System Simulation in the Development of Rolling Stock

Application Examples of System Simulation

in the Development of Rolling Stock

Karsten Todtermuschke

Senior Engineer, ITI GmbH

Prof. Dr.-Ing. Michael Beitelschmidt

TU Dresden

© ITI GmbH itisim.com

Agenda

• ITI company profile

• Applied simulation examples:

• Analysis of a locomotive driveline with traction control

• Self-excited torsional vibration of wheel sets

• Energy consumption of hybrid powertrains

2


ITI Gesellschaft für ingenieurtechnische

Informationsverarbeitung mbH

• Leading in the field of system

simulation

• ITI headquarters located at the

heart of Dresden, Germany

ITI Company Profile 3


Core Business 2014 & Team

• Customer oriented software

and services for virtual

product development

• Development and distribution of

simulation software

• Competent and innovative

engineering, programming and

R&D services

4 ITI Company Profile

40% Germany

40 %

Services

Software & Maintenance

40% Germany

40 %

Services

Software & Maintenance

Software Development + IT

Engineering

Sales Marketing

23

22

20


Industrial Customers

References 5

AUTOMOTIVE Audi, BMW, Daimler, Honda, Hitachi AMS,

Mazda, Mitsubishi Motors, Schaeffler, VW, ZF

INDUSTRIAL MACHINERY Ferromatik Milacron, Sumitomo

Demag, Husky, Schuler, ThyssenKrupp

ENERGY ABB, Aggreko, GE Jenbacher,

Kanto Seiki, Siemens, Toshiba, Veolia

RAILWAYS Alstom, Bombardier,

Siemens Transportation

E/E & DEVICES Alps Electric, Canon, Johnson Electric,

Mitsubishi Electric, NEC, Nikon, Ricoh, Sony

OIL & GAS Aker Solutions, Baker Hughes, Cameron,

FMC, National Oilwell Varco

HEAVY MACHINERY & CRANES Cargotec, Foton, Hitachi Construction,

Kirow Ardelt, Kranbau Köthen, Liebherr,

MARINE ENGINEERING Bureau Veritas, DNV GL,

Mitsui E&S, Stromag, Vulkan

MINING & MATERIAL HANDLING ABB, Herrenknecht, Komatsu Mining,

Romonta, Tenova TAKRAF

AEROSPACE & DEFENSE CASIC, CAST, EADS,

ESW, IMA, KMW, WTD 5

MEDICAL TECHNOLOGY LMT, Schaerer Medical, Toshiba

SMART HOME SYSTEMS Hager, Honda Research Institute Europe,

RWTH Aachen, TU München


Analysis of a locomotive driveline with traction control -

Problem statement

• vertical load transmitted via primary and secondary suspension

• traction transmitted via traction bodies and traction bars

• traction limited via friction between wheel and rail

• traction control necessary to avoid slip

6 locomotive driveline with traction control

f endstop,,xF x

y

zfsec,,z

F

f axle,,zF

r axle,,zF

r mount,T

f mount,T

f sec,,xF

r sec,,xF

r endstop,,xF

r axle,,xF

r bar, ,xF

f axle,,xF

f bar,,xF

r sec,,zF


Problem statement

• mounting torque has influence on vertical axle loads

• non-symmetrical axle loads

• lower adhesion at front axles

direction of train


x

y

zfsec,,z

F

f axle,,zF

r axle,,zF

r mount,T

f mount,T

f sec,,xF

r sec,,xF

r axle,,xF

r bar, ,xF

f axle,,xF

f bar,,xF

r sec,,zF


Problem statement

• two identical bogies

• each axle driven by an asynchronous motor

• primary suspension between axle and bogie frame

• secondary suspension between bogie frame and locomotive

bogies traction bars

traction bodies suspensions axis and

wheel

case

x

y

z

O



Modeling concept – lumped (network) elements

• Definition of potential and flow quantities for each physical domain

• Models consist of elements and connections

• Connections calculate potential quantities

and define conservation

equations

(e.g. ΣF = 0

for mechanical nodes)

