A new optimum technique for parameter identification of the proton exchange membrane fuel cells based on improved remora optimizer

The present study proposes a new parameter estimation technique for undefined parameters of proton exchange membrane fuel cell (PEMFC) stack models. To provide an efficient model identifier, an objective function by the Sum of Squared Error (SSE) between the empirical data and the predicted date of the PEM fuel cell stack has been employed, and the aim is to minimize this objective function. Here, a new improved design of Remora Optimizer is designed and used for the minimization purpose. The improved method has been improved by self-adaptive weighting for improving the convergence rate and for providing promising balance searching in both initial and next steps, and chaos mechanism for increasing the optimization process while keeping the exploration term based on pseudorandom values. The technique is used in two test cases and then a comparison of the results with several newest techniques is performed. Simulation results showed that the proposed IROA method with 2.05% and 0.019% error ratio for NedSstack PS6 and Horizon 500W PEMFC stack models, respectively, provides the best results compared with other comparative methods. Simulation achievements indicate that the presented approach has higher efficiency than the other compared methods.