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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 GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
Problem statement
• mounting torque has influence on vertical axle loads
• non-symmetrical axle loads
• lower adhesion at front axles
direction of train
7 locomotive driveline with traction control
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
© ITI GmbH itisim.com
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
8 locomotive driveline with traction control
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
10 locomotive driveline with traction control
© ITI GmbH itisim.com
Adhesion control
• Powertrain model includes:
• torsional behavior of the driveline
• adhesion of the wheel-rail contact
• adhesion control and its speed sensing
11 locomotive driveline with traction control
© ITI GmbH itisim.com
Half model of the bogie suspension
12 locomotive driveline with traction control
• multi-body model (3D) of the locomotive’s suspension system
• chassis
• bogie frame
• suspension system in three dimensional space
© ITI GmbH itisim.com
Suspension model
13 locomotive driveline with traction control
© ITI GmbH itisim.com
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
14 locomotive driveline with traction control
© ITI GmbH itisim.com locomotive driveline with traction control 15
© ITI GmbH itisim.com locomotive driveline with traction control 16
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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”
18 torsional vibration of wheelsets
© ITI GmbH itisim.com
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.
19 torsional vibration of wheelsets
© ITI GmbH itisim.com
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.
20 torsional vibration of wheelsets
© ITI GmbH itisim.com
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.
21 torsional vibration of wheelsets
© ITI GmbH itisim.com
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)
22 torsional vibration of wheelsets
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
Introduction
25
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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 %
)
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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“
© ITI GmbH itisim.com
Modeling
Cooling tower
36
transformator with thermal
capacities and
characteristic map based
losses
converter with thermal
capacities and
characteristic map based
losses
cooling towers with heat
exchangers
© ITI GmbH itisim.com
Modeling
Pressure system
37
controlled valve for brake
system supply
characteristic map based
losses screw compressor
with bypass and air
cooling
additional air consumers
as nearly constant
constant flow
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
characteristic map based
losses
forced convection
representing the
motor’s surface
brake resistor traction motor
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
Validation
Concept
41
sub-model A sub-model B
overall model
validated model
sensitivity analysis
validationvalidation
validation
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
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
© ITI GmbH itisim.com
Results of optimization measures
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
© ITI GmbH itisim.com
Results of optimization measures
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)
© ITI GmbH itisim.com
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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Schweriner Straße 1
01067 Dresden
T + 49 (0) 351.260 50 - 0
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