• Elements define relations between

flow und potential variables within elements

(e.g. F = k * Δx in a mechanical spring model)

element

FF

element

FF

element

FF 0F

Node

...,x,x

element

FF

element

FF

element

FF 0F

Node

...,x,x 𝐹𝑛𝑛

= 0

𝒙 𝒌 = 𝒗𝒌

𝒗 𝒌 = 𝒂𝒌

∆𝑣𝑖

−𝐹𝑖

∆𝑣𝑖+1

−𝐹𝑖+1 −𝐹𝑖−1

∆𝑣𝑖−1

𝑘

𝐹𝑖−1

𝐹𝑖

𝐹𝑖+1

node

9 lumped system circuit modeling


1D torsional model of powertrain

• Powertrain model includes:

• torsional behavior of the driveline

• adhesion of the wheel-rail contact

• adhesion control and its speed sensing



Adhesion control

• Powertrain model includes:

• torsional behavior of the driveline

• adhesion of the wheel-rail contact

• adhesion control and its speed sensing



Half model of the bogie suspension


• multi-body model (3D) of the locomotive’s suspension system

• chassis

• bogie frame

• suspension system in three dimensional space


Suspension model



Simulations

• Analyze the start-up behavior of a locomotive

• train of 1600 t

• 16 ‰ slope

• Sensitivity to parameters

• stiffness of powertrain

• sensitivity of traction control to the jerk

• the distribution of the 4 motor torques



Self-excited torsional vibration of wheelsets

• Phenomenon: left and right wheel of the wheel set oscillate against each

other

• Risk of overstress in press fit due to high dynamic torque in the driveshaft

• Potential problem of electrically driven locomotives with adhesion control

• Adhesion controls of modern electric locomotives try to recognize torsional

vibrations of the powertrain

17 torsional vibration of wheelsets


Presentations at the Rad & Schiene Conference 2014

Presentations at the Rad & Schiene Conference 2014

• Dipl.-Ing. (FH) Richard Schneider, Vice President R&D, Bombardier

Transportation, Winterthur

“’Rollierschwingungen’ – Ein neuer, integrierter und systematischer

Ansatz”

• Dr. Lütkepohl (Alstom) & Dr. Jenne (Gutehoffnungshütte Radsatz

GmbH)

“Radsatz - Ergänzende Nachweise zur Zulassung und

Kundenabnahme”

3 weeks of measurements for one certification of a locomotive

• Dr. Werner Breuer (Siemens AG) u.a.

“Auf der Suche nach dem maximalen dynamischen

Wellentorsionsmoment”



Problem statement

• Adhesion coefficient between

wheel and rail depends (among

other quantities) on slip.

• The dependency curve of slip

may have a negative slope which

corresponds with a negative

damping coefficient

• If the resulting damping

coefficient (including slip and

mechanical damping in the

system) is negative, steady-state

torsional vibrations between the

wheels occur.



Problem statement

• Without adhesion control, there

would not be an operating point

for slip with negative slope. Slip

would increase and the operating

point would move to a range with

positive slope for the friction

coefficient.



Problem statement

• The optimum operating point in

the range of micro slip is hard to

predict or even maintain, because

friction depends on many

quantities and may change rapidly

especially with wheel position and

speed.



System simulation potential

• Model for comprehensive

simulation approach must include

at least: • adhesion control

• powertrain (with torsional model of

wheelset)

• wheel-rail contact model including

adhesion

• optional model for press fit

system simulation

• Possible simulation tasks: • calculating maximum dynamic torque in

powertrain

• calculating a possible torsion of the

press fit

• testing algorithm and measurement of

torsional virbration detection (where

sensors should be located)



Introduction

23

Institute of Solid Mechanics

Chair of Nonlinear

Solid Mechanics

Chair of Dynamics and

Mechanism Design

Chair of Mechanics of

Multifunctional Structures

Workgroup Experimental

Mechanics and

Structural Durability


Introduction

24

Chair of Dynamics and

Mechanism Design

Chair of Dynamics and Mechanism Design

Mission: Leading edge research, service and

education in engineering technical dynamics

Vehicle Dynamics

Multi Body Systems

Flexible Bodies

Multi-Domain-Simulation

Dynamics of Machines

and experimental

vibration analysis

Energetic Optimization

of Vehicles and Machines Acoustics und Hearing

Mechanics of wood and

other natural materials

Mechanisms and

Robotics


Introduction

25


Introduction

26

Competences:

