© by FEV

© by FEV – all rights reserved. Confidential – no passing on to third parties

| 1


1st Session June 9th GHG – Trends and Legislation

2nd Session June 16th Low NOx – Trends and Legislation

3rd Session June 23rd Alternative Energy and Powertrains

4th Session June 30th Hydrogen Powered Future

5th Session July 2nd Focus Electrification

▪ Content:

− Five-session Virtual Series

− Insights into the future of On-Road

HD Commercial Vehicles

− Global Strategic Focus items

− Two hours per Session

− Four Focus Topics per Session

▪ Industry Partners

− Truck OEM’s

− Supply Base

Let’s stay in Close Contact!

FEV Future Truck Series

June – July 2020

STAY IN CLOSE CONTACT

OBJECTIVE

https://fev-live.com/webinars | 2


PART 1: CLEAN FUELS TOWARDS A CO2-NEUTRAL TRANSPORT

▪ The Need for Alternative Fuels

▪ What Fuel to Choose

▪ Synthesis and Costs of Alternative Fuels

PART 2: BENEFITS OF HD POWERTRAIN HYBRIDIZATION

▪ Modular Mild Hybrid System for Heavy-Duty On-Road Trucks

▪ Full Hybrid Powertrain for Class 6-7 Urban Truck

▪ Range Extender for Medium Duty Delivery Trucks

▪ FEV Hybrid Powertrain Development Services

PART 3: CUSTOMIZED CONTROL FUNCTIONS TO REACH LOW CO2, LOW EMISSIONS AND LOW SYSTEM COST

▪ FEV's Controls Roadmap for Heavy Duty Diesel Powertrains

▪ Development Examples

▪ FEV Function Development Services

AGENDA

| 3


PART 4: BENEFITS OF VIRTUAL POWERTRAIN CALIBRATION FOR HD ON-ROAD VEHICLES

▪ Introduction to FEV’s engineering for virtual calibration

▪ Overview of real-time powertrain modeling

▪ Hardware-in-the-Loop test bed

▪ Use case development in vehicle calibration and validation

AGENDA

| 4

3RD SESSION FEV WEBINAR

FUTURE TRUCK SERIES

ALTERNATIVE ENERGY AND

POWERTRAINS

BENEDIKT HEUSER, FEV EUROPE GmbH

ERIK KOEHLER, FEV INC.

MUFADDEL DAHODWALA, PHD, FEV INC.

JUNE 23RD 2020

WEBINAR PRESENTATION

Replace with

meaningful, high

resolution image

CLEAN FUELS

TOWARDS A CO2 NEUTRAL TRANSPORT















AGENDA

| 6


CO2 EMISSIONS IN MILLION TONS

Source: FEV, historical data EEA Benedikt Heuser, Future Truck Series, Alternative Fuels, 23rd of June 2020

Three low carbon emission pathways for heavy-duty trucks to reach the

emission targets have been developed

| 7

0

1990 2030

200

2010 2050

100

300

Year

-80%-95%

1990 level

◼ All pathways apply four measures

− Optimization of usage

− Electrification of powertrains

− Efficiency increase of vehicles

− Adaption of energy carriers

Scenario

Current

Policies

Balanced

Energy Carriers

Accelerated

Transformation

Approaching

Zero


DRIVERS OF THE CO2 EMISSION REDUCTION IN AN EXTENDED TANK-TO-WHEEL BALANCE1)

Source: FEV Benedikt Heuser, Future Truck Series, Alternative Fuels, 23rd of June 2020

In the Approaching Zero scenario, almost half of the total CO2-reduction is

enabled by adaption of energy carriers

| 8

1) The extended tank-to-wheel balance considers the CO2 emissions created when conversing the energy carrier to kinetic energy and a subtract of carbon storage that are realized during the production of the energy carrier.

200

20201990 2030 2040

0

2050

100

300

400

Year

CO2 emissions in million tons

-26%

-13%

-15%

-47%

Development stop

ResultUsage

Electrification

Efficiency

Electrification

Efficiency

Energy carrier

2050 ambition

1990 level

Usage

◼ In the approaching zero scenario the CO2 emissions

can be reduced to 8 million tons per year and thus

achieve the goal of a 95% reduction compared to 1990

◼ Usage contributes to the reduction by two factors that

offset a 3%-point higher share of goods transported

on-road compared to 2018

− 5%-point reduction of the share of heavy-duty

vehicles in on-road transport

− 5%-point increase of average truck utilization

◼ Electrification represents a significant number of

hybrid, followed by battery electric and fuel cell electric

vehicles in considerable numbers

◼ Efficiency increase contributes to the CO2 emission

reduction by improvements of gliders and powertrains

◼ Energy carriers includes an effective blend share of

renewables at 93 vol.-% in liquid fuels and 95 mass-%

in gaseous fuels

Extended tank-to-wheel balance


FINAL ENERGY DEMAND

Source: FEV Benedikt Heuser, Future Truck Series, Alternative Fuels, 23rd of June 2020

Final energy carrier demand of heavy-duty trucks in Europe, broken down by

energy carrier type and source in 2018 to 2050

| 9

Gaseous from renewables

Gaseous from fossils

Liquid from fossils

Liquid from renewables

Electric from fossils

Electric from renewables

◼ Huge demand for energy from

renewables

− 5 billion kg of hydrogen

− 40 billion liters of liquid fuel

− 150 PJ of electricity

Predicted energy demand of heavy-duty trucks in PJ

Approaching Zero Scenario5%

6%

94%

2018

85%

2030

13%

5%

54%

1%

34%

2040

27%

5%

60%

6%

2050

3,0252,853

2,593

2,282

-25%


RECENT AND ONGOING PUBLIC FUNDED PROJECTS

Synthetic fuels play a major role in FEV‘s research activities

| 10

FEV FEV FEV IFPEN RWE JLR/Ricardo CRF

42 12 36 46 36 39 48

25 Mio € 5 Mio € 25 Mio € 5 Mio € 6 Mio € 10 Mio € 17 Mio €

H2, Methanol,

DME, HVO,

Ethanol

Paraffins +

higher alcohols

DME, Octanol,

Methanol, MtG

Butanol

Hydrogen

additivated

Gasoline

DME, OMEx

Gasoline

w/ water

injection

Methane

01/2020 –

06/2023

10/2018 –

09/2021

08/2018 –

07/2021

10/2016 –

06/2020

07/2017 –

06/2020

10/2016 –

12/2019

05/2015 –

04/2019


PEMS for LD and

HD applications

4W Chassis

dynanometer

Light-duty single

cylinder engines

Heavy-duty single

cylinder engines

Multi-cylinder

enginesVehicles for multi-

fuel use

Turn-key

development

FEV’s fuels engineering services range from fundamental research

to turn-key projects – for all kind of fuels

11

COLLABORATION WITH RWTH AACHEN UNIVERSITY FOR MANY YEARS

IgnitionFuel formulation

Optically accessible

engines (non-fired)Pressure chambersKinetics

Optically accessible

engines (fired)

Global fuel

scenarios and

market trends

Benedikt Heuser, Future Truck Series, Alternative Fuels, 23rd of June 2020















AGENDA

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RENEWABLE HYDROGEN IS REQUIRED FOR ALL E-FUELS

Electricity based chemical energy carriers show a huge variety

| 13

Wind power

Solar power

CO2 from industry

Methanol

CO2/H2O from air

C/(CO2) from biomass

H2

Paraffins (via

Fischer-Tropsch)Ethanol

Hydrogenated

vegetable oil (HVO)Methane

WaterH2O

CO2

Di-Methyl etherOxymethylen

ether

Methanol-to-

Gasoline

Higher Alcohols (via

Hydroformylation)Butanol

Direct use of H2

(ICE or FC)

Chemicals and

Additives


H2 is the cheapest renewable and clean “burning” fuel with

initial applications being introduced

HYDROGEN REQUIRES NEW DISTRIBUTION NETWORKS

Hyundai H2 Xcient

▪ 35 kgH2 allow for a driving range of over 400 km

▪ Delivery starts in 2020, and 1,600 fuel cell trucks by

2025

▪ H2 is the cheapest renewable chemical energy carrier

▪ H2 is widely used already (mainly produced from nat.

gas)

▪ Fuels storage and distribution is challenging

▪ Available applications are very rare but will rise

(fuel cell or combustion engine)

