Solid Oxide Fuel Cell Parameter Extraction via Chaos Game Optimization

For the purposes of reliably evaluating the actual performance and optimal controlling of solid oxide fuel cell (SOFC) systems, SOFC parameter extraction derived from accurate modeling is indispensable. However, the strong coupling and inherent nonlinearity of SOFC model severely hinder traditional methods to obtain various gratified parameters. An innovative meta-heuristic algorithm based on chaos game optimization is proposed to overpower these thorny obstacles and accurately extract the parameters in this paper. Besides, on the basis of test data provided by the Energy Research Centre of the Netherlands (ECN), the optimization performance of chaos game optimization (CGO) is comprehensively and thoroughly compared with that of grey wolf optimization (GWO). According to the simulation results, the optimum root mean square error (RMSE) achieved by CGO is 29.19% less than that achieved by GWO in parameter extraction, which fully shows that CGO as a complete method can provide better and competitive results in considered design examples. It is proved that compared with that of GWO, the precision, accuracy, and stability of the optimization results can be remarkably improved via the application of CGO.