Vehicle Modeling and Simulation in the

Duisburg Mechatronics Laboratory

D. Adamski, R. Bardini, T. Bertram, C. Hörsken, O. Lange, U. Roll, M. Torlo and D. WardVehicle Simulation Group, Mechatronics Laboratory, Gerhard-Mercator-Universität Duisburg

Lotharstr. 1, D-47057 Duisburg{ adamski, bardini, bertram, hoersken, lange, roll, torlo, ward} @mechatronik.uni-duisburg.de

Abstract

This paper gives an overview of the current industry based projects in the field ofvehicle modeling and simulation in the Duisburg Mechatronics Laboratory. Itshows the wide range of research fields covered in Duisburg, including vehiclesystems, vehicle dynamics, occupant safety, adaptive cruise control, power steer-ing, hardware in the loop, fault tolerant real time systems and tolerance analysis.

1 Systematic System Approach for a Car-Wide Web – where all the vehi-cle functions are in networking with each other

Torsten Bertram

Something historic is happening in the automobile business. It will affect the transportationworld in the same way that the invention of the internal combustion engine affected personaltransportation. The process is happening overnight and is influencing the whole industry. Itinvolves an entirely new automobile development and manufacturing process. It requires afundamental shift in thinking. From thinking of the car as a mechanical device that carriessome electronic controls to thinking of the car as an mechatronic device (Figure 1.1). Thismeans a device where the mechanical, electrical, and software parts are fully integrated(Dickinson 1996, DesJardin 1996).

Figure 1.1: The automobile as a mechatronic device.

visibility

steer

electrical supply system

power unit

body and interior

vehicle motion

vehiclecoordinator

engine

suspension

brake

gearbox

clutch

propulsion

securitysafety

comfortdisplaybattery

alter-nator

mechanical device mechatronic device

The main driving force for this shift in our thinking is the expectations of the consumer. Con-sumers already expect the same things from their automobiles as they do from their other con-sumer electronics products. They expect safety, entertainment, security, reliability, ease ofoperation, comfort and value. Furthermore they expect what they can get elsewhere, for ex-ample in their home or in their office, to be available in their automobiles. The automobile inthe mind of the younger generation is no more than a powerful computer. The output, insteadof transporting data that they need from some distant mainframe memory, transports the userto an important destination.

It is important to keep in perspective the fact that automobiles are primarily mechanical prod-ucts with mechanical functionality. Electrical assemblies and the embedded software are onlyenabling technologies, and not the critical vehicle functions themselves. However, sophisti-cated functions such as engine management, traction control, and active vehicle dynamics canonly be implemented today by the judicious combination of the mechatronic technologies.

Among the various current developments in the automotive electronics field, the trend to-wards networking existing and newly developed systems playing a prominent role. Whilelinking control systems for active safety has already been employed for some years, the nextstep in this evolution is the integration of systems, aimed at the user’s wish for increasedsafety, improved security systems, reduced power consumption, environmental friendliness,comfort, and multimedia capabilities. Thus, electronic systems which were so far essentiallyautonomous are now growing together to form what could be called a Car-Wide Web. Thisprocess is mainly driven by demand for improved functionality and the need to limit costs.Extended system interaction helps to make more intelligent use of what is already installedand can even simplify present installations.

The traditional example of interaction in the area of active safety is the link between the trac-tion control and engine management systems for torque control. Currently adaptive cruisecontrol (ACC) is pushing automotive system networking to new levels. In the future, vehiclemotion systems, safety systems, security systems, comfort systems, and multimedia systemswill integrate and strive for the vision of accident-free, comfortable, well informed, and envi-ronmentally friendly driving (Figure 1.2).

The engineering of such a new, interconnected system poses great challenges - in particularfor guarantying its reliability, safety, and acceptance by the car user. The network has to beset up systematically to achieve advantages going beyond the sum of the components and toavoid mutual disturbance. On top of that, each network component must be able to work in awide variety of configurations where varying contributions from different sources come to-gether. Therefore the complete network must be scalable from a low level of functionality andcost via numerous customer oriented variants up to the future state-of-the-art in automotiveelectronics.

