Comparative Application of Speed and Regenerative Braking Process of an Electric Vehicle Using Intelligent Control Methods

Interest in alternative energy vehicles, particularly electric vehicles (EVs), has been steadily increasing due to growing concerns over environmental pollution and climate change. A key driver of this interest is the significant contribution of harmful emissions from internal combustion engine vehicles to global warming. EVs offer a promising alternative to mitigate these issues, making research into improving their efficiency essential. However, certain environmental parameters influencing EV performance are challenging to model mathematically. To address this, it is crucial to develop advanced control systems capable of ensuring optimal vehicle performance under varying conditions. In this study, a rear-wheel-drive EV was designed and equipped with a 1000 W motor drive system incorporating regenerative braking capability. A MATLAB/Simulink-based control algorithm was developed to generate the Pulse Width Modulation (PWM) signals required for motor control. Experimental validation demonstrated the effectiveness of the proposed drive system. Driving tests were conducted using the Urban Driving Cycle (UDC) and a scaled version of the New European Driving Cycle (NEDC), considering adaptive control, vehicle speed, road gradient, and vehicle parameters. Additional on-road experiments were performed on a predefined route to evaluate regenerative braking strategies employing intelligent control methods. Results show that regenerative braking increased the EV’s driving range by approximately 30%. The findings highlight the potential of the proposed system to enhance EV efficiency and promote their adoption, given their substantial environmental benefits.