Momentum - April 2019 - 11

STUDENT
GENERATION
FSAE Lincoln 2018 Endurance Track Map

Varying Shift RPM

900

20

800

15

Score difference from default

Y coordinate, feet

700
600
500
400
300
200
100
0

10
5
0
-5
-10
-15
-20
-25

-100
-100

100

300

500

700

-1.0

900

0.0

X coordinate, feet

Figure 1. The 2018 Lincoln Formula SAE course GPS trace that was
used for all the simulation testing.
power output and about another half point from an
increase in fuel efficiency. The scores are presented in
Figure 2 to show the change in score relative to each
other and lap time.
The optimized power output with gear shift RPM was
used while optimizing other parameters. For the other
full lap parameter sweeps, varying the maximum throttle
position and a leaner fuel mixture map allowed for an
increased overall competition score. The maximum
throttle position and lean map raised the score another
2 points separately; unfortunately the score increase did
not combine well. With the full lap simulation, the total
increase in score was about 5 points. The lift and coast
method was not able to save enough fuel to overcome
the lap performance deficit; the score did not increase.
Next, the simulation was expanded by separating the
track into sectors and allowing the parameters to vary
from sector to sector. This approach resulted in slightly
different trends but continued to improve the score
overall. With this approach, optimizing the gear shift
RPM for each sector independently increased the score
by 8.5 points, or more than twice as much improvement
as the full lap simulation. In contrast, varying the
maximum throttle position by sector only improved the
score by 0.1 points more than the full lap simulation. The
lift and coast method was able to increase the score by
1.3 points over the full lap simulation, and the lean maps
version was not able to increase the score over the full
lap simulation.
In the end, the gear shift RPM and lean mixture
parameters were optimized together, attempting millions of combinations in a sector-by-sector simulation.
This improved the score by 10 points compared to the
baseline lap. The score improvements by sector are
shown in Figure 3.
In future research, more accurate lean mixture engine
output data will be gathered to more accurately model
the options for reduced fuel consumption. Hopefully, this

MOMENTUM

2.0

3.0

Lap time difference from default, seconds
Sector points

Overall score

Endurance score

Efficiency score

Figure 2. The simulation results from varying gear shift RPM over a full lap simulation.

Overall score varying gears and lean maps by sector
8
Score difference from default

FSAEL Track Map

1.0

6
4
2
0
-2
-4
-0.5

0

0.5

1

1.5

2

Lap time difference from default
Sector 1

Sector 2

Sector 3

Sector 4

Sector 5

Sector 6

Figure 3. The score increases by sector from varying gear shift RPM and lean maps.
will lead to even greater potential score improvement. New simulation
techniques are currently being developed to improve the range and
combination of parameters that are feasible to be optimized at one
time. Eventually, these parameters might be able to be simulated
onboard the vehicle in real time and thus enable it to adapt to track
changes or driver differences during a competition run.
With this new information, the team will be able to consider design
changes to the car that would allow for the car to save fuel and increase
our overall competition score by improving our Fuel Efficiency Event
score more than the loss in the Endurance Event score. Implementation
is definitely challenging, but has the potential to improve both dynamic
scores and design score.
Cole Frederick, a computer Science P.h.D. student at the University
of Alabama, wrote this article for MOMENTUM. It is based on SAE
International technical paper 2019-01-1127. Cole was captain of the
University of Alabama Formula SAE team from 2013 to 2017.

April 2019 11



Momentum - April 2019

Table of Contents for the Digital Edition of Momentum - April 2019

Momentum - April 2019
Contents
A better MOMENTUM
Briefs
Reliability overhaul: a lesson in resilience
Turbulence on the track
Full circle
Lap simulation tool shows the way
Baja SAE technical inspection
VW’s MEB platform: a modularity enabler
The F-22 Raptor gets its first metallic 3D-printed part
SAE 101: WCX 2019
Moon shot as metaphor for autonomous vehicle technology
DOSSIER: Greg Sawvelle
Momentum - April 2019 - Momentum - April 2019
Momentum - April 2019 - Cover2
Momentum - April 2019 - Contents
Momentum - April 2019 - A better MOMENTUM
Momentum - April 2019 - Briefs
Momentum - April 2019 - Reliability overhaul: a lesson in resilience
Momentum - April 2019 - 5
Momentum - April 2019 - Turbulence on the track
Momentum - April 2019 - 7
Momentum - April 2019 - Full circle
Momentum - April 2019 - 9
Momentum - April 2019 - Lap simulation tool shows the way
Momentum - April 2019 - 11
Momentum - April 2019 - Baja SAE technical inspection
Momentum - April 2019 - VW’s MEB platform: a modularity enabler
Momentum - April 2019 - The F-22 Raptor gets its first metallic 3D-printed part
Momentum - April 2019 - SAE 101: WCX 2019
Momentum - April 2019 - Moon shot as metaphor for autonomous vehicle technology
Momentum - April 2019 - 17
Momentum - April 2019 - DOSSIER: Greg Sawvelle
Momentum - April 2019 - 19
Momentum - April 2019 - 20
Momentum - April 2019 - Cover3
Momentum - April 2019 - Cover4
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