Model Parameter Extraction for PEM Electrolyzer Using Honey Badger Algorithm

This study evaluates seven model parameters using honey badger algorithm for a proton exchange membrane (PEM) electrolyzer. Identification problem involves squared error based objective function and is defined as the sum of squared error (SSE) between the experimental and estimated voltage. The accuracy of the honey badger algorithm is validated using two different operating conditions. One condition involves temperature and pressure of 80°C and 1 bar whereas, other condition involves temperature and pressure of 50°C and 30 bar. Also, results obtained using honey badger algorithm have been compared with other algorithms such as particle swarm optimization (PSO), hybrid grey wolf-whale optimization algorithm (GWO-WOA), grey wolf optimization (GWO), and whale optimization algorithm (WOA). To identify the optimal value of unknown parameters closeness between experimental and estimated J-V curves have been checked. With a close match between J-V curves, honey badger algorithm is identified as a good identifier. Moreover, statistical analysis considering mean, standard deviation, best and worst value has been performed to check the robustness of the algorithm. Results show that the obtained values of SSE for two different operating conditions are 3.90E-04 and 4.23E-04. Also, box plot analysis and convergence curves have been reported to evaluate the effectiveness and the convergence speed of the algorithm. It is concluded that the honey badger algorithm outperforms other algorithms and proves its superiority. Furthermore, production of hydrogen at given operating conditions is analyzed.