▪ Renewable H2 will be available in large amounts

▪ Tank-to-Wheel: 0 g/CO2

H2 WILL BE A CENTRAL COMPONENT OF CO2 NEUTRAL MOBILITYFIRST HD FUEL CELL TRUCKS ANNOUNCED

Source: Hyundai | 14

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Hydrogen + o +/o – –


Source: china-hydrogen; chyxx, h2.live, H2stations.org, LBST, TÜV SÜD, nytimes, energy.gov, Nikkei, forbes

H2 Infrastructure development forecast

Total number of H2 stations

| 15

128

75

2

15102

1

1

1

4

19

128

1.998

4,126

20252019 2030

15 517

2019 20302025

75 500

1.000

2019 20302025

102 320

900

2025 20302019

Europe ChinaUSA Japan

4,650


HYDROGEN READY INTERNAL COMBUSTION ENGINE AND FUEL CELL TEST BENCHES

Testing facility “European Technical Center”

FEV test benches (CMP) in Aachen are Hydrogen ready

| 16

◼ Hydrogen specs:

− 25(80) bar H2 pressure upstream ICE/FC

− 40 kg/h max. H2 fuel flow

− H2 quality range from 3.0 … 8.0

◼ Bench features:

− Full transient operation (WLTP, WHTC,

WHSC, RDE-cycles, etc.)

− Up to

− 500 kW FC electrical power

− 640 kW IC engine power

− 4500 Nm IC engine torque

− Flow measurement of H2 and Air

− FEV FEVER emission measurement


Natural gas (methane) enables a TtW-CO2-reduction of up to 15% compared

to Diesel – but also requires new combustion system

MANY FINANCIAL INCENTIVES FOR GAS VEHICLES PURCHASE AND USE (E.G. NO TOLLS)

▪ CNG and LNG trucks in series production already

▪ Emissions and performance comparable to Euro VI

▪ Natural gas (from fossil) is cheap

▪ Biomethane and e-Methane (Sabatier) are CO2 neutral

▪ Additional hardware costs are often compensated by

incentives

▪ Fueling network is often poor

CNG/LNG ➔ US+Canada: 900/68; Europe: 3,848/248

▪ Natural gas is the only alternative fuel available on

short-term in large scale

ESTABLISHED GAS GRID FOR STORAGE & DISTRIBUTIONGAS ENGINES HAVE DIESEL-LIKE PERFORMANCE

Source: Scania | 17

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FT(-HF) +(/o)1

+/o +(/o)1

o -

1) When derived from renewables


Methanol is an already established platform molecule for

the chemical industry / fuel production and an excellent fuel!

METHANOL IS A PROBABLE ENERGY CARRIER FOR IMPORTING RENEWABLE ENERGY TO EUROPE

▪ Methanol-powered carriers already on the oceans

▪ M100 trucks available in China

▪ Sulphur free, ultra-low PM emissions

▪ Methanol is cheap to produce

▪ Established product and building-block

▪ Handling and infrastructure is considered to be more

complex

▪ Available applications very limited

(Legislation limits MeOH to 3% v/v in Europe, but

significant push from Asia)

▪ Methanol utilization in transport will significantly

rise

PROMISING ALTERNATIVE FOR SI AND CI ENGINESMETHANOL IS USED AS FUEL ALREADY

Source: bigwheel | 18

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Methanol +/o o + o o/–


Di-Methyl ether is a promising fuel for commercial transport if

not relying on public fueling pumps

DME IS ALREADY ESTABLISHED IN MANY PARTS OF THE WORLD IN CHEMICAL INDUSTRY

▪ DME is easy and cheap to produce

▪ DME is a standardized fuel in the US already

▪ LPG-like handling

▪ Available applications currently limited

▪ New vehicles required, new filling stations

▪ Clean fuel for commercial transport with own hub /

fuelling infrastructure

DME IS A PROMISING FUEL FOR SELECTED APPLICATIONINTEREST IN DME CONTINUOUSLY INCREASES

Source: Courtesy of DME Association | 19

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hic

les

DME +/o o + o o/-


Fischer-Tropsch fuels (optionally including hydroformylation)

can be blended into the existing vehicle fleet almost without limitation

FUEL CHARACTERISTICS CAN BE TAILORED TO BE COMPLIANT TO CURRENT FUEL STANDARDS

▪ Drop-In type fuel for all Diesel-engine applications in

the world

▪ FT(-HF) is more energy intense and costly to produce

▪ Technology is robust and widely known

▪ FT(-HF) can be used in existing vehicle fleet and

infrastructure

▪ Neat FT as fuel for airborne applications

▪ Clean fuel for all applications relying on worldwide

available infrastructure, particularly goods transport

FISCHER-TROPSCH IS WELL KNOWN TECHNOLOGYFISCHER-TROPSCH ARE CLEAN BURNING FUELS

Source: MAN | 20

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FT(-HF) o/- +/o +/o + +


ENERGY CARRIERS – ROADMAP OF WIDESPREAD CO2 REDUCTION MEASURES

* FEV scenario

Source: FEV

Roadmap of widespread CO2 reduction measures: Europe, HD sector,

long-haul and regional haul; focus group: Energy carriers

| 22















AGENDA

| 23


ENOUGH SOLAR POWER AVAILABLE TO PROVIDE ENERGY FOR THE WORLD

Sources: https://globalsolaratlas.info/ https://globalwindatlas.info/ DLR Statista 2020 Bloomberg NEF

Renewable energy is sufficiently available for production of

CO2 neutral chemical energy carriers

| 24

Win

d p

ow

er

de

nsity

Re

qu

ire

d a

rea

to

co

ve

r w

orld

en

erg

y d

em

and

in 2

05

0

Domestic renewable energy (primarily wind)

◼ Storage in chemical energy carriers and re-

electrification in times of calm darkness

Import of renewable energy in the form of chemical carriers

◼ In MENA an area of 0.576 Mio km² (0,3% of the worlds

landscape, 1% of today’s agriculture) is sufficient to

cover the worlds energy demand in 2050 by e-fuels

PV

ele

ctr

icity o

utp

ut

1000km

Assumptions:

Solar radiation: 2,500 kWh/a*m²

ηPV: 22.5% ηH2: 77.5% ηSynth: 82.5%

Electricity demand: 38,700 TWh

Energy demand: 240,000 TWh

Required surface area covered by PV to meet world electricity demand

Required surface area covered by PV to meet world energy demand with e-fuels


PRODUCTION COST ANALYSIS FOR PRODUCTION REGION MIDDLE EAST/ NORTH AFRICA (MENA) IN 2030

1) Diesel equivalent

Source: Wittler, Wienen, Value chain analysis of fuels from renewables, FEV Consulting GmbH, 2019

Production cost of below 1 € per liter diesel equivalent can be achieved

| 25

◼ Electricity costs dominate production costs

◼ Electricity price of 37 €/MWh are possible in MENA

region

− High availability of wind and solar energy

− Industrial electricity is required to achieve high

utilization of power to liquid plant

− Calculation made with the price of wind

electricity of 25 €/MWh, but price might drop to

15 €/MWh in windparks. Thus potentially

reducing the electricity costs.

◼ Key assumption

− 1500 MWPtL plant size

− 8000 h/a full load hours

− CO2 costs of 30 €/tCO2

Production cost per liter1) in Euro

0.16 €

0.07 €0.04 €

0.09 €

0.72 €

-0.12 €

0.97 €

Investment costs

Electricity

Miscellaneous

CO2

Maintanence and repairs

Byproducts income


MEASURES COULD ALSO BE COMBINED TO INCREASE IMPACT

Political actions required for e-fuel introduction, but different impact

on fleet CO2-emissions, investment security, consumer acceptance

| 26

Fossil energy

ConsumerAutomakerCO2

Fuel industry

CO2 tax on fossil fuelsI

Automaker sponsor renewablesII

Public tenders for e-fuelsIII

E-Fuels quotaIV

Fuel industry

= XX%!

Consumer

Fuel industryFuel industry Consumer

Consumer

Fuel industryConsumer


ADDRESSABLE MARKET OF FUELS FROM RENEWABLES IN EUROPE

1) 2050 information derived from a scenario to comply with the European greenhouse gas emission objective assuming 95% reduction in transport sector

2) Considering retail prices of $ 1.50 per liter PtX, $ 1.20 per kg methane and $ 4 per kg hydrogen.

