Hybrid algorithm for parameter estimation of fuel cell

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Abstract

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.

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