Holistic Hybrid Powertrain Optimization Methodology for Electrified Off-Highway Concepts
Rico Möllmann, Reza Rezaei, Dennis Jünemann,
07th October 2019, Frankfurt, GT-Conference
Content
IAV 10/2019 TP-C1 MR8 Status: released for GT conference2
• Motivation
• Engine and EAT evaluation
• System optimization possibilities
• Summary & Conclusion
• Outlook
Content
IAV 10/2019 TP-C1 MR8 Status: released for GT conference3
• Motivation
• Engine and EAT evaluation
• System optimization possibilities
• Summary & Conclusion
• Outlook
Holistic Hybrid Powertrain Optimization Methodology Motivation
IAV 10/2019 TP-C1 MR8 Status: released for GT conference4
Powertrain layout
• Thesis: Simplification of engine layout is possible if the engine runs only in one constant operating point
• A powertrain of a diesel-hydraulic forklift truck is used as application reference
Diesel-hydraulic powertrain of a forklift truck
internal combustion engine
bi-directional variable
displacement pump
variable displacement pump
for lift hydraulic
hydraulic traction drive motor
for vehicle propulsion
Holistic Hybrid Powertrain Optimization MethodologyMotivation
IAV 10/2019 TP-C1 MR8 Status: released for GT conference5
Powertrain layout
• Thesis: Simplification of engine layout is possible if the engine runs only in one constant operating point
• A powertrain of a diesel-hydraulic forklift truck is used as application reference
• The new constant engine operating point powertrain concept uses a diesel-electric serial hybrid structure
• In addition to the electric propulsion drive at least one electric drive is needed to power the hydraulic system
(working drives, steering)
Serial electric hybrid powertrain layout converter/inverter
electric motor
electric
generator
non-controllable hydraulic
pump for lift hydraulic
single point ICE
propulsion
lifting
high capacity
battery
Content
IAV 10/2019 TP-C1 MR8 Status: released for GT conference6
• Motivation
• Engine and EAT evaluation
• System optimization possibilities
• Summary & Conclusion
• Outlook
Holistic Hybrid Powertrain Optimization MethodologyPower/Operation Point Selection
IAV 10/2019 TP-C1 MR8 Status: released for GT conference7
Engine operating points
• The diagram shows the diesel engine
operating points of the standard powertrain
(blue) and the constant operating point
engine (green) in a low load forklift cycle
(according to VDI 2198)
• Using a serial hybrid powertrain layout allows
a constant operating point of the diesel
engine
• The operating strategy runs the engine in the
best point and the generator provides
electrical power to the drives and if possible
to the battery
Lines of constant power
Holistic Hybrid Powertrain Optimization MethodologyThermodynamics Engine Concept Design
IAV 10/2019 TP-C1 MR8 Status: released for GT conference8
Methodology overview of
combustion concept development
using the single cylinder
optimization tool:
• Simulation-based concept
development regarding future
legislations
• Model-based evaluation of
optimal engine concept:
– Optimal PFP optimal CR
– EGR ratio requirement
– Boost pressure requirement
• Estimation of
– BSFC
– NOX emissions
– Exhaust temperature, mass
flow and enthalpy
– Combustion noise
Target values
Exhaust gas
aftertreatment
concept
Emission
regulation
Certification /
operation cycles
Engine-out
NOx
CO2 (GHG)
Peak firing
pressure
Compression
ratio
Torque demand
(load curve)
Boost
pressure
demand
Required EGR
ratio
Injection
pressure
demand
Virtual
optimization
platform using
physical engine
combustion and
emission models
EATS conceptionOptimization
parameters
EATS: Exhaust aftertreatment system
Engine
geometry
Min. AFR
(soot limit)
Holistic Hybrid Powertrain Optimization MethodologyOperation Point Calibration
IAV 10/2019 TP-C1 MR8 Status: released for GT conference9
BS
FC
[g
/kW
h]
ma
x. cylin
de
r p
ressu
re [
bar]
spe
c.
