Evaluation of performance and power consumption when different controllers are used on adaptive cruise control of an electric vehicle

Research on electric vehicles became a very hot topic in the last few years because they have advantageous in terms of efficiency, carbon emission and performance compared to combustion engines. However, high battery cost, long charging time and limited traveling range of electric vehicles are the challenges that researchers are still currently working on. Innovative battery and its management systems have been developed to increase travelling range of electric vehicles. Furthermore, lowering the battery power consumption of the comfort and safety systems in the vehicle is another research topic to increase battery range. Adaptive Cruise Control (ACC), which is one of the comfort and safety systems, has also been used to increase the travelling range of electric vehicles. In this thesis, we design ACC with three well-known Proportional-Integral-Derivative (PID), Fuzzy and Model Predictive Controller (MPC) controllers, and evaluate their acceleration/deceleration performance, and power consumption. Initially, the dynamic model of an electric vehicle (LCV), which includes electric vehicle longitude dynamic, and battery consumption equations, is developed. Later, ACC mathematical equations with Proportional-Integral-Derivative (PID), Fuzzy and Model Predictive Controller (MPC) are derived, and integrated inside the dynamic model of the electrical vehicle. Simulation experiments are performed to evaluate the performance and power consumption when these three controllers are used on ACC of the electrical vehicle.