Figure 1.2: Intelligent transport system infrastructure and communications.

To deal with these challenges, and the long and complex supply chain associated with them,the automotive industry has been converging on a model reflected in the well known V-diagram. The development process, that consists of the analysis, design, and implementationis modeled as a V, where time is the horizontal axis, and the level of integration is the verticalaxis. The development and manufacturing process starts with the overall vehicle concept, andis gradually decomposed into its various assemblies and sub-assemblies until a specification isproduced. The rising right leg of the V demonstrates the build and implementation phase,where components are integrated into gradually larger assemblies and systems, until an auto-mobile is built. Each mechatronic system, for example powertrain, braking, steering, and sus-pension, are major systems within the automobile and follow the same V-diagram (Figure1.3).

Trafficinformation andmanagement

center

satellite

roadsideinformationpointhighway surface

conditions

weatherconditions

trafficcongestion

A

obstacledetection

satellite-to-vehiclecommunication

inter-vehiclecommunication

road-to-vehiclecommunication

weather conditionshighway surface conditionstraffic congestionand otherspeed and steering control

obstacle avoidancepre-crash sensingman-machine interfacein-car navigational systems

on-boardsystems

and other

Figure 1.3: V-diagram.

Already various parties in the industry are working on networking systems on various scales.As a foundation for networking and to support the analysis phase in the V-diagram in order to

describe the overall automobile concept an open structuring concept called CARTRONICâ

(Figure 1.4, Bertram et. al. 1998, 1999) is under development. At the moment it comprises themain aspects “ function“ and “safety“ .

The function architecture structures the logic of all known control tasks in the automobileindependent of specific hardware configuration and is open for future extension. On top ofthat, a safety architecture adds what is required to guarantee the safety of networked systems.As a whole, the ordering concept aims at providing a consistent methodology to handle thechallenges that arise from increasing system interconnection starting at the abstract level offunction analysis and going on through to a realized piece of electronic hardware.

The first step required to derive benefit from system interconnection is to analyze the func-tionality of each single system. Logical components, their tasks, interfaces, and interactionshave to be identified and the services offered and the interfaces required have to be described.Thus, components with the same task, nowadays multiply realized in different systems, can beused by every system in the network. This stage of system analysis, which still is independentof any implementation or realization considerations, is called Function Architecture. It can bemapped without structural changes to whatever electronics hardware is suitable and eco-nomic.

CAR CONCEPT CAR

OVERALL CAR CONCEPT

system architecure

subsystem architecture

module

real com-po-nent

level of integration

COMPONENTS

time

mixed technologysimulations

specifications based onmodels

modulardesign

synchronized designchanges

hardware emulation andhardware-in-the-loop

simulation

analysis

design

analysis

design

im-plemen-

tation

im- .plemen-

tation

The Function Architecture forms a prerequisite for the development of complex vehicle con-trol systems. After the analysis of the car functions and its interfaces from a logical point ofview engineers are able to define control loops, and can then start to design controllers usingclassical methods.

Additionally, there is an increasing requirement to ensure that safety risks are effectivelyminimized, and for ensuring that no degradation in performance from either a safety or envi-ronmental point of view might take place. Incorporating safety into the early stages of thedesign process may in fact be the most effective way to reduce or eliminate potentially seriousrisks posed by large and complex systems.

Figure 1.4: Definition of CARTRONICâ .

