A study of stability and power consumption of electric vehicles using different modern control strategies

This research paper aims at applying modern control techniques to study the stability and power consumption of electric vehicles. The main purpose is to propose an artificial neural network controller to estimate the direct yaw moment required for stabilizing the dynamics of electric vehicles with four in-wheel motors. Only one state variable, that can be measured using a cheap and standard sensor, is used to define the error signal for the controller. The algorithm of equality-constrained quadratic program is used to split the estimated yaw moment onto the four in-wheel motors. The neural network controller is trained using data obtained from fuzzy logic controller. In addition, the classical sliding mode control is applied for the sake of comparison. Simulation results prove that in comparison to classical controller, the mentioned modern controller provides better stability of the electric vehicle's lateral dynamics and lower usage of electric power for the motors. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.