NO
x m
ass
[g
/kW
h]
start of main injection [°CA after TDC]
-6 -4 -2 0 2 4
SC
R o
utle
t te
mpe
ratu
re [
°C]
start of main injection [°CA after TDC]
-6 -4 -2 0 2 4
2 g/kWh 20 bar
10 K 4 g/kWh
Variation of start of main injection
Operation point calibration
• An engine model of an industrial engine was set up with
predictive combustion and emission models
• The operation point of the combustion engine can be
optimized more specific.
• The function complexity for engine heat up can be decreased.
• The engine complexity can be reduced.
- base point
Engine torque was kept constant with injection controller
The constant operating point was calibrated to increase
efficiency and reduced engine out emissions taking the
EAT inlet temperature into account- target point
Holistic Hybrid Powertrain Optimization MethodologyOperation Point Calibration
IAV 10/2019 TP-C1 MR8 Status: released for GT conference10
BS
FC
[g
/kW
h]
ma
x. cylin
de
r p
ressu
re [
bar]
spe
c.
NO
x m
ass
[g
/kW
h]
compression ratio [-]
16.0 17.0 18.0 19.0 20.0
SC
R o
utle
t te
mpe
ratu
re [
°C]
compression ratio [-]
16.0 17.0 18.0 19.0 20.0
2 g/kWh 20 bar
10 K 4 g/kWh
Variation of compression ratio
Operation point calibration
• An engine model of an industrial engine was set up with
predictive combustion and emission models
• The operation point of the combustion engine can be
optimized more specific.
• The function complexity for engine heat up can be decreased.
• The engine is operated in only one operating point hence:
– The engine complexity and costs can be reduced
– The engine compression ratio can be increased as the
operates with lower BMEP at best operating point
- base point
Engine torque was kept constant with injection controller
The constant operating point was calibrated to increase
efficiency and reduced engine out emissions taking the
EAT inlet temperature into account- target point
Holistic Hybrid Powertrain Optimization MethodologyVDI 2198 Cycle
IAV 10/2019 TP-C1 MR8 Status: released for GT conference11
VDI 2198 is used for fuel consumption measurements of forklift trucks
30 m
transit1
2
34
5
6
lift/lower load 2m
reversetransit
lift/lower load 2m
reverse
fork lift
truck
Cycle will be repeated over a defined time
Holistic Hybrid Powertrain Optimization MethodologyHybrid Operation Strategy
IAV 10/2019 TP-C1 MR8 Status: released for GT conference12
Energy flow based evaluation
• The diagrams compare the powertrain operations of the standard powertrain (blue) with the alternative powertrain layout (green)
• If the battery SOC reaches a certain energy level the engine is switched off and the required electrical power is provided by the
battery
• This strategy allows significant engine off time
The hybrid system investigations of the point engine can be well used for function development of heat-up strategy
Te
mp
era
ture
[°C
]
0
50
100
150
200
250
300
350
base system constant point ICE
En
gin
e P
ow
er
[kW
]
-20
-10
0
10
20
30
40
50
60
70
Time [s]
0 50 100 150 200 250 300 350 400 450
base system constant point ICE
Holistic Hybrid Powertrain Optimization MethodologyHybrid Operation Strategy
IAV 10/2019 TP-C1 MR8 Status: released for GT conference13
DEF dosing
threshold
engine switched off engine switched off
SCR wall temperature
Engine Power
Simulation results
• By operating the combustion engine only
in one constant operation point the SCR
catalyst reaches the DEF dosing threshold
after around 95 seconds.
• The base system reaches this threshold
after 330 seconds.
• When the engine is switched off the
catalyst cools down slowly below the
dosing threshold.
• After restart of the combustion engine the
threshold is reached after 12 seconds.
• The VDI cycle is a low load cycle.