The addressable market for fuels from renewables in 2050 is significant

European market alone accounts for > $ 270 billion

| 27

◼ Within most-likely scenarios for

greenhouse gas emission reduction,

fuels from renewables are elementary

◼ Addressable market of fuels from

renewables for on-road transport in

Europe in 2050

− 153 billion liters of liquid fuels

from renewables

− 9 billion kg of hydrogen from

renewables

− 6 billion kg of methane from

renewables

$ 274 billionaddressable market for fuels from renewables

in Europe in 2050**

95%73%

5%

2018

6%Electricity20%

2050

Fuels from

renewables

Fossil fuels

-37%

Energy demand in 2018

18%

13%

12%

57%

USA

Europe

China

Others

Energy carrier 2018 and ‘501)

96,900 PJ


Combination of

measures required

to achieve CO2

reduction targets

Various fuel types

possible

Legislation has to

change to create a

business case for

the stake holders

▪ Powertrain electrification will rise also in commercial vehicles, but Diesel-type engine

will remain the prime powertrain concept

▪ Liquid and gaseous fuels will remain the prime energy carrier in heavy duty

applications

▪ There is not the silver bullet fuel – all candidates have pros and cons

− The best solution will depend on country and local boundaries

− Diesel-type fuels will have the highest market share

− New fuels will be introduced into the market

▪ Alternative fuels are becoming less expensive but costs remain higher than for fossils

− Politics have to create incentives to enable fast market introduction of e-fuels

Key findings | 28

THANK YOU FOR

YOUR ATTENTION

4TH INTERNATIONAL CONFERENCE

ZERO CO2 MOBILITY

10–11 NOVEMBER 2020

AACHEN, GERMANY

REGISTER

EARLY FOR A

DISCOUNT AND

JOIN THE EXPERTS!

zero-co2-

mobility.com


FEV Group GmbH - Neuenhofstraße 181 - 52078 Aachen - Germany - www.fev.com

Contact

ALTERNATIVE ENERGY AND POWERTRAINS

FOR HEAVY DUTY APPLICATIONS

Q&A

Dipl.-Wirt.-Ing.

Benedikt HeuserSenior Project Manager

Diesel Powertrains

Phone +49 241 5689-3947

Mobile +49 160 7463658

[email protected]

| 30



▪ Clean fuels towards a CO2-neutral transport

▪ Benefits of HD powertrain hybridization

▪ Customized control functions to reach low CO2, low

emissions and low system cost

▪ Benefits of virtual powertrain calibration for HD on-

road vehicles

▪ Content:








− Truck OEM’s

− Supply Base



June – July 2020


OBJECTIVE



FUTURE TRUCK SERIES


POWERTRAINS

DR. JOSCHKA SCHAUB, FEV EUROPE GmbH

SATYUM JOSHI, FEV INC.

JUNE 23RD 2020


Replace with

meaningful, high

resolution image

BENEFITS OF HEAVY-DUTY

POWERTRAIN HYBRIDIZATION



▪ The need for alternative fuels

▪ What fuel to choose

▪ Synthesis and costs of alternative fuels










AGENDA

| 33


FUEL ECONOMY/CO2 EMISSION REGULATION – HEAVY COMMERCIAL VEHICLES

1) FEV Scenario

Source: EPA, EC, transportpolicy.net, Dieselnet, ICCT 04/2019, FEV

CO2 emission regulations will be tightened for commercial vehicles: in US

and CN already effective, EU recently introduced standards for 2025/2030

34

12 13 14 2015 16 17 18 19 2020 21 22 23 24 2025 26 27 28 29 2030

CO2 g/tkm

GHG / FuelCO2/tmls

gal/1000 tmls

Fuel l/100km

US

EU

China

Stage IStage III

Stage II (National Standard)

10.5-14.5% lower limits Stage IV1)

approx. 15% lower limits

compared to Stage II

GHG Phase 1

Phase-in

15-20-40-60%GHG Phase 1 GHG 2 (2021) GHG2 (2024) GHG2 (2027)

2025 standards,

credits/debts accumulation

CO2 emission credits/debts

accumulation

15% reduction by 2025

compared to 2019 baseline30% vs.

2019-15%

-30%

15% - 29% GHG reduction

(depending on vehicle type)


CO2 is just one of the various requirements…

35

Fuel efficiency

Performance

Payback Period

Emission / Aftertreatment

Temperature

Cost / Weight Increase

Package Increase


LOW VOLTAGE 24 V / 48 V TOPOLOGIES

Source: Springer, FEV

Classification of different hybrid systems by type of hybridization and

functionality for heavy duty applications

36

Micro Hybrid

Stop-Start

Limited

recuperation

Stop-Start

Recuperation

ICE operating

point shift

Electric boost

Waste-Heat-

Recovery

Mild Hybrid

Electric Power

Voltage

Typ

e o

f h

yb

rid

izati

on

an

d f

un

cti

on

ali

ty

up to 5 kW

24V

Stop-Start Stop-Start

RecuperationRecuperation

ICE operating

point shift

Boost

e-drive within

short range

(up to 5 km)

Full Hybrid

up to 25 kW

48V + 24V

100 kW and more

High Voltage + 24V

ICE operating

point shift

Boost

e-drive within

limited range

(up to 100 km)

External

charging

Plug-In Hybrid /

ReX















AGENDA

| 37


FEV INVESTIGATION: CO2 IMPROVEMENT MEASURES

European CO2 legislation

38

1 2 3VEHICLE EFFICIENCY

Improvement ~6%

▪ Aerodynamic improvements

− Weight & dimensions legislation

− Extended front end, fairing, mirror

cams

▪ Transmission efficiency

▪ Low rolling resistance tires

▪ Vehicle mass reduction

POWERTRAIN EFFICIENCY

Improvement ~5%

▪ Combustion efficiency

− Compression & injection control

▪ Air management improvements

− High efficiency EGR & turbo charging

▪ Parasitic losses

− Piston & liner system

− Variable auxiliaries

VEHICLE USAGE

Improvement ~8%

▪ Decentralized supervisory control

− Predictive mission-, energy-, power-

and aftertreatment manager

▪ Waste heat recovery

▪ Modular 48V mild hybrid

− Low cost system

− Minimal adaptations to current vehicle

Fairings

Extenders


CompressorInlet

ExhaustSource: FEV

Evaluation of a Modular Mild Hybrid System with WHR for Heavy Duty On-

Road Applications – Five Modules

39

CAC

HP EGR

Turbine

ISG

EG

Expander

E

A

TS

Condenser

Battery

48 V

20-25 Ah

0.96-1.2 kWh

Electric Power

Steering Pump

Mechanic &

Electric Coolant

Pump

Electric AC

Compressor

Vehicle Board Net

Battery 12/24V

Exhaust Manifold

Intake Manifold

5

3

1

2Electric air

compressor

2

4

WHR

© by FEV – all rights reserved. Confidential – no passing on to third parties |

− Plant models for engine, EATS, powertrain,

hybrid,

vehicle, driver

− Flexible control functions for all systems

− Modular simulation tool realized in

Matlab/Simulink

− Real time capable (even faster)

− Included post processing and optimization

functions

− Model pre calibration with benchmark data

and research projects

− Also experienced in third party simulation

tools

FEV Mean Value Simulation Toolchain for Powertrain Concept Evaluation

40

Key facts

Vehicle

Road, Mission

(Cycle database)

HCU

Results

Co

ntr

oll

er

Mo

dels

Pla

nt

Mo

dels

EngineHybrid System EATS

ECU DCU

Transmission

TCU

Driver/Predictive Powertrain

Control

EATS: Exhaust Aftertreatment System; TCU: Transmission Control Unit; ECU: Engine Control Unit, DCU: Dosing Control Unit

