Efficient estimation of proton exchange membrane fuel cells parameters using a hybrid swarm intelligent algorithm

The identification of unknown parameters for proton exchange memberane fuel cells (PEMFCs) using nature-inspired optimization algorithms has emerged as a significant field of research in recent years. In the present study, a novel approach is presented, namely the hybrid Gray Particle Cuckoo (GPC) algorithm based on the hybrid properties of the grey wolf optimizer (GWO), particle swarm optimization (PSO), and cuckoo search (CS) to address the identification problem associated with PEMFCs. The effectiveness of the proposed GPC algorithm is evaluated on four commercially available PEMFCs (BCS500-W, Ballard Mark V, Temasek, as well as NedStack PS6). The fitness function has been expressed as the sum of the squared errors (SSE) that occurred between the estimated voltage and the data that corresponded to it. To further validate the model of the PEMFC, it is contrasted with other complex algorithms. The GPC algorithm showed the lowest SSE across all cases, resulting in SSE values of 0.011699, 0.813912, 2.267687, and 0.123276775 for the BCS500-W, Ballard Mark V, NedStack PS6 and Temasek PEMFC stack, respectively. Also, the PEMFC stacks are evaluated using different partial temperature and pressure conditions. In addition to real-world challenges, the GPC algorithm has been assessed on 100-digit CEC 2019 benchmarks and contrasted to other MH algorithms. Furthermore, both the parametric and non-parametric statistical tests are conducted to evaluate the efficacy of the GPC algorithm. The results in terms of mean square error (MSE), individual absolute error (IAE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE) demonstrate that the GPC algorithm is the optimal choice contrasted to other algorithms due to its better solution quality and faster convergence time.