Power and Voltage Modelling of a Proton-Exchange Membrane Fuel Cell Using Artificial Neural Networks

Abstract

A proton exchange membrane fuel cell (PEMFC) is a more environmentally friendly alternative to deliver electric power in various applications, including in the transportation industry. As PEMFC performance characteristics are inherently nonlinear and involved, the prediction of the performance in a given application for different operating conditions is important in order to optimize the efficiency of the system. Thus, modelling using artificial neural networks (ANNs) to predict its performance can significantly improve the capabilities of handling the multi-variable nonlinear performance of the PEMFC. However, further investigation is needed to develop a dynamic model using ANNs to predict the transient behavior of a PEMFC. This paper predicts the dynamic electrical and thermal performance of a PEMFC stack under various operating conditions. The input variables of the PEMFC stack for the analysis consist of the cathode inlet temperature, anode inlet pressure, anode and cathode inlet flow rates, and stack current. The performances of the ANN models using three different learning algorithms are determined based on the stack voltage and temperature, which have been shown to be consistently predicted by most of these models. Almost all models with varying hidden neurons have coefficients of determination of 0.9 or higher and mean squared errors of less than 5. Thus, the results show promise for dynamic modelling approaches using ANNs for the development of optimal operation of a PEMFC in various system applications.

Hybrid fuel cell-supercapacitor system: modeling and energy management using Proteus

“…The FC system is modeled by a complex system that enables it to generate its dynamic behavior by modeling various phenomena, including energy losses, double layer capacity, and the chemical delay due to the hydrogen and oxygen chemical reaction. Alternatively, there are models based on energy representations such as the Bond graph (BG) and energetic macroscopic representation (EMR) [34], [35], and models based on neural networks [40], [41]. However, the disadvantage of these models is the fact that they require a longer computation time.…”

Neural network and URED observer based fast terminal integral sliding mode control for energy efficient polymer electrolyte membrane fuel cell used in vehicular technologies

“…In this situation, a neural network can be very useful in accurately predicting the stack temperature. Neural networks have been shown to be effective at modeling complex systems and can be used to predict various variables in PEMFCs, including temperature [41,42]. In particular, NNs have been used to model the relationship between temperature and other variables such as current density, gas flow rate, and humidity [43].…”

A Dimension-Reduced Artificial Neural Network Model for the Cell Voltage Consistency Prediction of a Proton Exchange Membrane Fuel Cell Stack

“…Long et al developed an ANN model to predict the remaining useful life of the PEMFC in comparison with other data-driven models [37]. Wilberforce et al proposed an ANN model to predict the dynamic electrical and thermal performance of the PEMFC stack under various operating conditions [38,39]. Musharavati et al investigated a bio-inspired ANN model to find the optimal design and control variables for fuel cell dynamic operations [40].…”