The safety analysis itself is based on the Function Architecture and adds, from a safe point ofview, what is required to guarantee the safety of networked systems. In the early days of sys-tem design, safety and reliability received only limited consideration. The safety and reliabil-ity design was largely intuitive and based fundamentally on the designer’s experience andskill. Statistically based techniques deliver a more structured analysis of the device. Thesemethods gained popularity in the 1940s and 1950s. In the early 1960s the Failure mode andeffects analysis (FMEA) was devised and safety analysis was provided with a new drivingforce. Traditionally, safety analysis has been used primarily for verification purposes. Such anapproach fails when designing complex systems with significant elements of novelty. Forsuch systems, safety aspects need to bee systematically integrated into the development proc-

ess. The safety related aspect of CARTRONICâ follows an approach which is an abstractionof the FMEA system, and delivers the Safety Architecture from a functional and logical pointof view.

Through integration via a systematic system approach, it is possible to enhance quality, tomake the automobile safer, more secure, and a true communications device. For example, byintegrating the collision warning system with the speed sensor, ESP, windshield wipers, tire-pressure sensor, radio, and cellular phone, the warning distance is computed based on thebraking distance required for actual road, tire, and driver conditions. The cellular phone, navi-

CARTRONIC:Ordering conceptfor vehicle control systemscomprising modular, expandibleFunction and Safety Architectures.Based on agreedstructuring rules in order toorganize networking systemswith the aims�

new or improved functionality� control of total complexity� reuse and interchange of systems� independent optimization

supported through defineddevelopment method and tools. CC - cruise controlACC -adaptive

cruise controlSAG - stop & go

control

VGC - vehicle

guidance control

...

ALC - automatic

longitudinal control

ABS - antilockbraking control

TCS - tractioncontrol

ESP - electronicstability control

EBC - electronicbody control

EAS - electronicactive steering

VGC - vehicleguidance control

vehicle

control systems

gation, and air bags work together to automatically make an emergency call and identify thevehicles location if an accident or emergency occurs.

After analyzing the automobile functions from a logical and safety point of view an overalllogical and safe automobile concept is produced based on the agreed structuring and modeling

rules of CARTRONICâ . The automobile concept comprises the Function and Safety Archi-tecture, which describe the active parts, as well as the passive basis of the automobile (i.e. thechassis). The next step that is defined by the V-diagram is simulation of the automobile con-cept.

2 Vehicle Dynamics Simulation with FASIM_C++

Dirk Adamski

Development of vehicle controllers requires an appropriate model of the vehicle dynamics, allbuilt into a versatile simulation environment. This simulation environment has to be able tosimulate different vehicle types ormodels without any recompilation.The vehicle model has to have amodular form so that single compo-nents of the vehicle may be ex-changed depending on the simula-tion tasks. Thus, models of the vehi-cle dynamics with differing levels ofcomplexity can be defined coveringcorrespondent physical effects withthe desired accuracy. The modularstructure of a vehicle model inFASIM_C++ is shown in Figure 2.1using the example of a passengercar. The structure presented does notshow the construction details of the

modules, e.g. which kind of front axle is used. Duringinitialisation this is not important for FASIM_C++, be-cause the required information for generating the equa-tions of motion is part of the modules and only at thebeginning of simulation is it evaluated. The topology ofthe vehicle, which describes the kinematical topology ofthe individual modules, is shown in Figure 2.2. For rea-

sons of clarity the modules engine, engine sus-

pension, powertrain, braking system, driver and environment are not

shown (Hiller et al. 1997). The concept presented has been realised using the programming

car body

front axle

rear axle

drive

wheel

engine

powertrain hydraulics

wheel wheel

wheel

Figure 2.1: Modular structure ofFASIM_C++

engine suspension

McPherson strut

car body

power train

wheel

f ive point wheel suspension

L ,..., L6 11 L ,..., L12 17

L 1

L18

L2

L3 L 4

L5

Figure 2.2: Kinematical topology

language C++. In this way it is possible to decide during runtime which configuration of avehicle is used without any recompilation of the program. FASIM_C++ contains a large libraryof different vehicle modules such as wheel suspensions, tyre models, power trains, engines,engine suspensions, controllers, sensors, elasticities, a rigid or flexible car body, several hy-draulic braking systems, a driver and an environment model. The similar structure of themodules makes it easy to expand the library by adding new modules.

