Solid Oxide Fuel Cells (SOFCs) are emerging as a leading solution in sustainable power generation, boasting high power-to-energy density and minimal emissions. With efficiencies potentially exceeding 60% for electricity generation alone and up to 85% when in cogeneration applications, SOFCs significantly outperform traditional combustion-based technologies, which typically achieve efficiencies of around 35–40%. Operating effectively at elevated temperatures (600 °C to 1000 °C), SOFCs not only offer superior efficiency but also generate high-grade waste heat, making them ideal for cogeneration applications. However, these high operational temperatures pose significant thermal management challenges, necessitating innovative solutions to maintain system stability and longevity. This review aims to address these challenges by offering an exhaustive analysis of the latest advancements in SOFC thermal management. We begin by contextualizing the significance of thermal management in SOFC performance, focusing on its role in enhancing operational stability and minimizing thermal stresses. The core of this review delves into various thermal management subsystems such as afterburners, heat exchangers, and advanced thermal regulation strategies. A comprehensive examination of the recent literature is presented, highlighting innovations in subsystem design, fuel management, flow channel configuration, heat pipe integration, and efficient waste heat recovery techniques. In conclusion, we provide a forward-looking perspective on the state of research in SOFC thermal management, identifying potential avenues for future advancements and their implications for the broader field of sustainable energy technologies.

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.