…


RESULTS OVERVIEW: LONG HAUL CYCLE

41Source: FEV

Future efficient commercial powertrains

Simulation results – Long Haul Cycle

30,828,9

27,526,1 25,8 25,1 25,1 25,026,3 25,9 25,1 25,1 25,1

+ 48V w/

Load Shift

Base + Engine

Efficiency

+ Predictive

Powertrain

Ctrl

+ WHR

-15%

-5% -5% -1% -3% nihil nihil

700 W Auxiliaries

400 W AuxiliariesFC

[l/100km] Conventional 48V

-6%

-19%

Vehicle

Efficiency

-16%

+ e-Air

Compressor

+ e-Coolant

Pump

Results

Combination of measures can achieve 2025 CO2 targets

− Vehicle and powertrain efficiency measures

− Predictive powertrain controls

However

CO2 review in 2022 to include extended predictive controls

2030 CO2 target achievement requires ‘radical’ measures

− Renewable fuels & ICE / Fuel Cell


RESULTS OVERVIEW: CONSTRUCTION CYCLE

42Source: FEV

Future efficient commercial powertrains

Simulation results – Construction cycle

44,442,4

41,440,0 38,9 38,2 38,3 38,240,3 39,0 38,3 38,4 38,3

+ 48V w/

Load Shift

Base + Engine

Efficiency

+ Predictive

Powertrain

Ctrl

+ WHR

-10%

-2% -3% -3% -2% nihil nihil

700 W Auxiliaries

400 W AuxiliariesFC

[l/100km] Conventional 48V

-5%

-14%

Vehicle

Efficiency

-12%

+ e-Air

Compressor

+ e-Coolant

Pump

Results

Combination of measures can’t achieve 2025 CO2 targets

− Vehicle and powertrain efficiency measures

− Predictive powertrain controls

However

48V Mild hybrid system significantly reduces CO2 in more

transient cycle

− Modular mild hybrid system integration dependent on vehicle

application


IN COMBINATION WITH ENGINE DOWNSIZING UP TO 13 % REDUCTION IN FUEL CONSUMPTION

Source: FEV

ITES system integrates a secondary compressor, turbocompound or ORC

turbine, M/G unit over a planetary gear set

43

Cl

Br

Ring

Carrier

Sun

Compressor

M/G

Turbocompounding

/ORC Turbine

Engine

Ground

Band Brake

Dog Clutch















AGENDA

| 44


47,0

12,69,7

13,012,410,0 9,1 10,2

0

10

20

30

40

50

60

Fuel Consumption [gal/1000tonmile]

Extract of a Simulation Study for a Class 6-7 Urban Vocational Truck

~21% Lower Fuel Consumption in ARB transient cycle for a P2 HV Hybrid

45

1,9

7,2

9,3

6,97,0

8,89,6

8,5

0

2

4

6

8

10

12

Fuel Economy [MPGe]

Conventional

Hybrid

73,6

20,7

5,8

21,6

0

10

20

30

40

50

60

70

80

90

ARBCreep

ARBTransient

ARBCruise

HDUDDS

Fuel Consumption Reduction [%]

266,2

22,02,7

23,4

0

50

100

150

200

250

300

ARBCreep

ARBTransient

ARBCruise

HDUDDS

Fuel Economy Increase[%]















AGENDA

| 46


Dedicated diesel range extender engine for medium duty delivery trucks

Basic investigations for range extender operation

47

◼ Typical fleet daily trip share of a 12 ton vehicle in the

region of Aachen

◼ Reduction of battery range from 180 km → 100

km

− ~ 50 % of all trips could still be executed

using only battery

− ~ 38 % of the fleet mileage could be

performed with the electric vehicles

0

20

40

60

80

100

40 60 80 100 120 140 160 180

Sh

are

/ %

Battery range / km

Share of daytrips

Share of milage wo

REX

0

5

10

15

20

40-60 60-80 80-100 100-120 120-140 140-160 160-180

Sh

are

/ %

Daily trip range / km



Basic investigations for range extender operation

48

◼ Typical fleet daily trip share of a 12 ton vehicle in the

region of Aachen

◼ Reduction of battery range from 180 km → 100

km

− ~ 50 % of all trips could still be executed using

only battery

− ~38 % of the fleet mileage could be performed

with the electric vehicles

− With a range extender all trips could be

performed, and in total 80% of the fleet

mileage could be performed with electricity

from the grid

◼ Range extender increases vehicle range when

needed

− No risk of unplanned stops

− Vehicle can be used more flexible0

20

40

60

80

100

40 60 80 100 120 140 160 180

Sh

are

/ %

Battery range / km

Share of daytrips

Share of milage wo

REX

0

5

10

15

20

40-60 60-80 80-100 100-120 120-140 140-160 160-180

Sh

are

/ %

Daily trip range / km


RANGE EXTENDER SETUP AND REQUIREMENTS


49

◼ A typical battery electrical delivery truck with 12 ton max. weight and

150 KW power is analyzed

◼ 100% payload for all investigations

◼ Range extender is only used for recharging the battery, no requirements

for pure diesel electric operation

◼ Diesel passenger car engines are investigated with 1.0 / 1.5 / 2.0L

displacement

◼ Engine simplifications to reduce the engine cost

− Free float charger instead of VNT

− No external EGR

− Reduced max. rail pressure of 1600 bar

◼ Simplified EATS System

− DOC / SDPF (HC-Dozer and/or E-Heater)

**

*

REEV - 100km electric range

11. Downsized diesel 12.

Exhaust aftertreatment



Engine and EATS properties after layout process

50

7,9 11,815,7

29,3

44

58,6

42,1

63,8

84,2

0

20

40

60

80

100

1.0L 1.5L 2.0L

En

gin

e p

ow

er

/ kW

heat up best BSFC max power

240 240 238

210204

200

235230

225

180

190

200

210

220

230

240

250

1.0L 1.5L 2.0L

BSFC

/ g

/kw

h◼ Operation mode

− Heat up 1500rpm / 30% load

− Best BSFC 2000rpm / 100% load

− Max power: 3000rpm / 100% load

◼ Engine power in best BSFC operation is ~ 30% lower

than max power operation

◼ In heat up mode the combustion is adjusted to have

low engine out emissions and high exhaust enthalpy

◼ Fuel consumption in best BSFC operation is ~11%

lower than in the max power operation



Fleet fuel consumption

51

◼ Results for the predictive range extender control:

− Vehicle knows the trip energy in advance

− Starts to recharge the battery before the

minimum range of 30 km is reached

− Allowing smaller displacement engines as

range extender

◼ Dependency of the range extender power on the

fleet fuel consumption

− Not significant for battery capacities higher

than 100kwh

◼ Battery reduction from 200 → 100kwh

− Fleet fuel consumption still only 6 L/100kmFle

et F

uel

Con

sup

mti

on

/ l

/10

0k

m

0

2

4

6

8

10

12

14

16

18

20

BatteryCapacity / kWh

40 60 80 100 120 140 160 180 200

Rex

2.0 L

1.5 L 321 g/kWh

1.0 L 321 g/kWh

Fle

et C

O2 E

mis

sio

ns

/ g

/100

km

250

300

350

400

450

500

550

600

650


40 60 80 100 120 140 160 180 200

2.0 L 321 g/kWh

1.5 L 321 g/kWh

1.0 L 321 g/kWh

2.0 L 489 g/kWh

Pay

load

Spec

ific

CO

2 E

mis

sio

ns

/ g

/tk

m

92

94

96

98

100

102

104

106

108

110

112


40 60 80 100 120 140 160 180 200

REX

2.0 L

1.5 L

1.0 L


Hybrid technology

is essential to

achieve future

GHG legislation

Different levels of

hybridization will

be introduced for

the various MD

and HD

applications

▪ Besides CO2, various requirements such as payback period, cost/weight increase, etc.

must be considered for an optimized Hybrid powertrain

▪ 48 V Mild Hybrid systems can be implemented at comparably low cost in a current

engine architecture, offering fuel consumption benefits of ~3-5%

▪ Full Hybrid solutions can achieve higher fuel consumption, especially in highly

transient cycles

▪ For a Class 6-7 vocational truck a P2 Full Hybrid achieves fuel consumption reduction

of 21% with a 2 year payback

▪ ReX can increase the flexibility of MD Trucks for fleet operators with very low fuel

consumption

▪ ReX can increase the payload compare to pure BEVs

Key findings | 52















AGENDA

| 53


ANALYSIS OF

LEGISLATIVE

REQRUIREMENTS

AND MARKET

TRENDS

BRINGING INNOVATION TO THE STREET

Engineering Services at a Glance

54

HYBRID

POWERTRAIN

CONCEPT

OPTIMIZATION AND

SIMULATION

OPERATING

STRATEGY AND

ENERGY

MANAGEMENT

DIESEL HYBRID

POWERTRAIN

SERVICES

DEMONSTRATOR

SET-UP

COMPONENT

DEVELOPMENT

DIESEL HYBRID

POWERTRAIN

BENCHMARKING

POWERTRAIN

CALIBRATION

POWERTRAIN

INTEGRATION


▪ Set-up of technology demonstrator

▪ Identification of optimal Hybrid powertrain layouts for

different operating characteristica in the light

commercial vehicle segment

▪ Optimization of the operating strategy, e. g.: sailing,

optimized DPF regeneration

▪ Evaluation of CO2 and emission reduction potential

▪ Investigation of a cost optimized engine and EATS

system for the investigated hybrid concepts

▪ EU7/EUVII emission legislation

▪ Targets hybrid system:

− P02 hybrid topologiy

− Max. electric. vehicle speed >= 70 km/h

− Electric range 50 km

PHEV Demonstrator for Light Commercial Vehicle Application

PRESENTED AT BADEN-BADEN INTERNATIONAL ENGINE CONFERENCE FEB. 2020

MAIN TARGETS

55

Transmission

ClutchStarter

EM EM

EM

Clutch

ICE

BSG



Contacts

FUTURE POWERTRAIN CONCEPTS

FOR HEAVY DUTY APPLICATIONS

Q&A

Dr.-Ing.

Joschka SchaubDepartment Manager

Powertrain Concepts and Controls

Phone +49 (241) 5689 9435

Mobile +49 (173) 665 1733

[email protected]

Satyum JoshiSenior Manager

Commercial Engines

Phone +1 (248) 724-7917

[email protected]

| 56








road vehicles

◼ Content:







◼ Industry Partners

− Truck OEM’s

− Supply Base



June – July 2020


OBJECTIVE

https://fev-live.com/webinars

| 57


FUTURE TRUCK SERIES


POWERTRAINS

DR. JOSCHKA SCHAUB, FEV EUROPE GmbH

BRENDAN SHERRY, FEV INC.

JUNE 23RD 2020


Replace with

meaningful, high

resolution image

CUSTOMIZED CONTROL FUNCTIONS FOR

LOW CO2, LOW EMISSIONS & LOW COSTS















AGENDA

| 59















AGENDA

| 60


7. Virtual sensors

*: FEV Scenario / not finally fixed

Source: FEV

Controls Roadmap for Heavy-Duty Diesel Powertrains

61

1. Air management

2. Fuel injection /

combustion control

3. EATS control

4. Mode coordinators/

heating management

6. Hybrid / powertrain

control

NOx based EGR control, advanced

boost control, mean value models

Open-loop injection control,

open-loop CRS, model-

based fuel path

Rule-based combustion mode

coordinators, tailpipe emission control

Rule-based energy management,

engine-on sailing

Map-based models, simplified

physical models

Electric assisted airpath management, model-predictive

control, dynamic flow modeling

Active needle ctrl, CLCC individual feature (e.g. x50)

Optimization-based supervisory control

Predictive powertrain control based on digital maps and

telematics, optimization based energy management,

engine-off sailing and advanced start-stop

Data driven process models (e.g. NN, GP), advanced

physical models, “hybrid” models, cloud-based training

Full 1D airpath model

Predictive powertrain control by

car2x

Current technology

focus

Next generation

technology focus

Future

technology focusFE FE/CO2 regulation

20352020 20302025‘19 ‘21 ‘22 ‘23 ‘24 ‘26 ‘27 ‘28 ‘29 ‘31 ‘32 ‘33 ‘34

Full 1D airpath model, Cloud-

based model execution

Predictive supervisory control, advanced

models instead of different combustion

modes

CLCC: Closed-Loop Combustion Control, CRS: Combustion rate shaping, EATS: Exhaust aftertreatment system, , TD: Twin Dosing, CBM: Condition-based maintenance

8. Monitoring / diagnostics

Model-based controls Predictive control/CloudSelf learning controls (AI)

Euro VI Post Euro VI* “Ultra-Low NOx” *

EPA ´10 Post EPA ´10 “Ultra-Low NOx” *

FEFE

FE FE FE

Closed loop CRS (digital/continuous), Spark-

assisted combustion for certain fuels

Model-based controls, slice-based

EATS modelsTD EATS coordinator, closed-loop NH3 control

for SCR, kinetics based multi-brick EATS models

3D EATS models, multi-layer EATS models +

control

Learning functions for emission

robustness, eff. monitoring EATS, service

pred. models (e.g. oil dil./det.)

On-board monitoring (NOx), advanced pin-pointing

algorithms, condition-based maintenance

Online identification/adaption of

fuel, on-board mon. (other species)

Enabling TechnologiesExhaust Pressure sensor, Injector needle

position sensor, Turbospeed sensor; Multi-

core ECUs

Cyl. Pressure sensor, Fuel quality sensor,

Connected ECU with max computational

feature (larger RAM, FPGA)

Camera, Radar/Lidar, Connected ECU with

backend infrastructure (cloud computing)















AGENDA

| 62


7. Virtual sensors


Source: FEV


63

1. Air management

2. Fuel injection /

combustion control

3. EATS control


heating management


control

NOx based EGR control, advanced

boost control, mean value models



based fuel path




engine-on sailing


physical models












car2x

Current technology

focus

Next generation

technology focus

Future


20352020 20302025‘19 ‘21 ‘22 ‘23 ‘24 ‘26 ‘27 ‘28 ‘29 ‘31 ‘32 ‘33 ‘34





modes






FEFE

FE FE FE







control










core ECUs







▪ Nox Based EGR Control with NOx smoke

limitation

▪ Mean value model based on physical and

thermodynamic principles, e.g.:

− Conservation of mass and energy

− Generic flow equations

▪ Reduced number of sensors through model based

approach

▪ Automatic correction of different ambient

conditions

▪ Compensation of transient control deviations by

faster control loops

▪ NOx Based EGR Control facilitates introduction of

NOx tailpipe control and setpoint optimization

FEV air path control concept, a gain in accuracy and system robustness at

reduced system costs

MODEL-BASED EGR CONTROL

DESCRIPTION

64

O2 intake

limitation

(smoke)

NOx Model NOx Sensor

Adaption

Set point

conversion/

limitation

Inverted

NOx Model

Internal EGR

Control

HP EGR

Control

External/Internal

EGR Split

mCylinder

λmin

Quantity

φ𝑂2,Cyl,lim φ𝑁𝑂𝑥,Sensor

φ𝑂2,Cyl,des

φ𝑂2,Int,des

φ𝑂2,Int

φ𝑁𝑂𝑥,des

Exhaust

CamPosition

NOx Engine

Out Target

HP EGR Cooler

Bypass Valve

HP EGR Valve

Position


FEV’s model based control strategy for EGR and boosting systems

compensates dynamic and ambient impacts on emissions

MODEL BASED AIR PATH COMPENSATES IMPACT OF AMBIENT

CONDITIONS W/O CALIBRATION OF AMBIENT CORRECTIONS

MODEL BASED AIR PATH IMPROVES NOX/PM TRADEOFF IN

TRANSIENT CYCLES

65

En

gin

e-o

ut

PM

/ (

g/k

m)

Engine-out NOx / (g/km)

with model based air path control

with conventional air-mass based

strategy

+ 5%

- 15%

22 °

C

-7°C

833 m

bar

Model-based air path control

Conventional control strategy

En

gin

e-o

ut

PM

/ (

g/k

m)

Engine-out NOx / (g/km)

0.01

0.02


SENSITIVITY INVESTIGATIONS BASED ON WHTC TESTS

FEV’s model based control strategy reduces emission dispersion resulting in

fuel consumption and emission benefits

661

NOx [g/kWh]

PM

[g

/kW

h]

0.2

0.0

2

NOx [g/kWh]

BS

FC

[g

/kW

h]

