Benchmarking PID and BELBIC Controllers for Electric Vehicle Speed Regulation Under Realistic Driving Conditions

This study presents a comparative analysis of Proportional-Integral-Derivative (PID) and Brain Emotional Learning Based Intelligent Control (BELBIC) strategies for electric vehicle (EV) speed optimization using MATLAB/Simulink. The performance of both controllers is evaluated under two standardized drive cycles: ECE-R15 (Urban Driving Cycle) and New York Composite (NYComp). ECE-R15 simulates typical European urban driving, while NYComp, also known as the New York Bus Composite (NYBC) cycle, reflects real-world heavy-duty vehicle operation in New York City and is a compressed version of the U.S. EPA’s unfiltered bus test cycle. These drive cycles offer a comprehensive testing environment to assess controller performance. Key performance indicators—response time, steady-state error, and robustness—are analyzed to determine each controller's effectiveness. Results highlight the strengths and limitations of PID and BELBIC approaches, offering insight into their practical use for urban EV applications. This research supports the advancement ofintelligent EV control systems using MATLAB/Simulink, promoting improved efficiency and reliability in urban transportation.