Swarm Intelligence Based Controller for Electric Machines and Hybrid Electric Vehicles Applications

Abstract

Swarm Intelligence in the form of Particle Swarm Optimization (PSO) has potential applications in electric drives. The excellent characteristics of PSO may be successfully used to optimize the performance of electric machines and electric drives in many aspects. In this chapter, a field-oriented controller that is based on Particle Swarm Optimization is presented. In this system, the speed control of two asymmetrical windings induction motor is achieved while maintaining maximum efficiency of the motor. PSO selects the optimal rotor flux level at any operating point. In addition, the electromagnetic torque is also improved while maintaining a fast dynamic response. A novel approach is used to evaluate the optimal rotor flux level by using Particle Swarm Optimization. PSO method is a member of the wide category of Swarm Intelligence methods (SI). There are two speed control strategies will demonstrate in next sections. These are field-oriented controller (FOC), and FOC based on PSO. The strategies are implemented mathematically and experimental. The simulation and experimental results have demonstrated that the FOC based on PSO method saves more energy than the conventional FOC method. In this chapter, another application of PSO for losses and operating cost minimization control is presented for the induction motor drives. Two strategies for induction motor speed control are proposed in this section. These strategies are maximum efficiency strategy (MES), based PSO, and minimum operating cost Strategy. The proposed technique is based on the principle that the flux level in a machine can be adjusted to give the minimum amount of losses and minimum operating cost for a given value of speed and load torque. In the demonstrated systems, the powertrain components sizing and the power control strategy are the only adjustable parameters to achieve optimal power sharing between sources and optimal design with minimum cost and maximum efficiency for Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs). Their selection greatly influences the performance of the drive system in Hybrid Electric Vehicles applications. In this section, the design and power management are investigated and optimized by the Particle Swarm Optimization. It is very important to note that, these applications were achieved without any additional hardware cost, because the PSO is a software scheme. Consequently, PSO has positive promises for a wide range of variable speed drive and hybrid electric vehicles applications.