Griess Thesis Defense PP

BENCHMARKING, CHARACTERIZATION
AND TUNING OF SHELL ECOMARATHON
POWERTRAIN
Presented by Eric Griess
Masters of Science – Mechanical Engineering
California Polytechnic State University – San Luis Obispo

Outline
1. Project Summary
2. Test Setup
3. Calibration
4. Benchmark Testing
5. Simulation Development
6. Establishing AFR and Ignition Targets
7. Engine Tuning
8. Conclusions
9. Recommendations
10. Vehicle Performance Study

Competition Outline PROJECT SUMMARY
The Shell EcoMarathon is an annual national competition in
which schools enter student-built and student-driven vehicles
into various classes to achieve the best fuel efficiency
possible.
Urban Concept Prototype

Competition Outline
Prototype class competition:
- Large circuit course on public roads
- Total distance of 6 miles
Only limits on operation:
- Average speed of 15 MPH
- Maximum time limit
PROJECT SUMMARY

Competition Outline
Even with minimal requirements, successful teams have
adapted a “burn and coast” method. This introduced unique
challenges such as:
- Engine temperature variation
- Clutch control for starts, restart
- Fuel for engine starting
PROJECT SUMMARY

Problem Definition
The team placed 6th in 2013 with 1300 MPG. However, some aspects
of vehicle design left room for improvement:
- Engine was not tuned
- Non-ideal gear ratio due to unknown engine performance
- Electronics issue limited engine speed
- Chain drive system introduced additional losses
- Lack of analysis tools to guide design direction
PROJECT SUMMARY

Project Scope
This study aims to:
1. Quantify engine performance
2. Develop vehicle/engine simulation tool
3. Use information from both to select tuning targets for air-
fuel-ratio (AFR) and ignition advance settings.
4. Perform engine tune
5. Quantify improvements over previous tune
PROJECT SUMMARY

Project Scope
Deliverables to the team:
- Fully tuned engine with engine parameter tables
- Vehicle simulation tool
- Suggestions for continuing project development
- Recommendations for simulation-based vehicle
design approach
PROJECT SUMMARY

Table, Dynamometer
Dynamometer Table
Eddy Current Dyno
Controller
Power Supply
Torque Conditioner
TEST SETUP

Setup Requirements
In order to begin testing, the following items were necessary:
- Mounting system for engine, dynamometer
- Drive system
- Electronics integration (ECU, Power supplies, signal
conditioners)
- Dedicated fuel system
- Temperature control system
- Safety cage
- User interface
TEST SETUP

Engine, Dyno Mounting TEST SETUP

Engine, Dyno Mounting TEST SETUP
One-piece aluminum
engine mount was used
during competition.
For extended testing,
exhaust portion was
replaced for steel runner
with same diameter and
length.

Drive System TEST SETUP
Direct drive system
chosen after many
iterations
(discussed later)
Elastomer “spider”
damper used to allow for
small misalignment

Drive System TEST SETUP
• 2nd mount necessary
with new exhaust
• Placed as close to output
shaft as possible to
reduce local deflection

Drive System Alignment TEST SETUP
Parallel Alignment: .010” max
Axial Alignment: .010” max
Angular Alignment: 1º max

Electronics TEST SETUP
• Megasquirt II ECU
• Breakout Board
• Battery quick
disconnect for Cold
Cranking Amps
Requirement (40A)

Temperature Control TEST SETUP
Team normally runs engine without coolant
due to relatively short burn times.
In order to control engine temperature
during testing, factory water-cooling system
was used without a thermostat.
Then centrifugal blower with carefully
designed ducting system was used to control
mass air flow over radiator, manipulated
with control lever (welding rod)

User Control TEST SETUP
Emergency Kill
ECU Power Fuel Pump Ignition
Engine Start Throttle Control

Megasquirt Interface TEST SETUP

Dyno Control Interface TEST SETUP

Calibration Standards CALIBRATION
When possible, engine testing standard SAE J1349 was followed for
determining tolerances and test procedures. For data acquisition,
tolerances on sensors include:
Dynamometer Torque: ± 0.5%
Speed: ± 0.2%
Inlet Air Temperature: ± 1 ºC
Fuel Flow: ± 1%
Engine Temperature: ± 2 ºC

