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.

The research of fault prognosis for solid oxide fuel cell (SOFC) plays a critical role in increasing lifetime and improving durability. Therefore, a model-based SOFC fault prognosis method combining the mechanism degradation process model and particle filtering is proposed. The state of health (SOH), as well as end of life (EOL) and remaining useful life (RUL) are estimated by the proposed method. First, the degradation resistance is separated in the SOFC electrical balance to signify SOH. Furthermore, the feasibility of this scheme is verified by the parameter identification results of the SOFC's electrochemical performance curves. Second, the degradation process model of SOFC is developed by aging mechanism analysis. On this basis, an online parameter estimation method based on a particle filter is utilized to estimate SOH and its probability density function (PDF). Moreover, EOL, RUL, and their PDFs are predicted by extrapolating the time when the SOH reaches the performance threshold. Finally, the application cases of actual SOFC test data demonstrate that the proposed fault prognosis method effectively predicts the SOH evolution trend, and then gives reasonable EOL, RUL and their posterior PDFs.

The evolution of performance degradation has become a major obstacle to the long-life operation of the Solid Oxide Fuel Cell (SOFC) system. The feasibility of employing degradation resistance to assess the State of Health (SOH) is proposed and verified. In addition, a real-time Unscented Kalman Filter (UKF) based SOH estimation method is further proposed to eliminate the disturbance of calculating the SOH directly utilizing measurement and electric balance model. The results of real-time SOH estimation with an UKF under constant and varying load conditions demonstrate the feasibility and effectiveness of the SOFC performance degradation assessment method.

Performance prediction technology enhances the dependability and safety of solid oxide fuel cell (SOFC) systems. However, unreasonable prediction objects and insufficient prediction accuracy impede the advancement of prediction methodologies in SOFC systems. To better predict SOFC system performance changes, two algorithms are developed in this paper: a mutual information (MI)-based feature selection algorithm that identifies variables sensitive to SOFC performance changes, and a particle swarm algorithm optimized Back Propagation (BP) neural network (PSO_BP). The PSO_BP algorithm predicts hard-to-measure variables (e.g., heat exchanger temperature, combustion chamber temperature) using easy-to-measure data. Real data tests reveal significant improvements in feature reduction, prediction accuracy, and speed. The MI algorithm reduces feature variables by 70% (from 40 to 12), aiding sensor layout optimization. The PSO_BP algorithm trains in 2s and achieves less than 0.5% prediction error. It can also predict hard-to-measure temperature data, which can assist researchers in optimizing SOFC system performance.