0.2

Air mass based control concept

FEV model based air path control concept

HFM sensor tolerance ± 5%


7. Virtual sensors


Source: FEV


67

1. Air management

2. Fuel injection /

combustion control

3. EATS control


heating management


control

NOx based EGR control, advanced boost

control, mean value models



based fuel path




engine-on sailing


physical models












car2x

Current technology

focus

Next generation

technology focus

Future


20352020 20302025‘19 ‘21 ‘22 ‘23 ‘24 ‘26 ‘27 ‘28 ‘29 ‘31 ‘32 ‘33 ‘34





modes






FEFE

FE FE FE







control










core ECUs







▪ Achieve fast emission reduction in

all conditions

▪ Combustion modes with different

target for NOx, P2 and T4

▪ Combustion modes are triggered

based on EATS status (e.g. EATS

efficiencies)

▪ Complementary change of target

values, e.g. NOx, within a

combustion mode

State-of-the-art combustion mode coordinators

Combustion modes are triggered based on EATS state parameters

CONCEPT 0 – OPEN LOOP

SUMMARY

68

Tailpipe

emissions

EATS

Combustion Mode

CoordinatorNOx

NH3 target load

…

EATS temperatures, EATS efficiency, …

T4

P2Combustion

Modes

Engine


▪ Tracking emissions in a moving

window

▪ Continuous adaptation of target

values based on closed-loop

emission controller

▪ Coverage of system and

component tolerances / aging

>> Significant reduction in emission

dispersion and engineering margins

Significant reduction in emission dispersion and engineering margin via

closed loop emission controller

CONCEPT 1 – CLOSED LOOP TP EMISSION CONTROL

SUMMARY

69

Tailpipe

emissions

EATS

Closed-loop TP

Emission ControlNOx

NH3 target load

…


Emissions

targets

-

Em

issi

on

s /

g/k

m o

r

g/k

Wh

Time

x km

x kWh x

g CO2

Tracking emissions in a moving window

T4

P2

Engine

Combustion

Modes +

Emission

Controller


7. Virtual sensors


Source: FEV


70

1. Air management

2. Fuel injection /

combustion control

3. EATS control


heating management


control





based fuel path




engine-on sailing


physical models




Optimization-based supervisory

control








car2x

Current technology

focus

Next generation

technology focus

Future


20352020 20302025‘19 ‘21 ‘22 ‘23 ‘24 ‘26 ‘27 ‘28 ‘29 ‘31 ‘32 ‘33 ‘34





modes






FEFE

FE FE FE







control










core ECUs







▪ Optimization of current setpoints

while fulfilling emissions targets

− Offline optimization (short term)

− Online optimization (mid term)

▪ Correction of optimization target

based on tracked TP emissions

▪ Reduced emissions during critical

cycles and reduced CO2 emissions

in standard cycles

Optimization based supervisory control reduces emissions during critical

cycles and reduces CO2 in standard cycles

CONCEPT 2 – CLOSED LOOP TP EMISSION CONTROL + SETPOINT OPTIMIZATION

SUMMARY

71

Tailpipe

emissions

EATS

NOx

NH3 target load

…


Emissions

targets

-

Em

issi

on

s /

g/k

m o

r

g/k

Wh

Time

x km

x kWh x

g CO2


T4

P2

Engine

Current Setpoint

Optimization

Co

st f

un

ctio

n


OPTIMIZATION OF THE OPERATIONAL COST FOR ENGINE AND ADDITIONAL EATS SYSTEM OF A LCV

Optimization based supervisory control

Investigation of control concept in WHTC for a MD application

72

Sim. Conventional Sim. Optimal BSFC Sim. NOx&Costs Ctrl

ME /

Nm

-5000

5001000

t / s

0 200 400 600 800 1000 1200 1400 1600 1800 2000

nE /

rp

m

0100020003000

GF

/ (

kg

/h)

05

1015202530

mF

ue

l /

(kg

)

0.00.51.01.52.02.53.0

dm

Ad

Blu

e /

(m

g/s

)

0100

200300

400500600

mA

dB

lue

l /

g

0

20

40

60

80

100 Sim. Conventional Sim. Optimal BSFC Sim. NOx&Costs Ctrl

Exh

NO

X

Ta

ilpip

e /

(g

/s)

0.000.010.020.030.040.050.06

ME /

Nm

-5000

5001000

t / s

0 200 400 600 800 1000 1200 1400 1600 1800 2000

nE /

rp

m

0100020003000

BS

NO

X

Ta

ilpip

e /

(g

/kW

h)

0.00

0.10

0.20

0.30

0.40

0.50

Exh

NO

X

En

gO

ut

/ (g

/s)

0.000.050.100.150.200.25

BS

NO

X

En

gO

ut

/ (g

/kW

h)

0.01.02.03.04.05.06.0

e / g

0.000.300.60

Torq

ue

[Nm

]

Sp

eed

[rp

m]

Fu

el m

ass

flo

w [

kg

/h]

Cu

mu

late

d

fuel [k

g]

◼ Up to 2% reduction in fluids cost with >20% reduction of tailpipe NOx for hot WHTC

◼ Up to 4% reduction in fluids cost in WHTC without NOx ever exceeding emission target limit

Exh

au

st N

OX

Tailp

ipe [

g/s

]

BSN

OX

Tailp

ipe

[g/k

Wh

]

95100

Fuel / l

97116

AdBlue / l

100165

Conv

Opt BSFC

NOx Ctrl Total Costs

Fuel + AdBlue

€

100 95 98

-4,7% -2,4%

166

BSNOx EngOut

/ (g/kWh)

100 116

BSNOx Tailpipe

/ (g/kWh)

10068

160

WHTC cycle

relative figures


7. Virtual sensors


Source: FEV


73

1. Air management

2. Fuel injection /

combustion control

3. EATS control


heating management


control





based fuel path




engine-on sailing


physical models












car2x

Current technology

focus

Next generation

technology focus

Future


20352020 20302025‘19 ‘21 ‘22 ‘23 ‘24 ‘26 ‘27 ‘28 ‘29 ‘31 ‘32 ‘33 ‘34





modes






FEFE

FE FE FE







control










core ECUs







▪ Optimization of setpoints on

prediction horizon

▪ Correction of optimization target

based on tracked TP emissions

▪ Further reduced emissions during

critical cycles and reduced CO2

emissions in standard cycles

Predictive optimization

CONCEPT 2 – CLOSED LOOP TP EMISSION CONTROL + PREDICTIVE SETPOINT OPTIMIZATION

SUMMARY

74

Tailpipe

emissions

EATS

NOx

NH3 target load

…


Emissions

targets

-

Em

issio

ns /

g/k

m o

r g/k

Wh

Time

x km

x kWh

x g CO2


T4

P2

Engine

Predictive Setpoint

Optimization

Cost fu

nctio

nInformation of

predicted horizon

(speed, load, …)


(1) V2G Booking Assistance

Combination of cheapest fuel and e-

fillings stations on a fixed route

(2) ACC and Lane change Assistance

Maximum use of 40 tons kinetic

energy, with +/- 5 km/h speed

tolerance

(3) Gearshift & ICE on/off Assistance

Short horizon for comfortable

change the driving dynamical state

(4) Predictive EATS Assistance

Optimized engine and EATS control

over prediction horizon

Efficient vehicle usage by integrating connected data

can achieve significant CO2 reductions – up to 7 % in real world cycles

SUMMARY

75















AGENDA

| 76


WORK SCOPE RANGE

FEV offers a wide range of services for function and SW development

77

Development of production intent

software algorithms

Software Development

within OEM processes

SW Standardization

Projects

Development of new control concepts

Function Library

project A project B

DiCoRS

Controller

Actual Cylinder Pressure


Torque & Fuel Control✓ physics-based consideration

of all relevant combustion modes for torque-neutral transitions and most efficient engine operation even in heating- or Lambda-1 mode

✓ advanced closed-loop combustion control

FEV has a broad function database covering all relevant areas of Diesel

powertrain control

78

EGR & Boost Control✓ emission-based control

approach for robust engine-out emission in all transient and environmental conditions

On-Board Diagnostic and

Monitoring✓ advanced monitors for

engine and EATS subsystems

✓ New concepts for OBM

Exhaust Aftertreatment Control✓ enabled by chemics-oriented

EATS component models: you will always predict the correct operation strategy to keep the tailpipe sufficiently clean

Hybrid

Control

Diesel

Powertrain

Control

Mild – Full Hybrid Control✓ optimized powertrain operation

strategy as the key to a perfect symbiosis between combustion and electrification


FUNCTIONS AND BEYOND

Your Function Development Partner Worldwide

79

Calibration Guidelines Trainings / Workshops

Calibration Support

◼ General guidelines

◼ Application specific Guidelines

◼ Customer specific adjustment of

guidelines

◼ Guidelines for customer specific SW Services+

for ECU

Software

◼ Worldwide support locally for engine,

aftertreatment, powertrain & vehicles

◼ Calibration at customer testing sites

◼ Calibration at FEV local facilities

◼ Software Workshops

− Onsite & Offsite

◼ Trainings on test bench / vehicles

− conducted worldwide with support

by FEV subsidiaries

◼ AES & BES documentation

◼ OBD documentation

◼ Support for discussion with authorities

Documentation for Authorities



Contacts

CUSTOMIZED CONTROL FUNCTIONS FOR

LOW CO2, LOW EMISSIONS & LOW COSTS

Q&A

Dr.-Ing.

