Vehicle Energy Management Optimisation through Digital ... -...
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Transportation Technology R&D Center Vehicle Energy Management Optimisation through Digital Maps and Connectivity Dominik Karbowski, Vadim Sokolov, Aymeric Rousseau Argonne National Laboratory, USA The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory ("Argonne") . Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
Transcript of Vehicle Energy Management Optimisation through Digital ... -...
Transportation TechnologyR&D Center
Vehicle Energy Management Optimisation through Digital Maps and ConnectivityDominik Karbowski, Vadim Sokolov, Aymeric RousseauArgonne National Laboratory, USA
The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory ("Argonne") . Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
Route-Based Energy Management In Practice
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0 10 20 300
20
40
60
Miles
mph
Itinerary Computation
Pattern Recognition
OR
GPS
Live Traffic
Route Prediction
Optimal Energy MgmtDestination
Current Position
Average traffic speed
Detailed Segment-by-Segment Information
Speed & Grade
Route-based Optimization
Optimal Control
Scope of Argonne’s Research
• Original research on speed prediction, an often overlooked problem
• Research on implementable solutions for route-based control
• Evaluation of real-world benefits of route-based control
Driver’s Input
Maps / GIS Can Provide Information About a Given Itinerary
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– Traffic pattern speed: average traffic speed for a given time/day
– Road slope: modeled with splines, not simply from GPS altitude data
– Speed limitations– Position of traffic lights, stop signs,
intersections, and other signs– Category of road– Number of lanes– Etc.
Distance
Vehicle Speed
ADAS-RP
ADAS = Advanced Driver Assistance SystemsRP = Research Platform
But not enough to predict fuel consumption!
Real-World Stochastic Aspect Introduced by Constrained Markov Chains
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Markov Chains
…
0.02
0.050.01
0.02
0.050.01
0.02
0.050.01
……
… … …
…0.03
0.07
0.15
Valid Real-World Micro-Trips
Transition Probability
Matrix
14.5
15.5
16.0
16.5
13.5
14.0
17.0
t+1tt-1t-2
Spee
d (m
/s)
Time (s)
P=0.05
P=0.3
P=0.2
P=0.15
P=0.15
P=0.1
P=0.05
Initialization (t=0, a=0, v=0)
TPM
Random number generation
Compute next state
v=vend?
d>Dtarget?
Metadata matches target?
Speed Profile
Yes
Yes
Yes
No
No
No
0 200 4000
50
100
0 100 200 300 4000
50
100
0 100 200 300 4000
50
100
Constrained Markov Chain
Chicago Travel Survey(10k vehicle trips, 6M data points)
Examples of Synthesized Speed Profiles
5
Target Speed32 km/h
0 100 200 300 400 500 6000
10
20
30
40
50
60
70
80
90
Time (s)
Spe
ed (k
m/h
) / T
ime
(s)
V Vmax Vavgtgt Vavg
act tstop
One synthetic speed profile for one entire itineraryMultiple stochastic speed profiles for the same target micro-trip
Speed Limit50 km/h
High-Fidelity Model of the Prius Plug-in Hybrid (PHEV)
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Power-Split Hybrid-Electric (Toyota Prius Hybrid System)
Driver presses on pedals
Vehicle energy management computes torque demands
Powertrain = all components
200 300 400-50
0
50
100
150
0.05 0.05 0.050.1 0.1 0.10.15 0.15 0.150.2 0.2 0.20.25 0.25 0.250.3 0.3 0.3
0.35
0.35 0.35 0.35
Speed (rad/s)
Torq
ue (N
.m)
Components: dynamics + look-up tables from test data
A forward-looking model of the Prius PHEV in Autonomie
Vehicle Model Includes Energy Management Sensitive to Route
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𝑃𝑃𝑏𝑏∗ = argmin𝑃𝑃𝑏𝑏
(𝑃𝑃𝑓𝑓 𝑃𝑃𝑏𝑏 + 𝒓𝒓𝟎𝟎𝜃𝜃 𝑃𝑃𝑏𝑏 𝑃𝑃𝑏𝑏)
Hamiltonian
Fuel PowerFunction of 𝑃𝑃𝑏𝑏 through optimal operation maps
Equivalence Factor (EQF)
Term close to 1
Battery Power Command
What is the optimal power split (battery power 𝑃𝑃𝑏𝑏) at each time step that will lead to trip-level optimal control? Pontryagin Minimum Principle (PMP) provides the answer:
Optimal Command
SOCtgt
tend
SOCtgt
tend
SOCtgt
tend
SOC drops too fastBattery not used enough
Optimal SOC drop
Key challenge: finding the right EQF
Large-Scale Evaluation
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SOCtgt
tend
0
50
100Vehicle Speed (km/h)
0
50
100
0
50
100
0
50
100
0
50
100
0
50
100
0
50
100
0 5 10 15 200
50
100
Distance (km)
Start
Run EV+CS
∃ tSOC=30%? No PMP
Run PMP
tSOC=30%<tend - δt ?