The equations of motion are based on D’Alembert’s principle:

( ) ( )[ ] 01

=⋅−×++⋅−∑=

B

iiiii

n

iiSiSiiSSiSim TrFr δδ

���, (2.1)

with:

nB number of mass-endowed bodies,

iSim Θ, mass and inertia tensor of body i,

iSr�� acceleration of c.o.g.,

iSi TF , applied force and torque,

iiSr Φδδ , virtual linear and angular displacement.

Due to the constraints in the system, the virtual displacements in Eq. (2.1) are not independ-ent. To generate the equations of motion in minimal coordinates the choice of f independentgeneralised coordinates q1,...,qf is necessary, corresponding to the number of degrees of free-dom in the system. The equations of motion of the mechanical system in minimal coordinatescan then be written as:

),,,(),()( tqqQqqbqqM����

=+ (2.2)

with the notation and dimensions:

M ]ff[ × generalised mass matrix,

q ]1f[ × generalised coordinates,

b ]1f[ × generalised gyroscopic forces,

Q ]1f[ × generalised applied forces.

Applying the principle of kinematic differentials, the elements of the equations of motion arecalculated expressing partial derivatives using kinematic terms. Due to the modular structureof the matrices and vectors, their elements can easily be calculated from the correspondingmodules. For this reason they are subdivided into an inner sum, inside the module l consider-ing all its bodies nB and in an outer sum considering all modules nM.

( ) ( ) ( ) ( )( )[ ]( ) ( ) ( )[ ]

( ) ( )[ ]

⋅+⋅=

×+⋅+⋅=

⋅+⋅=

∑∑

∑∑

∑∑

= ∈

= ∈

= ∈

M

l

M

l

M

l

n

l Iii

jii

jij

n

l Iiiiiii

jii

jiij

n

l Ii

kii

ji

ki

jiikj

m

m

1

1

1,

ˆˆ

ˆˆˆˆ

ˆˆˆˆ

TFrQ

rrb

rrM

�

����

��

(2.3)

The pseudo velocities )(ˆ jir

�, ( )j

iω and pseudo accelerations ir , i are defined as follows (Hil-

ler and Kecskeméthy 1989):

( )

( )

===

==

∑

∑∑

=

= =f

j

iij

j

ii

j

iji

f

j

f

kkj

kj

ii

j

iji

qqq

qqqqq

1

1 1

2

; ˆ ,ˆ

ˆ ,ˆ

qJq

J

rr

rr

����

�

�����

∂∂ω

∂∂ω

∂ω∂ω

∂∂∂

∂∂

ωω

(2.4)

In the next two sections projects that use FASIM_C++ are presented.

3 Rollover Simulation

Roberto Bardini

One of the latest industrial applications of FASIM_C++ lies in the field of passive vehiclesafety. Featuring front airbags, side airbags, seat belt pretensioners and load limiters existingrestraint systems provide a high level of protection. Additionally so now that knee airbags andhead protecting side airbags are starting to come onto market. For the activation of these pro-tective devices comprehensive sensor systems are required which can react with the appropri-ate deployment of restraint systems, taking into account any relevant accident parameter. Forthis reason future sensor concepts must supply information about vehicle stability, approach-ing obstacles, vehicle interior conditions, accident type and crash severity (Grösch et al.1996). Thus future restraint systems should guarantee occupant protection in frontal, side andrear impacts as well as during rollover.

3.1 Simulation Tools

Computer simulation plays an important role in the development of a rollover detection sys-tem (Hiller and Bardini 1998). Vehicle dynamics simulation, for example, provides the possi-bility to test in advance various sensors and algorithms for rollover detection. Furthermore,occupant simulation can be used to establish trigger times for rollover detection. In the fieldof vehicle dynamics the modular computer program FASIM_C++ has proved to be extremelyhelpful. For occupant simulation the commercial simulation toolset MADYMO is used.MADYMO provides sophisticated and validated multi-body models of crash test dummies,that can be placed in any accident scenario (Lupker 1996).

