Abstract: A coordinating optimization method based on artificial ecosystem-based optimization (AEO) algorithm and manta ray foraging optimization (MRFO) algorithm is proposed in this paper, i.e. AEO-MRFO coordinating optimizer (EMCO), which is utilized for parameter identification of solid oxide fuel cell (SOFC). To enhance the precision and the stability of parameter identification of SOFC, EMCO discards strong random search operations of cyclone foraging operator of MRFO, and dynamically coordinates decomposition operator of AEO with revised somersault foraging operator of MRFO, which can properly balance the local exploitation and the global exploration in the optimization process. Four case studies are carried out to investigate the performance of EMCO for parameter identification of SOFC is investigated through the studies in four cases, including two test data sets of single SOFCs developed by Energy Research Centre of Netherlands and CEREL of Poland respectively, as well as two experimental data sets of a 5 kW SOFC stack at Montana State University under two different operating conditions. Besides, the impacts for the precision of the parameter identification have also been studied under variable temperatures and pressures of SOFC stack. The simulation results indicate that, compared with ant colony optimization (ACO) algorithm, equilibrium optimizer (EO), grey wolf optimization (GWO) algorithm, heap-based optimizer (HBO), particle swarm optimization (PSO) algorithm, AEO algorithm and MRFO algorithm, EMCO has the superiority of rapid, accuracy and stability for SOFC parameter identification.