Particle Swarm Optimization (PSO) Based PID Speed Control of BLDC Motor Drive with Regenerative Breaking for Electric Vehicle Application

Abstract

The combustion of fossil fuels, a non-renewable and finite resource, has exacerbated air pollution, ozone damage, acid rain, and global warming. Fast depletion of energy resources reminds the importance of energy conservation. Combustion engine vehicles are responsible for more than 90% of the world global warming rate. A motor delivers only around 30 to 35% of the input energy. Electric vehicles are alternative solutions in order to reduce environmental air pollution due to fossil fuel driven vehicles. However, energy utilization and efficiency are the major issues in electric vehicles apart from the role of reducing air pollution. Energy has also wasted within the sort of heat whenever the brake is applied. But experience has indicated that the advantages don't seem to be always as great as may be expected due to inefficiencies within the power conversion processes. Extra mass and price can frequently restrict the financial feasibility of an electric vehicle with regenerative braking. Regenerative braking systems for electric vehicles enable the range of the vehicle be extended. In this thesis, the energy storage device, converters, battery, speed control of BLDC motor by multi-optimal approach of PSO, and regenerative brake are basically modelled and analysed mathematically using wheel dynamics equations. The simulation model was established under the circumstance of MATLAB/SIMULINK and script according to the speed control of BLDC motor by PSO-PID controller model, braking pedal intensity, and battery state of charge (SOC) for BEV are simulated and analysed. The simulation results have shown that, the recharging of battery or energy recovered during regenerative braking when the speed decrease, torque, and current are negative the distance range of BEV increase in one time charging under normal condition.