A comprehensive and comparative review on parameter estimation methods for modelling proton exchange membrane fuel cell

Proton Exchange Membrane Fuel Cell (PEMFC) is one of the rising clean form of energy. Due to various advantages of PEMFCs, they have widespread applications. The structure of Fuel Cell systems is multivariate and non-linear. Thus it becomes necessary to model the system properly for simulation, design and analysis purpose. The mathematical model developed on the basis of empirical equations are used for designing purpose of PEMFC. The voltage obtained at the output of fuel cell depends on losses occurring in the system namely activation loss, concentration loss, and ohmic loss. These losses depend on certain parametric coefficients. Thus, these parametric coefficients are optimized to minimize the losses and to improve the output of PEMFC. Researchers have used various optimization methods to estimate and optimize parametric coefficients that affect the steady state and dynamic behaviour of different fuel cell models as these values are not provided in the manufacturer's datasheet. This paper presents an inclusive review of various technique used for parameter estimation of PEMFC model such as Least Square method, Artificial Neural Networks, metaheuristic techniques and bio-inspired methods. A brief overview of the methods used, novelty introduced to the methods to solve the objective function and get exact V-I curve along with the validation technique has been presented. This article will be a state of the art for researchers in this field to come up with new techniques or modified version of the techniques for optimizing the coefficients by comparing them with the methods already used in literature. This review will help engineers and researchers to visualise more advancements that could be done in the field of fuel cell system to make it most promising and reliable clean energy system.