• Finite Element Analysis

• Simulation of elastic multi-body-systems

• Modal and vibration analysis and Measurements

• Modeling of mechatronic systems

• Energy flow simulation

• Acoustic calculation and simulation

• Algorithm development

• Biomechanics and wood


Introduction

Motivation: Simulation and energetic optimization of auxiliaries

27

rising transport

volume rising energy

costs

political and public

awareness for

environmental protection

systematic

advantage over road

vehicles

degree of efficiency of single

components is already quite high

incentives for rail traffic

must be provided

rising efficiency in

road traffic

Reduction of railway vehicle’s energy

consumption by innovative measures and

intelligent control strategies

Cost efficient and sufficient precise

evaluation of these measures

Example: TRAXX F140 MS

Quelle: http://www.bahnbilder.ch/picture/8247


Introduction

Example of energy flows of railway vehicles

28

Up to 20 % of the energy demand are used for auxiliaries Optimization possible?

traction effort (79,6 %)

gear (1

,6 %

)

tract

ion m

oto

r (3

,9 %

)

tract

ion c

onvert

er (0

,6 %

)

train

energ

y s

upply

(6 %

)

const

. auxili

ary

consu

mption (0,4

%)

auxili

ary

coolin

g (1,0

%)

convert

er (1

,4 %

)

transf

orm

ato

r (5

,5 %

)


Introduction

Auxiliaries

29

board net battery converter cooling cooling tower

driver’s cabin cooling traction motor cooling transformator cooling air compressor

Picture source: Bombardier Transportation GmbH


Introduction

Approach

• Analysis and classification of the auxiliaries

• Improvement of energy efficiency by improved control strategies

• Proof of functionality and of improved energy balance by simulation

• Roadmap: Selection of simulation approach Modeling Validation Simulation of optimization measures

30

breaking system

compressor

headlights

wiper

door control

heater window

traction motors

aggregate-cooling

battery charger

starterseat lights

hand dryer

climate control

air conditioning

heater

220V-power-supply

fuel pump

common rail

autom. train-protection

control units

radio- communication

wagon lighting

eff

icie

ncy

sp

ecif

icco

ntr

ol

inte

llig

en

tco

ntr

ol

comfortoperationsafety

sh

ort

tim

ep

arti

tial lo

ad

full lo

ad


Simulation Approach

Energy Simulation

31

ENERGY

SIMULATION

evaluation

of driving

time and

driving task

energy

saving

driving

energy or

fuel

consump-

tion

evaluation

of new

concepts/

components

𝐸𝑡𝑜𝑡𝑎𝑙 = 𝑚 ⋅ 𝑎 + 𝐹𝐷𝑅 𝑣, 𝑠 ⋅𝑣

𝜂𝑙𝑜𝑐𝑜+ 𝑃𝑎𝑢𝑥(𝑧) ⋅ 𝑑𝑡

𝑡1

𝑡0

FDR … driving resistance

s … position on track

ηloco … efficiency locomotive

Paux … auxiliary power

v … vehicle speed

z … driving state

m … dynamic vehicle mass

𝑎 =𝐹𝑤ℎ𝑒𝑒𝑙 − 𝐹𝐷𝑅(𝑣, 𝑠)

𝑚

possible

limited

not possible


Simulation Approach

System Simulation

32

possible

limited

not possible

SYSTEM

SIMULATION

complex

control

strategies

represen-

tation of

thermal and

electrical

networks

evaluation

of new

concepts/

components

design of

new system

components

𝐸𝑡𝑜𝑡𝑎𝑙 = 𝑃𝑎𝑢𝑥,𝑖 𝑡

𝑛

𝑖=1

⋅ 𝑑𝑡

𝑡1

𝑡0

𝑃𝑎𝑢𝑥,𝑖 = 𝑓(𝐹𝑤ℎ𝑒𝑒𝑙 , 𝐶𝑡ℎ, 𝑧, 𝑣, 𝑡, … , 𝑃𝑎𝑢𝑥,𝑗)

Coupling between complex control and physical

representation of components

+ detailed system representation

– modelling effort

diesel

fuel

3

supercap

ancillaries

(mechanic)

auxiliaries

(electric)3

train mass

inertia

3

battery

train power supply

DC

-lin

k

GS

3diesel engine

M

3~

mech.

