Analysis of the novel dynamic semiempirical model of proton exchange membrane fuel cell by incorporating ambient condition variations

Abstract

Proton exchange membrane fuel cell PEMFC is nonlinear source so modeling its output is essential. Also PEMFC is extremely prone to change in ambient conditions. Previously the complex modeling techniques have been used to model PEMFC voltage, but these technique are not good for online purposes. Hence a semiempirical approach has been suggested for PEMFC voltage prediction which uses the combination of theoretical and empirical equations. But most of the semiempirical models have lot of deficiencies which need to be corrected with the help of experimentation of different PEMFC systems at different conditions. In this research paper, a novel semiempirical model has been chosen which is less complex, dynamic and has the ability to diagnose fault as well. The model is only tested on two PEMFC stacks but never tested on single PEMFC. In this research, the model has been tested on single-cell PEMFC system and the parameters are optimized by using lightening search algorithm. The temperature and voltage model have been validated, and the new optimized parameters are recorded for different ambient conditions. The model discrepancies have been identified, and the new equations for parameters have been proposed which can be helpful in the making of generic model.

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

“…The drawbacks mentioned in this reference are: (1) Obtaining test data for the whole life cycle is challenging, (2) The degradation processes of leaking current impact the performance. Reference [33] presents a novel semi-empirical model for PEMFC voltage estimation, optimizing parameters using the lightning search algorithm and validating the model under various conditions. The drawbacks mentioned in this reference are: (1) The semiempirical model for voltage prediction needs correction through experimentation, (2) The parameters in the model vary with ambient conditions, affecting accuracy, and (3) Semiempirical models lack the complexity and accuracy.…”