∃ tSOC=30%? SOCend>SOCtgt+δSOC?
tSOC=30%<tend - δt ? EQF Found
Increase EQF Decrease EQF
No
No No
No
Yes
Yes
No
Yes
Yes
Yes
30 itineraries 8 Generations 3 SOCinit 9 EQFs
× × ×
1 EQF Optimization
+ 8 suboptimal valuesaround
optimal EQF
Example of Result (1 Itinerary, 1 generation)
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0 500 1000 1500 2000 2500 300020
30
40
50
60
70
80
90
Time (s)
SO
C (
%)
0 500 1000 1500 2000 2500 30000
50
100
150
200
250
300
350
400
Time (s)
Fu
el(g
)
RefOpt2.532.542.552.562.582.592.62.61
RefOpt2.532.542.552.562.582.592.62.61
Example of Result (1 Itinerary, 1 generation)
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2.52 2.54 2.56 2.58 2.6 2.62 2.64-15
-10
-5
0
5
10
15
EqF
Fu
el S
avin
g (
%)
8 8.5 9 9.5 1012.5
13
13.5
14
14.5
15
15.5
Battery Energy (MJ)
Fu
el E
ner
gy
(MJ)
unadj.adj
EqF
2.53
2.54
2.55
2.56
2.57
2.58
2.59
2.6
Ref.Opt.Tgt SOC
Fuel savings need to be SOC adjusted: final SOC in optimal case is always 30%, but it varies for reference case (stays in the [28,32] range)
Preliminary Results Show Strong Benefits (Best Case Scenario: Optimized EQF)
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16 18 20 22 24 26 28 30 32 34 36-10
-5
0
5
10
15
20
25
30
Trip Distance (km)
Adj
. Fue
l Sav
ings
(%)
SOC0=50%
SOC0=70%
SOC0=90%
Conclusion
Route-based energy management is a promising way to save fuel, in particular for hybrid vehicles.
Our preliminary results show savings upwards of 5% are achievable Successful implementation require:
– vehicle speed and grade prediction => combination of maps and Markov chains– vehicle controller with optimization => PMP controller – adjusting the calibration to the trip ahead => vehicle model and EQF tuning
Future work will aim at: – improving the EQF prediction method, so that a full vehicle model is not
necessary.– improving of the robustness of the PMP controller by introducing periodical
updating of the optimal EQF.
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Acknowledgement
ContactDominik Karbowski (Principal Investigator): [email protected] / 1-630-252-5362Aymeric Rousseau (Systems Modeling and Control Manager): [email protected]
Funded by the Vehicle Technology OfficeProgram Manager: David Anderson
www.transportation.anl.govwww.autonomie.net
HERE provided a complimentary license for ADAS-RP
The submitted manuscript has been created byUChicago Argonne, LLC, Operator of ArgonneNational Laboratory ("Argonne") . Argonne, aU.S. Department of Energy Office of Sciencelaboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retainsfor itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license insaid article to reproduce, prepare derivativeworks, distribute copies to the public, andperform publicly and display publicly, by or onbehalf of the Government.