Dynamometer Calibration CALIBRATION

Sensor Calibration
Stein-Hart Equations for thermocouple behavior used for calibration
CALIBRATION
1
𝑇
= 𝐴 + 𝐵 ln 𝑅 + 𝐶 ln 𝑅 3

Fuel Flow Calibration CALIBRATION

Fuel Flow Calibration CALIBRATION
Unfortunately, flow meter was not accurate at most of the flow rates
the engine was seeing. Instead, injector duty cycle was related to flow
rate through the previous calibration.
𝑚 𝑓𝑢𝑒𝑙 = 0.474 ∗ 𝐷𝑢𝑡𝑦 𝐶𝑦𝑐𝑙𝑒 − 0.1114
Duty cycle output was captured for each steady state test and used for
fuel flow calculations.

Repeatability
In order to ensure repeatable results, repeatability testing was
conducted through 10 consecutive, identical tests.
𝑅 =
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑣𝑎𝑙𝑢𝑒 𝑜𝑣𝑒𝑟 1 𝑡𝑒𝑠𝑡
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑣𝑎𝑙𝑢𝑒 𝑜𝑣𝑒𝑟 10 𝑡𝑒𝑠𝑡𝑠
∗ 100
The importance of this is to assure valid results throughout a single
test, instead of repeating each test multiple times.
CALIBRATION

Repeatability Summary
Drivetrain iteration was necessary to meet acceptable repeatability
results ( < 1%)
CALIBRATION

Drivetrain Iterations
Iteration 1: Chain drive, no tensioner, no temperature control
CALIBRATION
- Chain vibration too violent for safe use

Iteration 2: Chain drive, torsion spring tension, no temperature control
CALIBRATION
- Chain ‘tight’ side still vibrating too much, repeatability unacceptable

Iteration 2: Chain drive, torsion spring tension, no temperature control
CALIBRATION
During tests, engine temperature was
varying from 120-190 ºF, even with
water cooling system.
Torque, power values were drifting
over time

Iteration 3: Chain drive, torsion tension, temperature control
CALIBRATION
Engine temperature was steady, but
torque was still varying excessively.
Next option was to address chain
vibration issue

Iteration 4: Chain drive, double idler tensioner, temperature control
CALIBRATION
- Still saw excessive vibration since lateral movement was allowed

Iteration 5: Chain drive, double idle tensioner, temperature control
CALIBRATION
• Chain overheated and caused
high stress condition.
• 4th crankshaft failure under chain
loading conditions
• Repeatability results were
unacceptable

Iteration 6: Direct Drive
Softer elastomers could not
withstand heating by friction.
Stiffer couplers used with
lubrication to alleviate issues.
CALIBRATION

Drivetrain Comparison
Figure shows engine speed comparison
over 60 seconds of testing.
Direct drive had ±20 RPM (±0.5%),
while chain drive saw ±60 RPM.
Best torque output repeatability with
chain was 3%, while direct drive was
0.2%.
CALIBRATION

Variable Definitions
BSFC = Brake Specific Fuel Consumption
=
𝑀𝑎𝑠𝑠 𝐹𝑙𝑜𝑤 𝑅𝑎𝑡𝑒 𝑜𝑓 𝐹𝑢𝑒𝑙
𝑃𝑜𝑤𝑒𝑟 𝑂𝑢𝑡𝑝𝑢𝑡 𝑜𝑓 𝐸𝑛𝑔𝑖𝑛𝑒
AFR = Air-fuel-ratio =
𝑀𝑎𝑠𝑠 𝑜𝑓 𝑎𝑖𝑟
𝑀𝑎𝑠𝑠 𝑜𝑓 𝑓𝑢𝑒𝑙
(λ) = Lambda =
𝐴𝐹𝑅 𝐴𝑐𝑡𝑢𝑎𝑙
𝐴𝐹𝑅 𝑠𝑡𝑜𝑖𝑐ℎ𝑖𝑜𝑚𝑒𝑡𝑟𝑖𝑐
VE = Volumetric Efficiency =
𝑚 𝑎
𝜌 𝑎,𝑟𝑒𝑓∗𝑉 𝑑
MBT = Maximum Brake Torque
Φ = Spark Timing (relative to MBT advance)
BTDC = Before Top Dead Center
BENCHMARK TESTING

