Abstract Precise and reliable modelling of solid oxide fuel cells (SOFC) is critical for simulation analysis and optimal control of SOFC systems, which typically relies on an accurate identification of its unknown parameters. However, such problem is characterized by high non-linearity, multi-variable, and strong nonconvexity, thus conventional strategies cannot always achieve satisfactory results. With the rapid advancement of computer science, numerous meta-heuristic algorithms have been developed to solve such obstacle. However, there is no prior review to systematically summarize these approaches, thus a thorough survey study is absolutely needed. Hence, this paper undertakes a comprehensive survey on state-of-the-art meta-heuristic algorithms and related variants utilized in SOFC parameter identification. Besides, various specific experimental performance of each algorithm, and other latest estimation techniques are also elaborated. Moreover, a thorough summary is carried out to systematically compare and summarize their basic features, upon which readers can effectively grasp and utilize them. Lastly, some constructive perspectives and recommendations are proposed in conclusion for future researches. Note that hybrid variants can often achieve more satisfactory results than individual algorithms. Hence, the combination of various effective methods is crucial for novel parameter identification techniques development, upon which more reliable and efficient approaches can be devised for better simulation analysis and optimal control of SOFC systems. Besides, normalized models on such problem are also needed to be established for more accurate performance evaluation and prediction. In general, this paper can be regarded as a one-stop handbook for future in-depth studies in the related field.