Comparative Study of Recurrent and Non Recurrent Neural Network Based Approach for Modeling of PEM Fuel Cell Powered Electric Vehicle

Abstract

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.