Baseline Testing Goals
1. Acquire performance data for engine used in
competition
2. Quantify relationships between fuel delivery, spark
timing, and temperature with torque output and
BSFC
3. Use these trends to identify minimum and maximum
points for torque output and BSFC.
BENCHMARK TESTING

Baseline Performance
Test procedure for baseline testing:
• Each test conducted at maximum 500 RPM intervals
• Data only recorded during steady state (T = 167 ± 3 ºF)
• Sampling at 1 Hz for one minute
• Engine returned to idle, then next speed tested immediately
BENCHMARK TESTING

Baseline Performance
Max Torque: 2.71 ftlb @ 4800 RPM
Max Power: 2.48 HP @ 4800 RPM
Minimum BSFC: .473 lb/hphr @ 3700RPM
BSFC is abnormally high for small spark
ignition gasoline engine. (0.35 - 0.45 exp.)
Torque output changes by 8% across entire
speed range
Power continues to increase before cutoff
BENCHMARK TESTING

Baseline Performance BENCHMARK TESTING
• At this point, cannot tell how far
ignition values are off (MBT not
found at each point)
• Engine was running up to 14%
rich at points, and an average of
8% rich across operating range.
• In order to find ideal operating
points for engine, variation
studies were performed.

Lambda Variation BENCHMARK TESTING
Testing Method:
• Engine Speed: 4000 RPM
• Engine Temperature: 167 ± 3 ºF
• Ignition: MBT Advance (ϕ = 0º)
By changing fuel delivery parameter in Engine Control Unit
(ECU), Lambda was varied from λ = 0.8 (20% rich) to λ =
1.2 (20% lean). Each test was taken for 1 minute at 1 Hz.

Lambda Variation BENCHMARK TESTING
Maximum torque:  = 0.95
Minimum BSFC:  = 1.10
Minimum Torque:  = 1.2 (12% loss)
Maximum BSFC:  = 0.8 (32% loss)
This trend is widely accepted. The dotted
lines show the trend observed in
Heywood.
Compression ratio, engine size, and
flow/combustion characteristics can shift
these curves

Ignition Variation BENCHMARK TESTING
Testing Method:
• Engine Temperature: 167 ± 3 ºF
• Lambda:  = 1.0 (Stoichiometric)
Ignition advance was changed from 18º - 26º BTDC. Since
22º was MBT timing, then these values mean  was varied
from -4º to 4º.

Ignition Variation BENCHMARK TESTING
Maximum Torque:  = 0º
Minimum BSFC:  = 0º
Minimum Torque:  = ±4º (3.8% loss)
Maximum BSFC:  = ±4º (2.9% loss)
• This was also an expected trend, as
seen from Heywood prediction
lines.
• Dip is thought to be caused by a
hardware issue during testing.

Temperature Variation BENCHMARK TESTING
Testing Method:
• Ignition: MBT Advance (ϕ = 0º)
• Lambda:  = 1.0 (Stoichiometric)
Temperature was varied from 130 – 210 ºF in 20 ± 2 ºF
increments. Tests were performed separately at steady state
for one minute.

Maximum Torque: T = 120 ºF
Minimum BSFC:  = 170 ºF
Minimum Torque:  = 210 ºF (5.4% loss)
Maximum BSFC:  = 210 ºF (1.2% loss)
BSFC =
𝑚 𝑓
𝑃
=
1
𝜔 𝑒
𝑚 𝑓
𝑇
Hypothesis: Engine torque decreases, but
slower rate due to closing tolerances (up to
170 ºF), then drops faster past that point due to
increasing friction losses

Temperature variation study revealed several important conclusions:
- Higher temperature is not always better
- Actual BSFC losses relatively small
- In order to minimize these losses, fueling needs to adapt to
temperature changes. If mass flow rate of fuel is constant, then
running  = 1.0 at stoichiometric at 120 ºF will result in running
nearly 10% rich at 210 ºF, causing a 15% increase in BSFC.

