Systems, Man & Cybernetics - April 2017 - 55

For the powertrain area of interest,
the most promising ideas found are
ways of storing more energy through
novel battery chemistries, such as
lithium-sulfur, and ways of generating more energy such as using regenerative braking aggressively and
almost exclusively.
For the baseline system, a configuration similar to the most popular
mainstream EV on the market was
chosen and modeled. The powertrain
chosen is composed of a permanent
magnet synchronous motor powered
by a Li-ion battery through an insulated-gate bipolar transistor (the most
conventional power switching technology on the market) power converter unit. The drivetrain uses a singlegear transmission to power the front
wheels. On the thermal management
side, the architecture uses liquid cooling for the powertrain, air cooling
for the battery, and a heat pump and
electrical heater for the cabin (HVAC).
The software chosen for modeling the
system in this case study is DYMOLA
(Modelica language) due to the array
of available libraries with parametrized models. The modeling effort
resulted in a new Modelica library
for electromechanical and thermal
simulation of EVs. The top-level models of the library, the Vehicle System
(mechanical/electrical aspects) and
the Thermal Management System,
were integrated externally into a cosimulation by using a MATLAB fixedpoint iteration scheme to reduce
simulation time. This solution was
automated through scripting and was
used to run a three-level full factorial
design of experiments.
Using the data obtained from the
simulations, a low-level performanceonly overview was first conducted
through sensitivity analysis. Relationships between the selected inputs
and outputs are shown in a table of
charts. This simultaneous visualization of all interactions aids in
searching for errors by observing
erroneous trends and helps reduce

What is an engineer?
What do engineers
really do? How do
they affect the world
as we know it? "Every
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the dimensionality of the problem by
identifying inputs with little impact
on results. For a higher-level overview, a technique for multicriteria
decision making, TOPSIS (Technique
for Order Preference by Similarity to
Ideal Solution), was used. This technique allows the user to select the
relative importance of objectives and
chooses the solution closest to the
ideal and farthest from the worst
possibility. The algorithm was coded
into an Excel-driven interface for effectiveness and portability.
About the Authors
George Bucsan (george.bucsan@
toyota.com) earned his M.S. degree in
aerospace engineering from Georgia
Tech. Upon graduation, he joined the
Toyota Research Institute of North
America as a research engineer to
continue his involvement in energy
management research and development projects.
Michael Balchanos (michael
.balchanos@asdl.gatech.edu) earned
his B.S. degree in physics from the
Aristotle University of Thessaloniki,
Greece, in 2002, and his M.Sc. and
Ph.D. degrees in aerospace engineering from Georgia Tech in 2005 and
2012, respectively. He is part of the
research faculty with the School of
Aerospace Engineering, where he
serves as the Resilient Systems
Branch lead at the Aerospace Systems
Design Laboratory at Georgia Tech.
Dimitri Mavris (dimitri.mavris@
aerospace.gatech.edu) earned his B.S.,

Ap ri l 2017

M.S., and Ph.D. degrees in aerospace
engineering from Georgia Tech in
1984, 1985, and 1988, respectively. He
is the Boeing chaired professor of
Advanced Aerospace Systems Analysis in Georgia Tech's School of Aerospace Engineering, regents professor,
and director of its ASDL. He is an S.P.
Langley National Institute of Aerospace distinguished professor, an
American Institute of Aeronautics and
Astronautics (AIAA) fellow, fellow of
the Royal Aeronautical Society, member of the International Council of
Aeronautical Sciences Executive
Committee, the AIAA Institute Development Committee, and the U.S. Air
Force Scientific Advisory Board. He is
also the director of the AIAA Technical, Aircraft, and Atmospheric Systems Group.
Jae Seung Lee (jae.lee@toyota
.com) earned his M.S. and Ph.D.
degrees in electrical and computer
engineering from the University of
California-Davis in 2004 and 2005,
respectively. He is currently a senior
manager at the Toyota Research Institute of North America's Toyota Technical Center in Ann Arbor, Michigan.
Masanori Ishigaki (masanori
.ishigaki@toyota.com) earned his B.S.
degree in electrical engineering from
Tokyo Metropolitan University in 2005
and his M.S. degree in electrical and
electronic engineering from the Tokyo
Institute of Technology in 2007. Upon
graduation, he joined Toyota Central
R&D Labs, Inc., Nagakute, Japan.
Since 2014, he has been working in
the Electronics Research Department
at the Toyota Research Institute of
North America.
Atsushi Iwai (atsushi.iwai@
toyota.com) earned his M.S. degree
in electrical and computer engineering from Nagoya University, Aichi,
Japan, in 2005. He is currently a
senior principal engineer at the Toyota Research Institute of North America, Toyota Technical Center, in Ann
Arbor, Michigan.

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