Parameter identification for solid oxide fuel cells using cooperative barebone particle swarm optimization with hybrid learning

Solid oxide fuel cell (SOFC) has been widely recognized as one of the most promising fuel cells. The SOFC performance is highly influenced by several parameters associated with the internal multi-physicochemical processes. In this work, the optimal modeling strategy is designed to determine the parameters of SOFC using a simple and efficient barebone particle swarm optimization (BPSO) algorithm. The cooperative coevolution strategy is applied to divide the output voltage function into four subfunctions based on the interdependence among variables. To the nonlinear characteristic of SOFC model, a hybrid learning strategy is proposed for BPSO to ensure a good balance between exploration and exploitation. The experimental results illustrate the effectiveness of the proposed algorithm. The comparisons also indicate that cooperative coevolution strategy and hybrid learning improve the performance of original PSO algorithm, offering better approximation effect and stronger robustness.