scholarly journals Extended Simulation of an Embedded Brushless Motor Drive (BLMD) System for Adjustable Speed Control Inclusive of a Novel Impedance Angle Compensation Technique for Improved Torque Control in Electric Vehicle Propulsion Systems

10.5772/29821 ◽  
2011 ◽  
Author(s):  
Richard A.
Keyword(s):  
Electric Vehicle ◽  
Speed Control ◽  
Torque Control ◽  
Motor Drive ◽  
Propulsion Systems ◽  
Brushless Motor ◽  
Vehicle Propulsion ◽  
Compensation Technique ◽  
Adjustable Speed

Wide‐range adjustable speed control method for dual‐motor drive systems

2015 ◽  
Vol 9 (2) ◽  
pp. 107-116 ◽  
Author(s):  
Shao‐Kai Tseng ◽  
Chih‐Chien Tseng ◽  
Tian‐Hua Liu ◽  
Jui‐Ling Chen
Keyword(s):  
Control Method ◽  
Speed Control ◽  
Motor Drive ◽  
Wide Range ◽  
Adjustable Speed ◽  
Drive Systems

scholarly journals Novel Application of Fast Simulated Annealing Method in Brushless Motor Drive (BLMD) Dynamical Parameter Identification for Electric Vehicle Propulsion

2021 ◽  
Author(s):  
Richard A. Guinee
Keyword(s):  
Simulated Annealing ◽  
Parameter Identification ◽  
Electric Vehicle ◽  
Weight Ratio ◽  
Torque Control ◽  
Function Evaluation ◽  
Dynamical Parameter ◽  
Penalty Cost ◽  
Brushless Motor ◽  
Identification Strategy

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.

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