Summary Proton Exchange Membrane Fuel Cells (PEMFC) have been one of the most promising energy alternatives to the non‐renewable resources of energy in the last few years owing to their numerous advantages such as reliability and pollution‐free, sustainability, steady power generation, and non‐self‐discharging. Due to this, fuel cells have a wide range of applications in different areas. Therefore, it is very crucial to do an accurate and precise estimation of the parameters of PEMFC as modeling their characteristics has huge significance in the study, simulation, and development of highly efficient fuel cells. In this study, a new hybrid algorithm based on two widely used meta‐heuristic algorithms that is, Particle Swarm Optimization (PSO) and Dingo Optimizer (DOX) is developed in order to estimate the parameters of PEMFC. First of all, the proposed algorithm is benchmarked on 10 functions in order to justify the algorithm. Further, the results of PEMFC parameter estimation obtained by the new proposed Hybrid Particle Swarm Optimization Dingo Optimizer (HPSODOX) algorithm are compared with other meta‐heuristic algorithms that is, Particle Swarm Optimization (PSO), Dingo Optimizer (DOX), Grey Wolf Optimization (GWO) algorithm, and hybrid algorithms that is, Grey Wolf Optimization ‐Cuckoo Search (GWOCS), and PSOGWO. Then, the evaluation metric used in this manuscript is the Sum of Square Error (SSE). The new proposed hybrid algorithm performs better than other meta‐heuristic algorithms as it has the minimum value of SSE. For the datasheet of Ballard Mark V, the complete statistical error analysis and non‐parametric tests are carried out in order to evaluate the performance and superiority of the new proposed hybrid algorithm.
Because of the current increase in energy requirement, reduction in fossil fuels, and global warming, as well as pollution, a suitable and promising alternative to the non-renewable energy sources is proton exchange membrane fuel cells. Hence, the efficiency of the renewable energy source can be increased by extracting the precise values for each of the parameters of the renewable mathematical model. Various optimization algorithms have been proposed and developed in order to estimate the parameters of proton exchange membrane fuel cells. In this manuscript, a novel hybrid algorithm, i.e., Hybrid Particle Swarm Optimization Puffer Fish (HPSOPF), based on the Particle Swarm Optimization and Puffer Fish algorithms, was proposed to estimate the proton exchange membrane fuel cell parameters. The two models were taken for the parameter estimation of proton exchange membrane fuel cells, i.e., Ballard Mark V and Avista SR-12 model. Firstly, justification of the proposed algorithm was achieved by benchmarking it on 10 functions and then a comparison of the parameter estimation results obtained using the Hybrid Particle Swarm Optimization Puffer Fish algorithm was done with other meta-heuristic algorithms, i.e., Particle Swarm Optimization, Puffer Fish algorithm, Grey Wolf Optimization, Grey Wolf Optimization Cuckoo Search, and Particle Swarm Optimization Grey Wolf Optimization. The sum of the square error was used as an evaluation metric for the performance evaluation and efficiency of the proposed algorithm. The results obtained show that the value of the sum of square error was smallest in the case of the proposed HPSOPF, while for the Ballard Mark V model it was 6.621 × 10−9 and for the Avista SR-12 model it was 5.65 × 10−8. To check the superiority and robustness of the proposed algorithm computation time, voltage–current (V–I) curve, power–current (P–I) curve, convergence curve, different operating temperature conditions, and different pressure results were obtained. From these results, it is concluded that the Hybrid Particle Swarm Optimization Puffer Fish algorithm had a better performance in comparison with the other compared algorithms. Furthermore, a non-parametric test, i.e., the Friedman Ranking Test, was performed and the results demonstrate that the efficiency and robustness of the proposed hybrid algorithm was superior.
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