Data Collection for Improved Cold Temperature Thermal Modeling and Strategy Development
2011 DOE Hydrogen and Vehicle TechnologiesAnnual Merit Review
May 10, 2011
Forrest Jehlik, Eric RaskArgonne National Laboratory
Sponsored by Lee Slezak
This presentation does not contain any proprietary, confidential, or otherwise restricted information
Project ID # VSS050
1Q 2010-current 50% complete
– Complete evaluation of Gen 2 Prius– Begun evaluation of 2010MY Gen 3
Prius– Leverages APRF benchmarking and
Environment Canada vehicle testing
Barriers addressed– Cold ambient testing shows
significant fuel consumption increase (w/o creature comforts)
– Engineered solutions could have potential drastic petroleum use reduction
Timeline
Budget
Barriers
Partners Environment Canada Total project funding
– FY10 funding: $200k
– FY11 funding: $300k
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Overview
Majority of fuel economy testing does not reflect real world ambient conditions
Four back-to-back UDDS cycles
Relevance: Cold ambient temp greatly reduces hybrid fuel economy
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30%60%
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60
70
80
0 200 400 600 800 1000 1200 1400
MPH
/Toi
l
Toil (Tamb = -5oC) Toil (Tamb = 35oC)
UDDS engine-off operation changes
Engine-off operation changes dramatically under cold ambient conditions and during vehicle warm-up
Engine efficiency differs throughout the entire warm-up period as a function of ambient temperature
Relevance: Cold operation impacts vehicle operation and efficiency
Steady state temperature reduced
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Relevance: Cold ambient operation still affects vehicle controls after initial warm-up
Usage Oscillation Due to Engine-off Cool-down to Ambient
Coolant Temperature Oscillation Due to Cool-down
Eng. Off disabled for “hot” run
Coolant temp. lower than other cycles
* Cool-down issue is even larger for PHEVs5
Simple methodology to estimate the impact of cold engine temperature on engine efficiency and estimate engine thermal state given usage and ambient temperature
– Current methodology uses coolant temperature, but can be generalized to other temperatures
– Methodology uses response surface and empirical data-fitting techniques
– Techniques result in simplified general models
Cold Ambient Testing
Fueling vs. Engine Temperature, Speed, Load
Coolant Temperature vs. Engine Usage, Ambient Temp.
Engine Usage and Starting Conditions
Estimated Temperature
Profile
Temperature based Engine Fueling and Fuel
Economy Estimate
Response Surface Methodology Model (RSM)
Approach: Combine RSM fueling map with temp prediction model
Lumped capacitance model
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Hymotion Prius was used for vehicle testing and methodology development
Vehicle testing focused on the UDDS and US06 cycles to obtain a cross section of usage
UDDS
020406080
100120
0 200 400 600 800 1000 1200 1400
Time (s)
Veh
icle
Spe
ed (k
m/h
)
US06
020406080
100120140
0 100 200 300 400 500 600
Time (s)
Veh
icle
Spe
ed (k
m/h
)
Signal Description NotesVehicle Speed Dynamometer/
CAN measuredDrive trace
measurementEngine RPM CAN/ spark
frequencyModel input
Brake Torque CAN Flywheel torque, model
inputT_oil Dipstick
thermocoupleModel input
T_coolant CAN Model inputFuel Flow Emissions
Bench carbon count
Model input
Approach: Experimental setup
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Project will develop a methodology for estimating the impact of engine thermal state on engine efficiency during cold ambient operation
– Quantifying/qualifying magnitude of losses highlight benefit for engineered solutions• Opportunity to greatly reduce all season, real world fuel consumption
Approach: Data from multiple cycles at varied temperatures
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Estimated fueling versus temperature and load works well (within 1%)
For more robust estimations, additional low temperature testing required
UDDS Estimated vs. Actual Fuel Used Fuel Rate During Warm-up
Accomplishments: Fueling rate sensitive to temperature
Estimated fueling map shows a strong sensitivity to coolant temperature
UDDS cycle coolant temperature UDDS engine speed/load points
25°C 50°C
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Using a single temperature does not provide adequate estimation during warm-up– Necessary to determine the number of points required to adequately reduce error
In this example, at least four temperatures are needed to evaluate the previous UDDS comparison points within 1%
Accomplishments: Temperature resolution sensitivity
UDDS Fuel Rate Error: Single Temp. vs. Time-series Total Fuel Comparison: Single Temp. vs. Time-series
While only 4 points are required, minimal points would not allow for control strategy optimization/ characterization of where losses are most prevalent
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Accomplishments: Estimating thermal state
Max Error ~6 deg.
