Holistic Hybrid Powertrain Optimization Methodology for ... · Optimization Methodology for...

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


• Motivation




• Outlook

Holistic Hybrid Powertrain Optimization Methodology Motivation


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


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


• Motivation




• Outlook

Holistic Hybrid Powertrain Optimization MethodologyPower/Operation Point Selection


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


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


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


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


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


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




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




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


• Motivation




• Outlook

Holistic Hybrid Powertrain Optimization MethodologySimulation Environment and Toolchain


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


• 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


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


• 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


• Motivation




• Outlook

Holistic Hybrid Powertrain Optimization MethodologySummary & Conclusion


• 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


• Motivation




• Outlook

Holistic Hybrid Powertrain Optimization MethodologyOutlook


• 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

[email protected]

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

[email protected]

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


• 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


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


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


“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


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


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


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


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.

Holistic Hybrid Powertrain Optimization Methodology for ... · Optimization Methodology for...

Documents

Transcript of Holistic Hybrid Powertrain Optimization Methodology for ... · Optimization Methodology for...