaches. Degradation performance optimization can improve the system performance based on degradation mechanism analysis and performance prediction results. The optimization study focuses on two aspects of constitutive improvement and health controller design. However, the existing research is not yet complete. In-depth studies on performance degradation are still needed to achieve further SOFC commercialization. This paper summarizes mainstream research methods, as well as deficiencies that can provide partial theoretical guidance for SOFC performance enhancement.

Modern greenhouses need a certain equipment to achieve the expected environmental temperature in a short time while saving energy. To achieve greenhouse temperature management and energy savings through intelligent control, a greenhouse mechanism model is first built to study the controller design for a greenhouse located in Yiyang city in this paper. Then, the model accuracy is verified by experimental data. Based on the verified model, two intelligent control technologies are proposed, namely, active disturbance rejection control and fuzzy active disturbance rejection control. Methods are adopted to adjust skylight opening and thermal air conditioning for greenhouse temperature control and energy savings. In 60 hours of continuous working time, fuzzy active disturbance rejection control takes 10 hours less than active disturbance rejection control to make the greenhouse temperature reach the ideal steady state, and the temperature overshoot is reduced by 60%. Through analysis, the proposed fuzzy active disturbance rejection control method for greenhouse temperature management can achieve 15% energy savings. The proposed intelligent control technology can also be applied to temperature management for real greenhouses.