Accurate Torque Estimation for Induction Motors by Utilizing Globally Optimized Flux Observers

Abstract

High precise torque estimation and torque control for induction motor drives is still an important research field due to the extended use of induction motors in torque controlled applications, for example in electric vehicles. Since the performance of torque estimation highly depends on the accuracy of the observed magnetic flux, a Kalman filter for rotor flux observation in combination with a global parameter identification is presented in this paper. Thereby, the basic induction motor model is extended by magnetic saturation of the main inductance, by an iron loss resistance, and by skin effect influences within the rotor and stator resistance. For estimating the set voltages of the inverter, an accurate gray-box inverter model is utilized. The parameters of the motor model are globally identified by a particle swarm optimization (PSO). For training and validation a comprehensive data set from test bench measurements is utilized. With this approach, in the complete operation range, a root mean squared torque estimation error of only approx. 0.8 % with respect to the nominal torque can be achieved. For comparison, a simple open- loop current model observer is additionally presented, which takes into account magnetic saturation and skin effect influences, and whose parameters are identified by PSO. This observer leads to a normalized root mean squared torque error of 2.9 %.