Joschka SchaubDepartment Manager

Powertrain Concepts and

Controls

Phone +49 (241) 5689 9435

Mobile +49 (173) 665 1733

[email protected]

Brendan SherryManager

Diesel Engine Controls, OBD

and Vehicle Application

Phone +1 (248) 724-7537

Mobile +1 (248) 804-3029

[email protected]

| 80








road vehicles

◼ Content:







◼ Industry Partners

− Truck OEM’s

− Supply Base



June – July 2020


OBJECTIVE

https://fev-live.com/webinars

| 81


FUTURE TRUCK SERIES


POWERTRAINS

FLORIAN SCHU, FEV GROUP GmbH

JUNE 23RD 2020


Replace with

meaningful, high

resolution image

BENEFITS OF VIRTUAL POWERTRAIN

CALIBRATION FOR HD ON-ROAD VEHICLES







AGENDA

| 83







AGENDA

| 84


MAIN CHALLENGES OF FUTURE POWERTRAIN DEVELOPMENT

Source: FEV

Introduction & Challenges

What are the main problems faced in the development?

| 85

Cost

Time

Accuracy

Effort

◼ Challenging test matrix of

variants

◼ Limited number of prototypes

◼ High demand for reducing calibration

effort to get cost efficient products

◼ Complex dependencies and

interactions between various

domains

◼ Increasing system complexity

of hardware and software

◼ Hardware/Software

changes during calibration ◼ High-priced testing effort

◼ Increased cost and effort for

seamless validation


▪ Smart Testing: Reduced number of

real tests

▪ Reduced number of protoypes

▪ Quality Increase

▪ Early Predictions

▪ Time Savings

▪ Right size of technology packages

Cost and time improvements through virtualization

INTEGRATION OF SIMULATION PRINCIPLE INTO DEVELOPMENT

BENEFITS OF VIRTUALIZATION

Source: FEV | 86

Develo

pm

en

t C

ost

Powertrain complexity

Cost increase

due to higher

resources and

manpower

Cost

reduction by

virtualization

*Schematic representation


◼ Usage of best-practice simulation models for

vehicle and engine hardware and development

process

◼ Connection of virtual powertrain with real control

functions of the vehicle/engine

Source: FEV

Cost and time improvements through virtualization

FEV`s Idea of virtual calibration solution

| 87

Idea of FEV virtual calibration solution

Fast and efficient way to simulate multiple variants,

Increasing customer requirements and various type of usage

Simulation

Plant Models

Control Function

xCU(e.g. ECU, TCU)


´

´Source: FEV

Tension area of the engineering interaction for virtual calibration

| 88

Engine/Vehicle

Calibration

Team

HiL Team

Knowhow from

calibration field

◼ Combustion

◼ Aftertreatment

◼ OBD

Knowhow from

Embedded systems

◼ SW testing

◼ Real-time

modeling

◼ Embedded system

Virtual Calibration Team

◼ Interaction between the different

fields

◼ Real-time simulation of virtual full-

powertrain

◼ Setting up and operation of virtual

calibration platforms

◼ Development of virtual calibration

processes

Hardware

Development

Team

Knowhow from hardware

development

◼ Powertrain HW & SW

development

◼ Simulation knowhow

Virtual

Calibration

Team


VIRTUAL CALIBRATION THROUGHOUT PRODUCT LIFECYCLE

Source: FEV

Vehicle Calibration

Virtual calibration in use from concept phase to end of vehicle life

| 89

◼ Validation

◼ Quality check

◼ Emission robustness

◼ OBD & OBM

robustness

◼ Virtual PEMS testing

◼ System robustness

for conformity

Concept Definition Serial CalibrationValidation / Fleet

testingAfter Market / Lifecycle

◼ Field issues

◼ SW updates

◼ ISC validation

◼ Analyze profiles for

customized applications

◼ Emission calibration

◼ Governor calibration

◼ Engine full load

◼ Environmental

corrections

◼ Aftertreatment system

calibration

◼ OBD calibration

◼ Transmission / Hybrid

calibration

◼ Thermal management

◼ HW/SW Layout

◼ SW

Commissioning /

Release

◼ Base engine and

model calibration

◼ Demonstrator

development







AGENDA

| 90


´Source: FEV

FEV Mean value simulation approach

Overview

| 91

Benefits:

◼ Highly adaptable to different HW layouts

◼ Reduced calibration efforts

◼ High flexible models, customizing possible

◼ Real time capable (or faster)

◼ High accuracy after calibration

◼ Validated benchmark models for sub models

available

◼ Optimized for HiL, MiL and EiL use

EATSVehicle Engine

Benefits:

◼ Sub models for all sub systems in different modelling

depths available (modular setup)

◼ Physical / data driven / semi physical models

◼ Mean value based approach

◼ FEV owns source code for all models

◼ Models are always up to date

◼ Realized in MATLAB/Simulink, sub models from GT-

Suite or AMESim could be used via FEV xMOD

FEV Mean Value

plant models

Driver Transmission Environment


THE SELECTION WILL DEPEND UPON SEVERAL FACTORS

´Source: FEV

FEV Mean value simulation approach

Different model approaches are available for the engine model

| 92

Maps Based Approach Physics Based Approach

Mathematical Models (DoE) FEV approach

◼ A combination of all approaches gives best results,

e.g. Air path with mean value approach & cylinder modeling

with global DoE

◼ The selection of the model will depend upon:

− Level of accuracy required

− Available data

− Project Objectives

− Available Time

Eff

ective M

ean P

ressure

/ b

ar

0

5

10

15

20

25

Engine Speed / rpm

1000 2000 3000 4000

Engine Speed / rpm

1000 2000 3000 4000

Eff

ective M

ean P

ressure

/ b

ar

0

5

10

15

20

25

Engine Speed / rpm

1000 2000 3000 4000

Engine Speed / rpm

1000 2000 3000 4000

◼ For complete engine modelling

only with limited accuracy

◼ Not able to extrapolate

◼ For sub components useful

◼ For complete engine

modelling only with limited

accuracy, high number of training

points required

◼ Not able to extrapolate

◼ For sub components useful,

especially for highly not linear

behavior (emission formation)

◼ Mean value model

approach

◼ 0d approach (GT-Power

models)

◼ High accuracy for

transient operation

◼ Capable for extrapolation







AGENDA

| 93


HARDWARE-IN-THE-LOOP TEST BED AND ITS COMPONENTS

´Source: FEV

Hardware-in-the-Loop Test Bench

Connection of powertrain models to real hardware xCU

| 94

1

2

3

4

HiL test bed for turn-key calibration projects

◼ ECU, components & harness integration (1), HiL simulator (2), host pc (3) and HiL calibration

desktop (remote) (4)

◼ Some VCAP features:

− MORPHEE automation system used at VCAP as on the test bench

− MORPHEE PC embeds the powertrain models

− ECU and hardware cabinet allows easy and fast switch from one configuration to another

one

− Fast ECU connection between MORPHEE and customer ECU

− Connection in INCA via ASAP/MCD3







AGENDA

| 95


◼ Boundary conditions:

− RDE95 (warm engine start)

− Altitude: 0m

− Ambient temperature: 0°C

◼ Comments:

− Good correlation between the

vehicle simulation and the

measurement

− Typical target for tailpipe /

Engine Out NOx deviation at

the end of the cycle < 10%

Virtual calibration using HiL test beds

Example of light commercial vehicles and heavy duty applications (1/4)

NO

x T

P /

-N

Ox

EO

/ -

NO

x E

O

mas

s / -

HiL (Virtual vehicle) Chassis dyno (vehicle)

