Extraction of PEM fuel cell variables based on modified hippopotamus optimization algorithm

Parameter identification of a PEMFC is the process of using optimization approaches to determine the best unknown variables suitable for the development of a precision fuel cell performance forecasting model. Since these variables may not always be mentioned in the manufacturer's datasheet, identifying them is essential to accurately forecasting and evaluating the fuel cell's performance. Like many swarm-based algorithms, the Hippopotamus Optimization (HO) algorithm is prone to getting trapped in local optima, which can hinder its ability to identify global optimal solutions. This limitation becomes particularly pronounced in complex, constrained optimization problems. Additionally, the algorithm's reliance on previous solutions for updating positions often leads to slow convergence. To address these challenges, a modified version of the HO algorithm (MHO) is proposed that integrates two innovative strategies: a novel exploitation mechanism and an Enhanced Solution Quality method. Five distinct optimization techniques; the MHO algorithm, the Grey Wolf Optimizer (GWO), the HO algorithm, the Chimp Optimization Algorithm (ChOA), and the sine cosine algorithm (SCA) are used to calculate the six unknown parameters of a PEMFC. The sum square error (SSE) between the estimated and measured cell voltages is the fitness function that needs to be minimized during optimization, and these six parameters act as choice variables. HO, GWO, SCA, and ChOA came after the MHO algorithm, which produced an SSE of 1.748996055. Because MHO accurately anticipated the performance of the fuel cell, it is suitable for the development of digital twins for fuel-cell applications and control systems for the automobile industry. Furthermore, it was demonstrated that MHO converged faster than the other techniques studied.