Abstract
Permanent magnet brushless motor drives (BLMD) are extensively used in electric vehicle (EV) propulsion systems because of their high power and torque to weight ratio, virtually maintenance free operation with precision control of torque, speed and position. An accurate dynamical parameter identification strategy is an essential feature in the adaptive control of such BLMD-EV systems where sensorless current feedback is employed for reliable torque control, with multi-modal penalty cost surfaces, in EV high performance tracking and target ranging. Application of the classical Powell Conjugate Direction optimization method is first discussed and its inaccuracy in dynamical parameter identification is illustrated for multimodal cost surfaces. This is used for comparison with the more accurate Fast Simulated Annealing/Diffusion (FSD) method, presented here, in terms of the returned parameter estimates. Details of the FSD development and application to the BLMD parameter estimation problem based on the minimum quantized parameter step sizes from noise considerations are provided. The accuracy of global parameter convergence estimates returned, cost function evaluation and the algorithm run time are presented. Validation of the FSD identification strategy is provided by excellent correlation of BLMD model simulation trace coherence with experimental test data at the optimal estimates and from cost surface simulation.
Reference62 articles.
1. EL_Sharkawi M A. Advanced control techniques for High Performance Electric Drives. In: Control and Dynamic Systems. Academic Press; 1991. Vol 44; p. 59-130.
2. Friedrich G, Kant M. Choice of drives for electric vehicles: a comparison between two permanent magnet AC machines. IEE Proc.-Elec. Power Appl. May 1998; Vol. 145; No. 3.
3. Demerdash N A, Nehl T W, Maslowski E. Dynamic modelling of brushless dc motors in electric propulsion and electromechanical actuation by digital techniques. In: IEEE/IAS Conf. Rec. CH1575-0/80/0000-0570; 1980. pp. 570-579.
4. Dote Y. Servo Motor and Motion Control using Digital Signal Processors. Prentice Hall International;1990.
5. Kuo B C, Tal J. Incremental Motion Control Systems: DC Motors and Control Systems. Ilinois: SRL Publ. Co.; 1978.