Practical Modeling and Simulation of Permanent Magnet Direct Current (PMDC) Motors

Electrical Computer Aided Engineering (CAE) is necessary and useful for the automotive industry [1,2]. It provides the user with necessary information that helps him/her make faster and more certain design decisions. CAE facilitates for the user options and the means to locate and choose optimums [6]. It requires models that best represent the actual system while avoiding unnecessary numerical overhead. Therefore, it is necessary to build the mathematical model in a systematic way that captures dynamics of the actual system [3]. Also, an optimal solution for the system parameters is required to increase the accuracy of the model and to make a correct decision while designing, testing, and validating [3,6]. This paper studies the CAE analysis of a PMDC motor. It develops a systematic approach to model, simulate and analyze PMDC motors with robust output. It enhances the accuracy of PMDC to a 6-Sigma level. A robust model minimizes the effort required in analyzing, predicting and validating the outcome of the motor. The uniqueness in this model is the use of Least Squares algorithm to solve for the optimal value of the motor parameters and the use for Recursive Least Squares (RLS) [7 (page 70)] for On-Line learning. The use of learning overcomes a wide band of ambiguity and non-linearity included in the actual motor. The developed model introduces a procedure for calculating each of the motor parameters (e.g. R_a, L_a, K_T, K_v, J, D, T_Loss). In this paper, simulation study uses a seat motor assembly that consists of three different motors as an example. The Saber simulator is used for running the simulation study. The actual data (experimental lab results) and the estimated data (Saber model) are then compared to assure the validity of the seat motor model compared to the physical seat motor.