PEMFC Fractional-order Subspace Identification Model

A proton exchange membrane fuel cell(PEMFC) is a new type of hydrogen fuel cell that plays an indispensable role in an energy network. However, the multivariable and fractional-order characteristics of PEMFC make it difficult to establish a practical model. Herein, a fractional-order subspace identification model based on the adaptive monarch butterfly optimization algorithm with opposition-based learning(ALMBO) algorithm is proposed for PEMFC. Introducing the fractional-order theory into the subspace identification method by adopting a Poisson filter for with input and output data,a weight matrix is proposed to improve the identification accuracy. Additionally, the ALMBO algorithm is employed to optimize the parameters of the Poisson filter and fractional order, which introduces an opposition-based learning strategy into the migration operator and incorporates adaptive weights to improve the optimization accuracy and prevent falling into a locally optimal solution. Finally, the PEMFC fractional-order subspace identification model is established, which can accurately describe the dynamic process of PEMFC.