COMPARATIVE ANALYSIS OF VARIOUS CONTROLLERS FOR CRUISE CONTROL OF AN ELECTRICAL VEHICLE DRIVE

Khuban L. Khan,∗ Solihah S. Shiekh,∗ Farzan F. Malik,∗ Tanzeela Mir,∗ Munazah Mushtaq,∗ and Shoeb Hussain∗

Keywords

Electric vehicle, fuzzy, ANFIS, MPC, neural network, SMC

Abstract

Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) have emerged as a good alternative for the conventional IC engine vehicles due to the depleting levels of low-cost fossil fuels and ever increasing environmental pollution. There are, however, some issues related to the effective power conversions due to power controllers, energysaving and good battery management in the electrical vehicles. PID controllers are presently most widely used in EVs due to their simplicity and ease of implementation. Owing to numerous advantages, the modern controllers offer, implementation of these controllers cut is the need of the hour to improve the dynamics of the EV drive and increase its efficiency. This paper presents a comparative analysis of various controllers, viz. PID controller, Fuzzy Logic controller, Artificiial Neural Network (ANN) controller, Sliding Mode Controller (SMC), Adaptive Neural Fuzzy Inference System (ANFIS) controller and Model Predictive Controller (MPC) in cruise control of an EV. The aim is to control the speed of an EV drive and increase its efficiency using advanced control strategies. MATLAB simulation of the EV drive has been carried out to understand its dynamic characteristics.

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