brakes

gear box

+ wheel

unidirectional energy flow bidirectional energy flow


Simulation Approach

Joining the benefits of both simulation approaches

33

ENERGY

SIMULATION

evaluation

of driving

time and

driving task

energy

saving

driving

energy or

fuel

consump-

tion

SYSTEM

SIMULATION

complex

control

strategies

represen-

tation of

thermal and

electrical

networks

design of

new system

components

evaluation

of new

concepts/

components

𝐸𝑔𝑒𝑠 = 𝑚 ⋅ 𝑎 + 𝐹𝐷𝑅 𝑣, 𝑠 ⋅𝑣

𝜂𝑙𝑜𝑐𝑜⋅ 𝑑𝑡 + 𝑃𝑎𝑢𝑥,𝑖 𝑡

𝑛

𝑖=1

⋅ 𝑑𝑡

𝑡1

𝑡0

𝑡1

𝑡0

𝑎 =𝐹𝑤ℎ𝑒𝑒𝑙 𝑃𝑎𝑢𝑥 𝑡 − 𝐹𝐷𝑅(𝑣, 𝑠)

𝑚

𝐹𝑤ℎ𝑒𝑒𝑙 , 𝑣

𝑃𝑎𝑢𝑥 Hybrid Train

Optimizer


Simulation Approach

Implementation

34

Auxiliary model

Auxiliary definition

• auxiliary characteristics

• control strategies

• thermal network model

• cooling model

• battery model

Power demand

• depending on traction

power demand and vehicle

speed

Train model

(Hybrid Train Optimizer)

FWL

FG

L

FWG1FG

W1

FWG2FG

W2

FWG3FG

W3 FWG4

FG

W4

FWG5

FG

W5

Train definition

• powertrain characteristics

• number of traction units

and wagons

• driving resistance

• traction control

Driving trajectory

• fastest trip

• energy optimization of

trajectory

MATLAB

coupling

𝐹𝑤ℎ𝑒𝑒𝑙 𝑡 , 𝑣 𝑡

𝑃𝑎𝑢𝑥 𝑡

weak coupling may allow

sequential simulation


Modeling

Model overview

35

track data

Energy Simulation

train data

model control

compressor air system

transformator + converter +

cooling towers

braking resistor + cooling

traction motors + coolingaux. converter 1:

frequency control

battery charger + 110V-network

driver’s cabin climate control

Σ total power

from catenaryconsumed power

general control signal

frequency value

temperature signal

„compounds“


Modeling

Cooling tower

36

transformator with thermal

capacities and

characteristic map based

losses

converter with thermal

capacities and


losses

cooling towers with heat

exchangers


Modeling

Pressure system

37

controlled valve for brake

system supply


losses screw compressor

with bypass and air

cooling

additional air consumers

as nearly constant

constant flow


Modeling

Driver’s cabin

38

air conditioning and

heaters

forced convection (fluid flow over

surface) representing the roof and

the window of the driver’s cabin

walls modeled as

thermal resistances

climate control

solar radiation


Modeling

Brake resistor and traction motor with cooling

39

brake resistor represented

as thermal capacity

forced convection

representing the

resistor’s surface

ventilation control of brake

resistor cooling

traction motor represented

as thermal capacity and


losses

forced convection

representing the

motor’s surface

brake resistor traction motor


Modeling

Combining compounds to overall model

40

model control

brake resistor

auxiliary control

transformator, converters, cooling

towers

traction motors

driver‘s cabin climatisation

pressure system

110V battery and network


Validation

Concept

41

sub-model A sub-model B

overall model

validated model

sensitivity analysis

validationvalidation

validation


Validation

Brake resistor

42

0 100 200 300 400 5000

100

200

300

400

500

600

time in s

bra

ke

re

sis

tor

tem

pe

ratu

re in

°C

measured

simulated

0 50 100 150 200 250 300 350 4000

100

200

300

400

500

600

700

time in s

bra

ke

re

sis

tor

tem

pe

ratu

re in

°C

measured

simulated

ventilation frequency: 60 Hz ventilation frequency: 45 Hz


Validation

Overall model

43

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

10

20

30

40

50

60

time in s

tem

pe

ratu

re in

°C

/ fre

qu

en

cy in

Hz

Transformator measured in °C

converter measured in °C

Aux.-converter 1 measured in Hz

Ambient temperature measured in °C

Transformator simulated in °C

converter simulated in °C

Aux.-converter 1 simulated in Hz

Ambient temperature simulated in °C


Validation

Sensitivity analysis

• Evaluation of a parameter variation on objective functions (energy

from catenary and energy consumed by auxiliaries)

