Showing papers by "Juan Carlos Balda published in 2001"

Optimum speed ratio of induction motor drives for electrical vehicle propulsion

Abstract: When sizing and subsequently designing an electric motor drive for any battery-powered electric vehicle (EV), the main objective is to get a large charge range while achieving low drive weight, volume and cost. The selection of the speed ratio (i.e., motor maximum speed divided by its rated speed) has a great impact on the drive efficiency, weight, volume and cost. To this end, the paper presents a methodology to determine the optimum speed ratio of an induction motor drive for EV applications using the EV America Technical Specifications as the basis. Factors like the peak power rating, continuous power rating, peak current, weight, volume and average loss of motor drives are functions of the speed ratio. The paper shows the need to consider several performance and design factors besides the peak power rating, and includes a set of criteria for selecting the optimum speed ratio of induction motors used in EV applications. The optimum speed ratio was found to be 3.0, where the motor weight, volume, cost, drive peak current and average losses (affecting the life of the battery pack) are low.

Artificial neural networks based models for the multiply excited switched reluctance motor

Abstract: The flux per phase of multiply excited switched reluctance motors (SRM) deviates from the flux per phase in the singly excited case. Likewise, the net torque deviates from the value obtained by superposition of the results from the singly excited case. This is due to mutual flux interactions between the excited phases and nonlinearities in the system. This paper describes a simplified approach, based on feedforward (FF) artificial neural networks (ANN), to account for these mutual interactions and for the estimation of torque when simultaneously exciting two phases. The proposed technique requires a small measured data set and involves simple calculations. This technique can be applied in simulations as well as in real-time implementations for online phase-flux and torque computations. A description is included of an implementation on the Texas Instruments (TI) TMS320C6701 DSP. The paper also describes a scheme for measurements and quantification of the mutual flux interaction between the phases.

Intelligent and adaptive on-line direct electromagnetic torque estimator for switched reluctance motors based on artificial neural networks

Abstract: Torque estimation is an important task in the implementation of controllers for electric motor drives. In the case of switched reluctance motors (SRM), the computation technique should account for the nonlinearity of the magnetic material and the variations of the flux-linkage characteristics with rotor position and current level. Also, it is essential to adapt to the characteristics of the individual SRM (due to manufacturing deviations) when requiring high accuracy in this task. This paper presents a technique based on artificial neural networks (ANN) that estimates the electromagnetic torque developed by learning the characteristics of the SRM drive system using online measurements. The technique is then illustrated by applying it in simulations for predicting the electromagnetic torque.

An enhanced simple method for designing switched reluctance motors under multi-phase excitation

Abstract: The paper presents a simple method for designing switched reluctance motors (SRM) under multi-phase excitation using specific design coefficients (derived from the flux and MMF distributions for each SRM configuration), and taking into account the magnetic loading. The torque and magnetic loading performances of various SRM configurations designed using the proposed method are verified through finite element analysis (FEA).