NO

x T

P

mas

s / -

Veh

icle

spee

d / k

m/h

04080

120

Time / s

0 500 1000 1500 2000 2500

Tem

pera

ture

aDP

F /

-

TRANSIENT VALIDATION OF HIL TEST BED / NOMINAL CONDITION

REMARKS

´Source: FEV | 96



− Synthetically generated RDE

cycle (warm engine start)

− Altitude: 1300m


◼ Comments:

− Good correlation between the

vehicle simulation and the

measurement

− Typical target for tailpipe /

Engine Out NOx deviation at

the end of the cycle < 10%



NO

x T

P

mas

s / -

Veh

icle

spee

d / k

m/h

04080

120160

Time / s

0 1000 2000 3000 4000 5000 6000 7000

Tem

pera

ture

aDP

F /

-

NO

x E

O

mas

s / -

NO

x E

O /

- HiL (Virtual vehicle) Chassis dyno (vehicle)

NO

x T

P /

-

TRANSIENT VALIDATION OF HIL TEST BED / EXTENDED CONDITION

REMARKS

´Source: FEV | 97


◼ Measurement in black and

simulation in red


− WHTC (warm engine

− Altitude: 0m


◼ Comments:

− HiL system for an EU VI HD

application (~7L, HP EGR, VNT

TC)

− Target use case: Virtual PEMs

for validation purpose



TRANSIENT VALIDATION OF HIL TEST BED / NOMINAL CONDITION

REMARKS

´Source: FEV | 98

Model Name: FrameEngineModelFlat_Cal_v32 Titel: WHTC wEGR HiL Simulation Engine: DDi75

Last Save Date: 02.09.2018 Subtitle: TB vs HiL Results ECU Version Playb.: V200

Test-Type: WHTC Description: VSR003_WHTC_hot ECU Version TB: V200

NO

x em

issi

on [p

pm]

0

200

400

600

800

1000

1200

1400

000000_20180806WHTC_F00001_DURAREC_20180806_resampled_EATS_hot2.csv

VSR003_WHTC_VCAP_2018-08-31_20-07-10.csv

31-08-2018 20_38_52_VSR003_VTG_EGR_CL_INCA001.dat

Eng

ine

Spe

ed [r

pm]

5001000150020002500

TestTime

-200 0 200 400 600 800 1000 1200 1400 1600 1800 2000

Fue

l Qua

ntity

[mg/

str/

cyl]

050100150200

Model Name: FrameEngineModelFlat_Cal_v32 Titel: WHTC wEGR HiL Simulation Engine: DDi75

Last Save Date: 07.09.2018 Subtitle: TB vs HiL Results ECU Version Playb.: V200

Test-Type: WHTC Description: VSR003_WHTC_hot ECU Version TB: V200

NO

x em

issi

on [g

]

0.00.20.40.60.81.01.21.41.6

000000_20180806WHTC_F00001_DURAREC_20180806_resampled_EATS_hot2.csv

VSR003_WHTC_VCAP_2018-09-07_16-56-12.csv

07-09-2018 17_27_58_VSR003_VTG_EGR_CL_INCA001.datE

ngin

e S

peed

[rpm

]

5001000150020002500

TestTime

-200 0 200 400 600 800 1000 1200 1400 1600 1800 2000

Fue

l Qua

ntity

[mg/

str/

cyl]

050100150200

NO

x T

P [g

]


◼ Aspects to be considered before setting a

target

− Targeted use cases

− Highest accuracy not always mandatory (!)

− Measurement errors

− Physical modeling limits

◼ Validation procedure includes

− Steady-state engine mapping

− Transient cycles

◼ The final model accuracy is developed during

the project with customers, depending upon

the final requirements for the targeted use

cases and the models are being updated to

maintain the latest maturity level



VALIDATION OF HIL TEST BED / TYPICAL MODEL ACCURACY TARGET

REMARKS

´Source: FEV | 99

(for the whole set of operating points)


▪ HiL test bed allows virtual testing in:

− Different vehicle configurations

− Different Environmental conditions

− Different test profile ( speed / load /

drive style)

− Automated process of test and also

evaluation

▪ Vehicle based test condition generation

▪ Identification of critical conditions

▪ Efficient and effective test plan on real

road condition


Virtualization of PEMS testing

VIRTUAL VEHICLE / PEMS TEST

VIRTUAL PEMS TESTING

´Source: FEV | 100

Cycle A Cycle Z

Vehicle Configuration Road Condition Environment Condition

Cycle K


▪ To validate an EU 6d calibration

300 virtual PEMS test are

performed

▪ Extended conditions apply if

− Ambient temperature below 0°C or

above 30°C

− Altitude over 700m

▪ ~ 70 virtual PEMS per week (~2

hours per test)

▪ 300 virtual PEMS test are more

cost and time efficient than real

tests


Validation / Fleet testing PEMS validation campaign

VCAP – TOTAL TP-NOX-EMISSIONS

REMARKS

´Source: FEV | 101

Tests with altitude < 700m

Extended altitude NOx limit with CF

NOx

limit


FEV Solution for XiL calibration environment

FEV Global HiL resources and competences

102

FEV Germany

◼ Plant model providing and

development

◼ Use case development

◼ Support / Training

◼ Steering

◼ Setting up of HiL systems

◼ Performing of virtual

calibration projects

FEV France / STS

Setting up and providing of VCAP

systems

Performing HiL cal. projects

FEV China

2 VCAP systems in Operation

Setting up of HiL systems

Performing HiL cal. projects

FEV India

Plant model calibration

HiL System

FEV Romania

Plant model library

FEV Italy

Plant model calibration

FEV Japan

VCAP system in operation

FEV USA

Simulation & Model calibration


Source: FEV

FEV Engineering for Virtual Calibration and Validation

Engineering overview

| 103

CUSTOMIZING

CUSTOMER

PLATFORMS

PLATFORMS FOR

TURNKEY

CALIBRATION

REAL TIME

POWERTRAIN

MODELING

VIRTUAL

CALIBRATION

DEVELOPMENT

PLANT MODEL

CALIBRATION

CALIBRATION

ROBUSTNESS

EVALUATION

TECHNICAL

CONSULTING

VIRTUAL SERIAL

CALIBRATION

VALIDATION

SUPPORT

INNOVATIONS BRIDGING

CALIBRATION & SIMULATION


Virtual calibration

Is the key

technology future

powertrain

development

Virtual calibration

combines various

engineering

fields…

▪ Legal boundaries and complexity of future technology packages force the

development of new processes and tools

▪ Virtual calibration is the important component for future powertrain development and

calibration

▪ The earlier virtual calibration is introduced into the development process the more

effective it can get and helps you to reduce cost and improve quality

▪ Various variants can be easily simulated with a right sized virtual approach

▪ Early identification of weaknesses in HW, SW and calibration helps to right size the

technology packages for the respective use

▪ Best-practice modeling and efficient HiL systems are continuously developed and

improved to meet today and future requirements

▪ FEV`S virtual calibration process combines the best of decades of conventional

powertrain development with latest simulation findings

Key findings | 104

© by FEV – all rights reserved. Confidential – no passing on to third parties |Footnote 105



Contact

Department Manager Diesel Powertrains

Phone +49 241 5689 9974

Mobile +49 160 7463696

[email protected]

Florian Schu

Q&ADipl.-Ing.

BENEFITS OF VIRTUAL POWERTRAIN

CALIBRATION FOR

HD ON-ROAD VEHICLES

| 106



Contact

Vice president Commercial Engines

Phone +49 241 5689 5250

Mobile +49 151 43127283

[email protected]

Dipl.-Ing., MBA

Dieter van der Put

FEV FUTURE TRUCK SERIES

SESSION LEADER

| 107


1st Session June 9th GHG – Trends and Legislation

2nd Session June 16th Low NOx – Trends and Legislation


4th Session June 30th Hydrogen Powered Future

5th Session July 2nd Focus Electrification

▪ Content:








− Truck OEM’s

− Supply Base



June – July 2020


OBJECTIVE



1

0

9

FEV IS READY TO SUPPORT YOUR DEVELOPMENT!

NO MATTER WHO WILL WIN THE RACE FOR FUTURE LONG HAUL TRANSPORT.

Documents

© by FEV