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Developed technique works well for estimating UDDS coolant temperature
During US06 cycle operation, coolant temperature is over estimated due to significant thermostat operation
Modeled UDDS and US06 cycles when operating in cold ambient conditions
Cycle Percent Error of Actual versus Estimated Grams Fuel Used (%)
UDDS #1 1.3UDDS #2 0.7US06 #1 3.0US06 #2 4.4
Accomplishments: Model works well predicting UDDS/US06
Back-to-Back UDDS Runs
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-5oC
15oC
35oC
3
4
5
6
7
8
9
Fuel
con
sum
ptio
n (L
/100
km)
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Accomplishments: Early results clearly show large petroleum reduction potential*
-10
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5
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Am
bien
t tem
pera
ture
[oC]
Chicago Washington DC Los Angeles
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Gal
lons
con
sum
ed /
mon
th
Chicago Washington DC Los Angeles
*Results shown are trends from experimental, not modeling data
37%
25%Predominant EPA test temperature range
1. Testing at multiple temps… 2. Greater variation in real world…
3. Significantly higher fuel consumption
Real world
There are still many open issues to investigate
– Improve procedure/technique for model development
– Define potential for engineered solutions to reduce real world fuel consumption
– Incorporate creature comfort features into modeling effort (NREL)
– Incorporate catalyst light off features into modeling effort (ORNL)
– Further investigate thermal linkages between components
– Ensure robustness with additional vehicle testing leveraging APRF thermal capability upgrade and continuing Environment Canada collaboration
Conclusions/Proposed future work
Alternative Temperature Signals and Cool-down
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Automonie•Development of thermal capability in models
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J1711 HEV & PHEV test procedures• Early thermal worked
used in guidance
DOE technology evaluation• Future collaborative potential with ORNL/NREL
Environment Canada•Testing and tech support
Collaborations and coordination with other institutions
APRF• Warm testing data collected at APRF• APRF under construction to begin
cold testing
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Summary Current vehicle testing protocols under represent real world ambient conditions
Methodology developed to predict real season fuel consumption in HEV’s• Response surface fueling rate modeling technique finalized• Lumped capacitance temperature modeling technique developed
Modeling shown to be relatively accurate relative to experimental data• Model vs. experimental fuel consumption data within a few %
Results demonstrate significant engineering potential to reduce petroleum consumption
Further work needs to be complete to assess how much efficiency can be gained and the most effective pathways
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PublicationsI. “PHEV Energy Management Strategies at Cold Temperatures with Battery Temperature Rise and Engine Efficiency
Improvement Considerations”, Shidore, Neeraj, S., Jehlik, F., Rask, E., SAE World Congress, Detroit, SAE 2011-01-0872
II. “Development of Variable Temperature Brake Specific Fuel Consumption Engine Maps”, Jehlik, F., Rask, E., SAE Powertrain Fuels and Lube Conference, San Diego, SAE 2010-01-2181
III. “Simplified Methodology for Modeling Cold Temperature Effects on Engine Efficiency for Hybrid and Plug-in Hybrid Vehicles”, Jehlik, F., Rask, E., Christenson, M., SAE Powertrain Fuels and Lube Conference, San Diego, SAE 2010-01-2213
IV. “Methodology and Analysis of Determining Plug-In Hybrid Engine Thermal State and Resulting Efficiency”, Jehlik, F., SAE World Congress, Detroit, Mi., SAE 2009-01-13081
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