Idle vs. Cut Test BENCHMARK TESTING
In order to verify the burn/coast method as desirable to begin with, the
fuel flow during engine starting and idle were studied.
Crossover time: 0.28 seconds
Meaning if car is coasting for
more than 0.28 seconds, cutoff
is more efficient than idling.

Benchmark Testing Conclusions BENCHMARK TESTING
• BSFC is vastly more sensitive to AFR than spark timing
and engine temperature.
• Temperature control is only important due to varying
volumetric efficiency causing changes to AFR.
• Burn and coast method verified
Variable Maximum Observed
BSFC Loss
AFR/Lambda () 32 %
Spark Timing 2.9 %
Temperature 1.2 %

Simulation Goals SIMULATION DEVELOPMENT
1. Introduce relationships only seen during transient engine
operation to replicate competition conditions
2. Vary both AFR and ignition under competition conditions
to define theoretical targets
3. Compare these targets to steady state targets (is
minimizing BSFC best for fuel economy?)
4. Suggest simulation-based design techniques

Disclaimer SIMULATION DEVELOPMENT
Simulation is NOT verified. Although the vehicle model was carefully
developed and tested, the team needs to perform their own verification
of this model to improve confidence. Some large assumptions in this
model:
- Straight line acceleration/deceleration
- Clutch engagement speed is constant
- Constant average rolling resistance
- Constant environment for both vehicle and engine

Model Overview SIMULATION DEVELOPMENT
1. Engine / clutch speed control
2. Calculates output torque based on speed, where lambda, ignition, and
temperature effects are integrated
3. Tractive forces calculated after drag, rolling resistance, and inertial
losses calculated.
4. Vehicle acceleration, velocity, distance calculated then velocity used to
calculate closed loop engine speed

Model Overview SIMULATION DEVELOPMENT
Simulation features:
- Constant modification of torque and BSFC with lambda, ignition
and temperature tracking.
- Centrifugal clutch engagement model
- Accounts for environmental effects (engine bay temp, local altitude)
- Energy path analysis
- 2D , 3D trend studies between variables

Goals AFR, IGNITION TARGETS
• Use simulation to establish theoretical AFR,
ignition targets
• Investigate differences, if any
• Select target values

Acquisition Method AFR, IGNITION TARGETS
For 3D trend studies, the simulation was run through
an iteration matrix of lambda and ignition values.
• Lambda from 0.8 – 1.2 (.05 step)
• Ignition from  = -4º to + 4º (1º step)
• 9 x 9 combinations

Results AFR, IGNITION TARGETS
Component Simulation Steady State
Lambda () 1.05 1.1
Ignition Advance () 0 (MBT) 0 (MBT)
Suggested Improvement 19.5%
Despite having steady state
performance data in simulation,
vehicle analysis suggests running
an AFR 5% richer than minimum
BSFC.

Hypothesis AFR, IGNITION TARGETS
• Suggested  = 1.05 is between minimum BSFC
(λ = 1.1) and maximum torque (λ = 0.95),
• Hypothesized that the best fuel economy comes from
a balance between the two
To investigate this, the lambda variation trend
was revisited.

Here, BSFC and torque trends
are normalized to % loss from
ideal value (maximum torque /
minimum BSFC)
Solid line is both of the losses
added together. The smallest
represents the point where total
loss is smallest. This coincides
with  = 1.05.

Identical plot, only with
total vehicle simulation
results normalized and
superimposed.
This shows that fuel
economy trend follows
‘total loss’ curve, instead
of BSFC or torque.