Identification of influential parameters

Testing the influence of uncertain parameters

44

𝛿𝑗

𝛿𝑥 ≈𝑗𝑚 𝑥 + 𝜖𝑛𝑒 𝑛 − 𝑗𝑚(𝑥 )

𝜖𝑛⋅𝑥𝑛𝑗𝑚

… canoncial unit vector … vector of parameters … vector of parameter variations … vector of objective functions

𝑥

𝜖

𝑒

𝑗

no influential

uncertain parameters

high influence of

traction motor cooling


Results of optimization measures

Optimization measures

• Idea: Shifting consumption periods into recuperation phases

energy costs reduction even in AC mode, if recuperated energy is

less than the price for consumed energy

• Optimization measures

• Driver’s cabin climate control

usage of cabin’s thermal capacity for preferred heating/cooling in

recuperation phases

• Air compressor control

Raise the lower pressure limit of the main air reservoir in

recuperation phases

• Battery control

Usage of board battery as small recuperation storage

• Cooling tower ventilation depending on recuperation

Pre-cooling in recuperation phases

45



Results

46

-5 15-5

-4

-3

-2

-1

0

1

2

3

4

5

TAtm

in °C

E

aux.-

conv. i

n %

AC

DC

-5 15-5

-4

-3

-2

-1

0

1

2

3

4

5

TAtm

in °C

E

cate

nary

,aux.-

conv. in

%

AC

DC

-5 15-3

-2

-1

0

1

2

3

TAtm

in °C

E

cate

nary

in

%

AC, consumption

AC, recuperation

DC, consumption

-5 15 35-5

-4

-3

-2

-1

0

1

2

3

4

5

E

aux.-

conv. i

n %

TAtm

in °C

AC

DC

-5 15 35-5

-4

-3

-2

-1

0

1

2

3

4

5

E

cate

nary

,aux.-

conv. in

%

TAtm

in °C

AC

DC

-5 15 35-3

-2

-1

0

1

2

3

E

cate

nary

in

%

TAtm

in °C

AC, consumption

AC, recuperation

DC, consumption

-5 15 35-5

-4

-3

-2

-1

0

1

2

3

4

5

E

aux.-

conv. i

n %

TAtm

in °C

AC

DC

-5 15 35-5

-4

-3

-2

-1

0

1

2

3

4

5

E

cate

nary

,aux.-

conv. in

%

TAtm

in °C

AC

DC

-5 15 35-3

-2

-1

0

1

2

3

E

cate

nary

in

%

TAtm

in °C

AC, consumption

AC, recuperation

DC, consumption

-5 15 35-4

-2

0

2

4

6

8

10

12

14

16

18

E

aux.-

conv. i

n %

TAtm

in °C

AC

-5 15 35-4

-2

0

2

4

6

8

10

12

14

16

18

E

cate

nary

,aux.-

conv. in

%

TAtm

in °C

AC

-5 15 35-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

E

cate

nary

in

%

TAtm

in °C

AC, consumption

AC, recuperation

driver‘s cabin climate control air compressor control

recuperation-depending cooling battery control



Combination of all measures

47

-5 15-20

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

E

aux.-

conv. i

n %

TAtm

in °C

AC

DC

-5 15-20

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

E

cate

nary

,aux.-

conv. in

%

TAtm

in °C

AC

DC

-5 15-7

-6

-5

-4

-3

-2

-1

0

1

2

3

E

cate

nary

in

%

TAtm

in °C

AC, consumption

AC, recuperation

DC, consumption

combination of all measures leads to a reduction of up to 20 % of the consumed auxiliary energy

approx. 0,2 bis 0,3 % energy costs reduction

(energy prices according to DB Energie)


Summary

Summary

• Various methods for railway vehicle’s energy consumption are

existing

Choice of suitable method depending on calculation task

• Simulation helps to understand system behavior and to test

optimization measures

• The presented example of the auxiliaries control’s optimization

illustrates an approach to reveal consumption potentials

48

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System Simulation in the Development of Rolling Stock

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