Scalability as a degree of freedom in electric drive train simulation

Abstract

Automotive companies are developing electric vehicles in different ways. There are several system topologies and component configurations used for prototype and mass production. To save time and money during the design process empirical values are necessary. In this paper, scalability is used as a powerful tool of electric drive train simulation. It enables the size variegation of drive train components like the electrical machine, the frequency inverter, and the energy storage. In addition, a novel scheme is derived for the control of rotating field machines in vehicular applications. The control strategy as well as the component configuration is optimized by a wellchosen method using scalability as degree of freedom. Closing, the several influences of component size variation and control parameterization on the drive train behavior is discussed.

Optimal sizing of an electrical machine using a magnetic circuit model: application to a hybrid electrical vehicle

“…There is no direct link between the nominal power P b , k and the geometrical parameters. Nevertheless, in order to compute the new geometrical characteristics of the scaled machine, it is usually assumed that the power density is constant and lengths proportional to k 1/3 [5, 18]. Mass moments of inertia have units of dimension M · L 2 , this leads to a moment of inertia proportional to k 5/3 .…”

An Optimization Methodology to Pre Design an Electric Vehicle Powertrain

“…Researches on new concepts of electric machine [1][2], specific transmissions [3], or storage technologies [4] can hardly take into account the system view. Moreover, system studies are performed by comparing some selected designs [5] or simplifying the problem with one big design factor [6], resulting in some limitations of the design space that could be explored. When the designs are obtained with an optimization process, the design variables are usually limited to the electrical machine even though a driving cycle is simulated [7] [8].…”