Research On Vector Control System Of Induction Motor For Electric Vehicles

Owing to its advantage in energy savings and environmental protection, as a new genre of automobiles, the Electric Vehicle (EV) has been undergoing rapid development in recent years. As essential components of EV, the performance of the motor and its drive system directly affects the overall performance of the EV. The induction motor has the advantages of durability, reduced size, requiring less maintenance, low cost and its ability in working in explosive and flammable conditions. As the performance of vector control continually improves, the induction motor has become the main drive motor for the EV. Vector control system of induction motor for the EV is different from other industrial systems, in its emphasis and focus on efficiency maximization, wide speed variability, increased reliability and good parameter robustness. These problems are discussed herein and some results are achieved in this paper.Loss model of induction motor under efficiency maximization control is studied. Through analyzing the relationship between the stator/rotor iron loss and slip/synchronization angle frequency, we build a loss model of the induction motor under efficiency maximization control that takes the stator leak inductance, the rotor leak inductance, the stator iron loss and the rotor iron loss into account. Based on the proposed loss model, relationship among the rotor flux, load and the rotor speed under efficiency maximization control is derived using the Lagrange theorem. This relationship henceforth leads to the relationship between the rotor relative iron loss and synchronization angle frequency. So it is shown theoretically that the effect of rotor iron loss on the efficiency of induction motor is distinct at low speed. The experimental results show that the proposed loss model is accurate over the full speed range.A new efficiency controller based on fuzzy logic is designed to improve the robustness of the parameters of the induction motor under efficiency maximization control, and the stability, rapidity and accuracy of the process of efficiency optimization. By assigning the searching initial value of the new efficiency controller, determined by the proposed loss model of the induction motor; and its searching step, adjusted automatically according to fuzzy rule, the DC input power of inverter is minimized by changing the rotor flux, so the efficiency maximization is achieved ultimately. The problems associated with low frequency pulsating torque and torque fast response are addressed by using a feedforward compensation algorithm and a first order differentiator. The experimental results show that the proposed efficiency fuzzy controller is effective and is meaningful to improving the EV running distance after a battery charge.The difficulty to estimate the rotor flux orientation angle and the rotor speed at low speed under speed sensorless vector control, and poor speed control, lead to failure to meet the demand for widely variable speed of EV, especially while running at low speed range. Firstly, a novel rotor flux orientation angle estimation hybrid model integrating high frequency signal injection method (HFSIM) and the modified voltage model (MVM) is designed, so it addresses the problem of the rotor flux orientation angle estimation over a full speed range. The high frequency signal amplitude decreases with an increase of speed, so the smooth transition between the different estimation methods from low to high speed range is realized. Furthermore, by proposing the hybrid model serving as the reference model, and a conventional current model serving as the adjustable model, a model reference adaptive system (MRAS) is established, which addresses the problem of the rotor speed estimation including low speed. The rotor resistance online identification scheme is proposed to update the rotor resistance contained in the adjustable model and to ensure the speed estimation accuracy. The experimental results show that the proposed rotor flux orientation angle and rotor speed estimation methods are effective from low to high speed range.The mutual inductance of induction motor is variable under efficiency maximization control, a new method for online identification of the mutual inductance based on MRAS is proposed. A special function, whose variables are the stator current and the stator voltage integral, serves as the reference model and adjustable model of MRAS separately in different reference frames. Also, the equation group, containing steady-state electric torque equation, rotor voltage equation and rotor flux equation, is established and solved to achieve the online identification of the inductance motor mutual inductance. It is shown in theory that the proposed identification method is independent of the rotor flux orientation angle, the dead time of the inverter, and the motor parameters, including the stator and rotor resistance. Good robustness and high precision are achieved. The experimental results show that the proposed identification method is effective.