Open AccessComparative Study of Recurrent and Non Recurrent Neural Network Based Approach for Modeling of PEM Fuel Cell Powered Electric Vehicle
Author(s) -
K. Gomathi,
M. Karthik,
S. Usha
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/937/1/012040
Subject(s) - nonlinear autoregressive exogenous model , proton exchange membrane fuel cell , recurrent neural network , artificial neural network , benchmark (surveying) , electric vehicle , nonlinear system , reliability (semiconductor) , engineering , control theory (sociology) , autoregressive model , computer science , convergence (economics) , automotive engineering , fuel cells , artificial intelligence , power (physics) , mathematics , control (management) , physics , geodesy , quantum mechanics , chemical engineering , geography , economic growth , economics , econometrics
In this paper, a non recurrent Focused Time Delay Neural Network (FTDN) and a recurrent nonlinear autoregressive network with exogenous inputs (NARX) Neural Network are employed as a black box prediction model for substituting the complex conventional model of PEM 5kW Proton Exchange Membrane (PEM) Fuel Cell system. A comparative assessment is performed between the recurrent and non recurrent neural network on certain performance measures to identify an optimal network for modeling the PEM Fuel Cell System in PEM Fuel Cell powered electric vehicle application. From the simulation result, it is observed that the recurrent NARX network is showed excellent prediction ability in terms of minimizing the Mean Square Error (MSE) value with faster convergence. The optimized network is tested with intermittent data for examining its adaptability and validated with experimental benchmark data for proving its reliability. Hence the optimum network is integrated with converters and vehicle dynamic system to develop a fuel cell based electric vehicle system. The performance of the proposed vehicle is tested with US06 drive cycle pattern for justifying its reliability.