Therefore the battery is charged faster
and hence the engine is switched off
earlier.
cum
. fu
el con
su
mp
tio
n[g
]
0
100
200
300
400
500
600
700
800
Time [s]
0 50 100 150 200 250 300 350 400 450
Te
mp
era
ture
[°C
]
0
50
100
150
200
250
300
350
base system constant point ICE
Holistic Hybrid Powertrain Optimization MethodologyHybrid Operation Strategy
IAV 10/2019 TP-C1 MR8 Status: released for GT conference14
DEF dosing
threshold
engine switched off engine switched off
SCR wall temperature
Fuel consumption
-8%
Simulation results
• By operating the combustion engine only
in one constant operation point the SCR
catalyst reaches the DEF dosing threshold
after around 95 seconds.
• The base system reaches this threshold
after 330 seconds.
• When the engine is switched off the
catalyst cools down slowly below the
dosing threshold.
• After restart of the combustion engine the
threshold is reached after 12 seconds.
• The VDI cycle is a low load cycle.
Therefore the battery is charged faster
and hence the engine is switched off
earlier.
• The calculated fuel consumption
advantage of the electric hybrid system in
VDI cycle is about 8% (SOCstart = SOCend)
Content
IAV 10/2019 TP-C1 MR8 Status: released for GT conference15
• Motivation
• Engine and EAT evaluation
• System optimization possibilities
• Summary & Conclusion
• Outlook
Holistic Hybrid Powertrain Optimization MethodologySimulation Environment and Toolchain
IAV 10/2019 TP-C1 MR8 Status: released for GT conference16
Simulation of GT-Suite component models
• GT-SUITE modeling approaches for system components:
– Generator/motor
– Traction battery
– Combustion engine model for transient operation
– Exhaust after-treatment for holistic simulation
– Vehicle modeling
Holistic Hybrid Powertrain Optimization MethodologyOptimization Possibilities
IAV 10/2019 TP-C1 MR8 Status: released for GT conference17
• Optimization and simplification of combustion engine with respect to emissions and fuel consumption
• Simplification of exhaust after-treatment architecture
• Optimization of traction battery capacity with respect to engine operation strategy
• Optimization of battery architecture with regard to maximum required charge and discharge power
• Optimization of electric propulsion and battery architecture with regard to drivability and electric energy
consumption
• Optimization of electric motor for work hydraulic application, matching of motor and hydraulic pump
There are various possibilities for system optimization
Holistic Hybrid Powertrain Optimization MethodologyEvaluation Propulsion Motor
IAV 10/2019 TP-C1 MR8 Status: released for GT conference18
The use of a 2-gear transmission could improve the electric energy consumption at higher travel speeds
Constant vehicle speeds• Evaluation on required electric power for propulsion motor
0.92
0.90
0.88
0.88
0.85
0.80
0.75
0.70
0.65
0.60
0.55
0.50
0.45
0.40
Torq
ue [
Nm
]
0
50
100
150
200
250
300
350
400
450
500
Motor speed [rpm]
0 500 1000 1500 2000 2500 3000 3500
Transmission ratio 20Transmission ratio 10
Vehicle mass: 7300 kgMaximum load (5000 kg)Half load (2500 kg)No load
Required e
lectr
ic p
ow
er
[kW
]
0
5
10
15
20
25
30
35
Vehicle speed [km/h]
6 9 12 15 18 21
Transmission ratio 20 Transmission ratio 10
Vehicle mass: 7300 kgMaximum load (5000 kg)Half load (2500 kg)No load
Holistic Hybrid Powertrain Optimization MethodologyEvaluation Battery Architecture
IAV 10/2019 TP-C1 MR8 Status: released for GT conference19
• Cells with 57 Ah and 3.3 V were tested
• A battery with 80 can provide most of the
demanded power for steady-state
