Optimal Sizing of Traction Motors Using Scalable Electric Machine Model

This paper presents a novel method for optimal sizing of traction motors based on vehicle level targets. The method employs a scalable electric machine model inside the multiobjective optimization (MOO) algorithm. A reference motor geometry is scaled in axial and radial directions in the optimizatio...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on transportation electrification Vol. 4; no. 1; pp. 314 - 321
Main Authors: Ramakrishnan, Kesavan, Stipetic, Stjepan, Gobbi, Massimiliano, Mastinu, Gianpiero
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.03.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computational modeling
Design optimization
Domains
Electric machines
Energy conservation
Energy consumption
Light duty vehicles
Linear programming
Motors
Multiobjective optimization (MOO)
Multiple objective analysis
Optimization
Pareto optimum
Pareto-optimal set
Permanent magnet motors
Product design
scaling laws
Sizing
Torque
Traction
Traction motors
ISSN:2332-7782, 2577-4212, 2332-7782
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a novel method for optimal sizing of traction motors based on vehicle level targets. The method employs a scalable electric machine model inside the multiobjective optimization (MOO) algorithm. A reference motor geometry is scaled in axial and radial directions in the optimization routines according to the values of the design variables. The optimization candidates are the scaled versions of the reference motor and their performance is evaluated using the computationally inexpensive scalable electric machine model. The approach is demonstrated on a vehicle comprising in-wheel motors. The design variables are the rated torque and the base speed of the motor while the considered vehicle level design objectives are the maximization of driving performance and the minimization of energy consumption and unsprung mass. MOO approach considers the objective functions simultaneously and derives the Pareto-optimal solutions in both the objective functions and the design variables domains. The analysis is repeated with New European Driving Cycle and the Worldwide Harmonized Light Vehicles Test Procedure to present the influence of driving pattern on the optimization results.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2332-7782
2577-4212
2332-7782
DOI:10.1109/TTE.2017.2750488