Optimal estimation of proton exchange membrane fuel cell model parameters based on an improved chicken swarm optimization algorithm

Proton exchange membrane fuel cell (PEMFC) has been gradually applied in new energy vehicles, aviation and other industries, attracting widespread attention. Accurately identifying unknown parameters in the mathematical model of PEMFC is beneficial to the simulation, control and prediction of its output Current-Voltage curve. In order to identify the optimal unknown parameters, based on basic Chicken Swarm Optimization, this paper introduces positive/negative learning strategies for roosters and positive learning strategies for hens and chicks. An Improved Chicken Swarm Optimization algorithm is proposed. Compared with Particle Swarm Optimization, Salp Swarm Algorithm, Whale Optimization Algorithm and basic CSO algorithm, the proposed algorithm shows better convergence and accuracy. The five algorithms are applied to three common stacks (250W PEMFC, NedStack PS6 PEMFC, Ballard Mark V) and PEMFC monomer for model unknown parameter identification and optimization. The results show that, the ICSO algorithm obtains the minimum integral of absolute error of the actual stack voltage and the simulated stack voltage in the three test stacks and a PEMFC monomer, which are 2.288, 5.857, 2.407 and 0.408, the ICSO algorithm has a maximum increase of 8.63%, 4.52%, 6.20% and 64.83% in accuracy, respectively. The simulation data agrees well with the experimental data. These indicating that the mathematical model of PEMFC based on ICSO algorithm can accurately simulate the polarization curve at different temperatures and partial pressures, and it can be obtained that with the increase of temperature and partial pressure, the output performance of the PEMFC is also getting better.