Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm

The artificial rabbits optimization (ARO) algorithm is proposed in this article to find the optimum values for uncertain parameters for the proton exchange membrane fuel cell (PEMFC) model. The voltage-current polarization curve of the PEMFC is nonlinear, and the model used in this paper to describe it is Mann's model, which has seven uncertain parameters. The sum of square errors (SSE) between the ARO-based estimated voltages of the model and the measured voltages of the fuel cell defines the objective function. The simulation results show that the ARO technique has the best SSE compared to other optimization techniques. The precision of the ARO model is evaluated by comparing the optimized model's power-current and voltage-current curves with the measured curves of three stacks which are NedStack PS6, BCS stack 500 W, and Ballard Mark V. The results show that the estimated curves and measured curves are very close which, means a high accuracy is achieved. Moreover, the ARO method shows a fast convergence curve with a minimal standard deviation. Furthermore, the PEMFC-optimized model is studied at different temperature and pressure operating conditions.

Automated summary

The artificial rabbits optimization (ARO) algorithm is proposed to find the optimum values for uncertain parameters in the proton exchange membrane fuel cell (PEMFC) model. The ARO technique achieves the best sum of square errors (SSE) compared to other optimization techniques. The accuracy of the ARO model is evaluated by comparing the optimized model's performance with the measured curves of three different fuel cell stacks.