Performance evaluation of an electric vehicle integrated with regenerative braking system using fuzzy controller
Abstract
Electric vehicles (EVs) receive impressive concerns worldwide due to their efficiency for reducing environmental pollution. Consequently, automotive institutions and academics are facing many challenges to provide various techniques to increase the EVs reliability. One of the crucial parameters is the regenerative braking system (RBS) that helps for optimal usage of the battery stored energy and to extend the driving range. The current research presents a new regenerative braking strategy focusing on various driving cycles to compromise the implementation of the RBS braking control and mechanical braking system using the artificial neural fuzzy system (ANFIS). The tackled different drive cycles are New York City Cycle (NYCC), New European Driving Cycle (NEDC), Federal Test Procedure 72 (FTP-72) and Federal Test Procedure 75 (FTP-75). Furthermore, the basic concepts of the modelling of electric vehicles with different road conditions are considered to demonstrate the vehicle’s dynamics performance. The MATLAB/SIMULINK software package is used for developing the model to study the regenerative braking systems performance under different conditions. The simulation results of this study show that the proposed regeneration braking strategy improves regeneration efficiency by about 40 % and extends the driving travel distance per charge up to 31 %
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