demand
• A battery with 96 sells can provide the
demand power except for some high
power peaks
• A battery with 112 cells can provide the
required power for the electric motors
Dis
charg
e b
att
ery
pow
er
[kW
]
0
5
10
15
20
25
30
35
40
45
Time [s]
100 120 140 160 180 200 220
Power demandavailable discharge power of battery with: 48 cells
64 cells 80 cells 96 cells 112 cells
Content
IAV 10/2019 TP-C1 MR8 Status: released for GT conference20
• Motivation
• Engine and EAT evaluation
• System optimization possibilities
• Summary & Conclusion
• Outlook
Holistic Hybrid Powertrain Optimization MethodologySummary & Conclusion
IAV 10/2019 TP-C1 MR8 Status: released for GT conference21
• Using a single point engine operation the ICE and after-treatment can be optimized for the best operating point
• Investigation were performed on exchanging and conventional diesel-hydraulic powertrain of a forklift truck with
a diesel-electric powertrain with a single point operation ICE and traction battery
• Possible improvements of the combustion system operation point were evaluated
• Boundary condition for an exhaust after-treatment system evaluation were simulated
• Investigations on electric powertrain were carried out for transmission system
• Using the system simulation platform the battery architecture and size can be optimized
Content
IAV 10/2019 TP-C1 MR8 Status: released for GT conference22
• Motivation
• Engine and EAT evaluation
• System optimization possibilities
• Summary & Conclusion
• Outlook
Holistic Hybrid Powertrain Optimization MethodologyOutlook
IAV 10/2019 TP-C1 MR8 Status: released for GT conference23
• In order to make a specific concept design, detailed boundary conditions regarding packaging other vehicle
limitations required
• In order to improve the model quality and prediction ability, following model development can be performed:
Carry out of advanced holistic system investigation
Implementation of hybrid emission models
Exchange of fast-running engine model with AI-based engine model to speed up the system simulation
Implementation of hydraulic system simulation
Contact
Rico Möllmann
Advanced Engineering & Model-Based
Development
Commercial Vehicle Powertrain
IAV GmbH
Nordhoffstr. 5, 38518 GIFHORN (GERMANY)
Phone +49 5371 80 – 53974
www.iav.com
Priv.-Doz. Dr.-Ing. habil. Reza Rezaei
Manager
Advanced Engineering & Model-Based
Development
Commercial Vehicle Powertrain
IAV GmbH
Nordhoffstr. 5, 38518 GIFHORN (GERMANY)
Phone +49 5371 80 – 52271
www.iav.com
Appendix I
Additional slides on modeling approaches
Engine test bench
Engine model
Inlet boundary:
• Exhaust mass flow
• Exhaust gas temp.
• Concentrations
Coupling to
EAT models
EAT models
Holistic System Investigation: Engine + EAT
IAV 10/2019 TP-C1 MR8 Status: released for GT conference26
• Engine model with predictive combustion and NOx model instead of test bench
Predictive combustion and NOx model as well as EAT models are key factors
DPF in
measured simulated
Tem
pera
ture
[°C
] (D
= 1
00 K
)
SCR in
Time, s
ASC out
Cu
m.
Po
wer
[kW
h]
measured simulated
Cu
m.
NO
x [
g]
T a
fter
turb
ine [
deg
C]
Time [s]
Holistic System Investigation: Engine + EAT
IAV 10/2019 TP-C1 MR8 Status: released for GT conference27
Validation of the full engine model – transient
• E.g. cold-start FTP cycle
• Thermal inertias taken
into account
Validation of EATS model
cold FTP
transient
results
cycles (e.g. RMC, FTP,
WHTC, customer)
Cum. power
E-out NOx emissions
T after turbine
• Thermal behavior
• Pollutant conversion rates
• DEF dosing functionality
Full engine
model incl.
