Optimal control system design for electric vehicle

This paper presents a description of optimal control system design for electric vehicles. Optimal control is applied to optimise the performance of the electric car design with minimum use of energy and maximum average speed. Optimal control and linear quadratic regulator in electric cars have significant impact on reducing operational costs of electric vehicles and increasing electric vehicle range and speed. The simulation model of this control strategy is constructed by using MATLAB Simulink and Stateflow. The model takes into account the role of combined wind/drag, slope, and rolling loads to minimise the battery charge consumption under various driving conditions. Vehicle modelling and simulation are very important in order to design and develop electric vehicles. The multi domain complexity interacts between various mechanical and electrical components, electrical machinery, control systems, and vehicle mechanics with a high level of complexity and number of parameters subsystems that are discussed in depth. This complexity, combined with the large number of subsystem parameters, makes electric vehicle design a formidable engineering problem. Model-based design has several advantages as a practical approach to solving problems, including the use of a single environment for managing multi domain complexity, iterative modelling, and design elaboration. Two simulation comparisons were conducted on the electric vehicle MATLAB Simulink model. In a nutshell optimal control and linear quadratic regulator manages to minimise electric vehicle energy consumption and solves maximum average speed of an electric car issue with limited control input. The results are that electric vehicle energy efficiency is improved up to 16.67% and electric car with optimal control on average travels up to 14.67 kph or 0.175 m/s faster during the simulation.