Mathematical Analysis for Response Surface Parameter Identification of Motor Dynamics in Electric Vehicle Propulsion Control

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

“…The extraction of the drive dynamical parameters generally relies on the minimization of some quantitative measure of error cost in terms of the goodness-of-fit, based on the MSE norm [13], between the observed motor drive output experimental test data and its model equivalent. The presence of multiminima in the MSE penalty function, however, results in a large spread of parameter estimates about the global minimum with model accuracy and subsequent controller design performance very dependent on the minimization technique adopted and the initial search point chosen.…”

“…The presence of multiminima in the MSE penalty function, however, results in a large spread of parameter estimates about the global minimum with model accuracy and subsequent controller design performance very dependent on the minimization technique adopted and the initial search point chosen. The existence of a noisy cost function, resulting in 'false' local minima proliferation in the stationary region containing the global extremum [13,28], depends on the numerical accuracy with which the PWM delayed inverter switching instants are resolved in the model simulation [13]. Furthermore the plurality of genuine local minima is governed by the choice of data training record used in the objective function formulation which in the case of step response feedback current (FC) has a sinc-like topography [13].…”

“…The existence of a noisy cost function, resulting in 'false' local minima proliferation in the stationary region containing the global extremum [13,28], depends on the numerical accuracy with which the PWM delayed inverter switching instants are resolved in the model simulation [13]. Furthermore the plurality of genuine local minima is governed by the choice of data training record used in the objective function formulation which in the case of step response feedback current (FC) has a sinc-like topography [13]. The use of a step input (i/p) as a test stimulus is motivated by the fact that in normal industrial applications in the online mode input command changes are generally sudden and step-like and are sufficient for persistent excitation of the BLMD system.…”

“…This book chapter, which is divided into three separate but interrelated sections, concerns BLMD dynamical parameter identification in which the choice of target data used in the Minimum Squared Error (MSE) objective function formulation has a significant effect on cost surface topology and selectivity in the vicinity of the global minimum [13,14]. The penalty cost function selection in the identification of the motor drive dynamics impacts directly on the type of parameter search strategy to be adopted where embedded cost surface multiminima are concerned, which is discussed below, in terms of the accuracy of the returned parameter estimates.…”

“…In the first section the classical application of the Powell Conjugate Direction (PCD) optimization technique of motor parameter extraction over a two dimensional shaft velocity cost surface, which has a parabolic wedge shaped topography with a 'line minmum' stationary region [13,14], is discussed. This will be used later for comparison purposes with the more successful Fast Simulated Diffusion (FSD) method, which is presented in the second section, to show that the FSD method is better in terms of the accuracy of returned FSD parameter estimates.…”