TC group
Engine and EAT models were validated with transient test data
Holistic Engine and EAT Optimization Methodology
IAV 10/2019 TP-C1 MR8 Status: released for GT conference28
Aim: optimal system architecture (engine emission
concept & EATS system layout) and operation:
• Lowest possible CO2
• While complying with (future) NOx limits
Optimization Strategies
• Using holistic approach the CO2/NOx trade-off can be
well optimized
• Multiple measures for thermal management, like
intake/exhaust throttling, etc. can be optimized
• Exhaust after-treatment technology can be best
optimized considering the real engine behavior
Further details in IAV’s publication to this topic:
SAE 2018-01-1700
Holistic model is used for optimization of system architecture and operation strategy optimization
IAV Novel Hybrid Emission Model
Physico-chemical emission modeling
• Predictive air-path and comb. Model. Very good extrapolation ability for high altitude
or other engine operating modes
• 3D phenomena in mixture formation can not be captured by 1D sim. Always poor
results for soot, HC and CO
Classical DoE emission modeling
• No physical and chemical background but very good match for NOx, soot. CO, HC
• Extrapolation and changing operating mode and comb. can lead to poor results
• Not suitable for high altitude optimization
Artifical Intelligentic for hybrid emission modeling of NOx, soot, HC and CO
• Consideration of representative parameters from the combustion model, like flame
temp., duration of comb., etc. for training of the data-driven approach
• Extrapolation due to physical combustion modeling possible
• Artificial intelligence methods can be used using characteristic parameters from the
combustion model for emission prediction
IAV 10/2019 TP-C1 MR8 Status: released for GT conference29
“Classical”
DoE
emission
model
Artificial
Intelligence
for Hybrid
emission
model
nEng
minj
EGR
SOImain
Boost pressure
λExhaust
EGR + res. gas
Rail pressure
Ignition delay
O2,exhaust
Predictive
comb.
model
IAV Novel Hybrid Emission ModelValidation results - Soot
IAV 10/2019 TP-C1 MR8 Status: released for GT conference30
Evaluation of soot emission model
• Simulated results show relative
low deviation for all data
• Good results for validated data
Soot
[FS
N]
Case [-]
0 25 50 75 100 125 150 175 200 225 250 275
Measured Simulated Validated Data
Validated DataTrained Data Trained Data
0.2 FSN
IAV Novel Hybrid Emission ModelInput parameter for the hybrid and classical approach
IAV 10/2019 TP-C1 MR8 Status: released for GT conference31
t(CS-CA50), 𝜆, SOIlast_event, Tmax, averaged, Theta
n, 𝑚𝑖𝑛𝑗, EGR, SOImain, boost pressure
Hybrid modeling approach for HC emissions
Conventional /classical DoE model
t(CS-CoC): time between combustion start and center of combustion
Theta: Injection spray cone angle of IAV Combustion Model
Using representative parameters from predictive combustion model, instead of normal engine parameters, as input
parameters for the data-driven emission modeling is the main difference between hybrid and classic DoE modeling
Artificial Intelligence (AI) based fast-running Simulation Models
IAV 10/2019 TP-C1 MR8 Status: released for GT conference32
Physical based „mean-value-cylinder“ within GT-Suite will decrease computational time
Necessary inputs are IMEP, exhaust gas temperature and volumetric efficiency can be simulated by AI model
AI core model
Artificial Intelligence (AI) based fast-running Simulation ModelsResults for transient Engine Simulation
IAV 10/2019 TP-C1 MR8 Status: released for GT conference33
The artificial intelligence shows reasonable results despite of being trained for steady-state only
Exh
au
st
ga
s t
em
pe
ratu
re [
K]
300
400
500
600
700
800
900
1000
1100
1200
time [s]
0 200 400 600 800 1000 1200
Vo
lum
etr
ic e
ffic
ien
cy [
-]
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
Neural network Detailed GT-Suite modelArtificial intelligence
Detailed GT-Suite model
• There is a big difference in
simulation time.
• Good results, especially for exhaust
gas temperature which is essential
for EAT system.
• As using physical-based mean-
value GT-Suite model, physical
phenomena like thermal behavior
can be considered.
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