Parameters Estimation of Proton Exchange Membrane Fuel Cell Model Based on an Improved Walrus Optimization Algorithm

Abstract

Proton Exchange Membrane Fuel Cells (PEMFCs) play a crucial role in the advancement of clean hydrogen vehicles. Their ability to convert hydrogen into electricity makes them promising candidates to replace conventional engines. However, optimizing their performance and efficiency necessitates accurate modeling techniques capable of simulating their behavior. In this context, this paper proposes an advanced approach for precise parameter estimation in PEMFC models. Employing an Enhanced Walrus Optimization (EWO) algorithm integrated with Lévy flight exploration, the approach tackles the inherent nonlinearity of PEMFC systems. The technique aims to minimize the squared error between measured and simulated terminal voltage, thereby ensuring superior accuracy and robustness compared to established algorithms. The effectiveness of the proposed model is validated through comparisons between theoretical simulations and experimental measurements. The findings demonstrate the efficacy of the EWO algorithm, consistently outperforming previously published algorithms and achieving notably lower errors. Moreover, the incorporation of Lévy flights enhances the algorithm's capabilities, leading to expedited convergence and more accurate parameter estimations. Beyond facilitating precise parameter estimation, this enhanced modeling strategy opens avenues for refining design and optimization strategies in fuel cell research and development. The major contributions of this paper include the enhancement of the WO algorithm, evaluation of theoretical model accuracy, and robustness assessment of the EWO in optimizing the PEMFC model. By furnishing accurate models validated through experimental evidence, this enhanced modeling strategy paves the way for refining design and optimization strategies in fuel cell research and development.

An Accurate Parameter Estimation Method of the Voltage Model for Proton Exchange Membrane Fuel Cells

“…To stably, efficiently and accurately discern unidentified features, many investigations have been done, mainly classified into traditional methods and meta-heuristic-based methods [16]. While for this multivariable, highly coupled and nonlinear parameter identification problem, conventional approaches are often ignored as a result of their shortcomings such as being time-consuming and offering low parameter accuracy.…”