Target Selection AFR, IGNITION TARGETS
1. The lambda variation trend observed at 4000 RPM is expected vary
slightly at different speeds. Aiming for  = 1.05 should represent a
good “average” value that sacrifices only 1.4% BSFC at 4000 RPM.
2. Hypothesis from simulation data, supported by experimental trends
Component Selected Target
Lambda () 1.05
Ignition Advance () 0 (MBT)

Outline
1. Project Summary
2. Test Setup
3. Calibration
7. Engine Tuning
8. Conclusions
9. Recommendations

Tuning Method ENGINE TUNING
Test Procedure:
1. Reach steady state at each operating point
2. Change ECU fuel table to result in  = 1.05
3. Change ignition values for  = 0º (MBT)
4. Verify both targets, acquire steady state
5. Record data for 1 minute, 1 Hz
6. Repeat for each point

BSFC Improvement ENGINE TUNING

Projected Competition Improvement ENGINE TUNING
New engine tune replaced
baseline tune in engine, with all
other variables unchanged.
Estimated 17.5% improvement,
which is similar to the 18.8 %
average BSFC improvement
from engine tune.

Summary CONCLUSIONS
Experimental Conclusions:
- BSFC is much more sensitive to AFR than spark timing or
engine temperature.
- MBT timing is always optimal for maximizing torque and
minimizing BSFC
- Engine temperature does not play a large role, as long as
fueling can adapt to changing volumetric efficiency.

Summary CONCLUSIONS
Simulation-based hypotheses:
- Vehicle fuel economy trends are governed more by the total
loss curve between torque and BSFC, instead of individual
curves.
- The lambda value that minimizes total loss should result in best
overall fuel economy.
- Average BSFC improvement across operating range in steady
state should be expected to result in similar overall fuel
economy improvement.

Engine Tuning RECOMMENDATIONS
Primary recommendations:
1. Consider using narrow-band oxygen sensor for more accuracy
2. Acquire lambda variation trends at all testing speeds
3. Develop accurate temperature compensation curves for fuel
4. Alleviate artificial engine speed limit caused by sensor

Drivetrain Efficiency RECOMMENDATIONS
Switching from chain
drive to direct drive
allowed direct comparison
of drive systems.
Average drivetrain
efficiency with centrifugal
clutch and single chain
was 85%.

Crankshaft Failure RECOMMENDATIONS
Believed that the transverse loading
condition from chain tension
caused the multiple crankshaft
failures.
Suggestions for prevention:
- Decrease cantilever loading
- Minimize potentially
unbalanced weight

Simulation Development RECOMMENDATIONS
In order to improve accuracy of the simulation, the following variables
should be studied further:
- Average rolling resistance
- Coefficient of drag
- Clutch control behavior
- Overall drivetrain efficiency
- Engine bay transient behavior
- Environmental conditions (average temperature, wind speed, etc)

Simulation Development RECOMMENDATIONS
Suggested verification methods:
1. Simulate fraction of drive cycles on engine or chassis dynamometer, and record
all relevant data. Then change a variable such as AFR or gear ratio, then
compare recorded data with simulation data.
2. Perform the same in a very controlled environment, but necessary to take more
samples in order to address outlying data.
3. Implement an electronic drive motor with well-known characteristics to
measure vehicle losses

Vehicle Design RECOMMENDATIONS
Once simulation is further verified, then perform sensitivity analysis on
variables of interest.
𝑆 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 =
% 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑂𝑢𝑡𝑝𝑢𝑡 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒
% 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝐼𝑛𝑝𝑢𝑡 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒
If listed in descending order, it may provide design direction for the
team in order to maximize results.
For example, the gear ratio trend study saw a 0.83 % change in MPG
for every % change in gear ratio, while vehicle mass only saw 0.14%.

Outline
1. Project Summary
2. Test Setup
3. Calibration
7. Engine Tuning
8. Conclusions
9. Recommendations
10.Vehicle Performance Study

Goals VEHICLE PERFORMANCE STUDY
Quick additional exercise, changed some variables that
team has control over:
1. Gear Ratio
2. Clutch Pad Mass
3. Drivetrain Efficiency

Improvement Summary VEHICLE PERFORMANCE STUDY
NOTE: These are only projected improvements from simulation

Acknowledgements
Special thanks to:
Patrick Lemieux
Jim Gerhardt
Dorian Capps
Sean Michel

QUESTIONS?
CONCERNS?
INQUIRIES?

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