无刷直流电机结构设计的基本知识

A Knowledge Based Decision Support Architecture for Designing Brushless DC

Motors

Vahab Akbarzadeh Ryerson University 350 Victoria Street Toronto, Ontario M5B 2K3 vahab.akbarzadeh@ryerson.ca

Alireza Sadeghian Ryerson University 350 Victoria Street Toronto, Ontario M5B 2K3 asadeghi@ryerson.ca

Abstract

This paper presents a know l

edge based decision support system that can be used to design brushless DC motors. A hybrid approach, that inc l udes an object oriented paradigm using frames and procedura l

attachments together with a rul e based mechanism, is used to bui d the proposed architecture. The design

strategy is impl emented using a rul e-based successive

iterative method, through which the expert designer approach is emul ated and embedded in a knowl edge-based system. The performance of the proposed system is compared with results from the literature.

1. Introduction

Application of brushless DC (BLDC) motors has increased significantly over the past decades. This is mainly due to high reliability and efficiency of BLDC motors as well as their ability to reach very high speed. Brushless DC motors are rotational brushless permanent magnet motors which are driven by DC current and use electronic control systems instead of the brushes that are usually used in conventional DC motors. Compared to conventional commutator type DC motors, BLDC motors are more efficient, need less maintenance and have longer life span. On the other hand, the control system of BLDC motors needs a rotor positioning mechanism, and the magnets might gradually demagnetize [1], [8]. BLDC motors have been used in a wide variety of applications from industrial to household devices. Typical examples include industrial tools (pumps, compressors), power tools (drills, hammers), transportation (electric vehicles), and household devices (electric shavers, mixers) [4]. Small BLDC motors have also been extensively used in precision devices including medical equipment, computer drives, hard disks, and players.

The conventional design process for BLDC motors consists of selection of the appropriate magnetic material and specification of the geometrical properties of the motor. First, based on the design specifications, the

expert designer selects the set of materials to be used for motor construction, including material for the permanent magnet. Properties of the selected materials are then plugged into a set of equations which calculate the geometrical properties of the motor. Characteristics of the proposed design are then measured in terms of indicators such as efficiency, motor constant, weight, and cost. Magnetic modeling in the conventional method is usually simplified to use a magnetic circuit instead of finite element analysis. This simplification reduces the computation complexity of the design process [6]. Table 1 shows the types of knowledge involved in motor design, independent of the design process and the assumptions [7]:

Tab l e 1. Types of know l edge to be incorporated in the know l

edge base environment DATA TYPE DESCRIPTION

Structural Physical data including material, core, wire gauge, etc.

Graphical Charts and graphs, e.g., core loss rates of various magnetic materials versus frequency and flux density.

Heuristic The empirical knowledge rules used by the experts as a design aid.

Procedural The motor design process and the modification steps.

Criteria

The final performance requirements of the system.

Analytical The design and performance equations.

This paper presents a knowledge based architecture that provides an attractive setting for the BLDC motor design problem by providing a suitable framework whereby analytical data as well as empirical and heuristic data can be readily incorporated and assessed. By presenting a number of alternate designs that have satisfied the design specifications and vary in one or more key characteristics, the proposed system acts as a knowledge driven decision support system capable of providing assistance to expert designers. A contrasting

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