FASIM_C++ and Madymo have been combined to form an application and development envi-ronment for the ROSE rollover detection system from Robert Bosch GmbH (Mehler et al.1998).

3.1.1 Rollover Simulation using FASIM_C++

Some special enhancements have been made in FASIM_C++ for conducting rollover simula-tions. Firstly the sensor including the ROSE rollover detection algorithm was implemented.Thus it was now possible to analyze the triggering behavior in any simulated maneuver.

Furthermore, it was necessary to enhance the modeling of the environment. For the simulationof embankment and ramp maneuvers it is now possible to configure surfaces like those shownin Figure 3.3.

Figure 3.3: Example of the surface contour used for rollover simulation

Validation

The application of the modeling techniques to the analysis of real-world vehicle problemsshall be illustrated by the simulation of an embankment rollover. As leaving the road is statis-tically the most likely cause of a rollover situation, it is very important that this maneuver isdetected by a rollover protective system. Therefore, a full scale rollover test with a middleclass car has been investigated in detail.

As shown in Figure 3.4 a very good correlation between the simulation and the real experi-ment has been achieved. Only when the car body hits the ground does the simulation yieldincorrect results, as the contact interactions between the exterior of the vehicle and the envi-ronment have not been modeled. Since this phase of the rollover is no longer of importancefor rollover detection these errors have been neglected. When the car body hits the groundrollover detection must have taken place long ago.

bX

Y

Z

µ=0,45

Street

h

B

H

Ramp

Embankment 2

Embankment 1

µ=0,8

Figure 3.4: Comparison of simulation with full scale rollover

The sensor module that has been implemented in the vehicle is fed with translational accel-eration and angular velocity data during simulation by the chassis module, and returns thetrigger signal for controlling the rollover protective devices. The instant of rollover detectionis visualized by a cone which has been added to the animation and which becomes visibleabove the hood when the sensor triggers (see Figure 3.4). Unfortunately in this particular casea recording equipment failure means that this comparison can only be qualitative. Howeverother customer data confirms the quantitative validity of the model.

With the validated model it is possible to perform parameter studies in order to optimize therollover sensing concept, and to establish trigger times for rollover protective devices like beltpretensioners and window airbags. By combining FASIM_C++ and MADYMO an applicationand development environment has been formed, as illustrated in Figure 3.5.

Figure 3.5: Using FASIM_C++ and MADYMO 3D as calibration tools for ROSE

3.1.2 Occupant Simulation Using MADYMO

MADYMO (MAthematical DYnamic MOdel) is a commercial computer package which hasmainly been developed for studying occupant behavior during car crashes (Lupker 1996).MADYMO combines in one simulation program the capabilities offered by multi-body andfinite element techniques. Furthermore, MADYMO offers a set of standard force models e.g.for seat belts, airbags and contact of bodies with each other and with their surroundings. Con-sequently, it also allows assessments to be made on the suitability of various restraint systems.One main feature of MADYMO is the availability of a validated database of crash-testdummy multi-body models, which are suitable to study injuries sustained by accident victimswith a high accuracy. The ''50th percentile male Hybrid III Dummy'' represents an ''average''of the USA adult male population. It is modeled as a multi-body system and has 57 degrees offreedom. The interaction between dummy and passenger compartment can be represented bygeometric primitives such as planes, ellipsoids and cylinders attached to the bodies of themulti-body system (Figure 3.6). As in rollover maneuvers a slip movement between thedummy body and the seat belt often occurs, a finite element model for the seat belt was im-

Position and Orientation Data

a X

, a, a

, ,

,

Y

Z

X

Y

Z

ωω

ω

F _C++ASIMMADYMO 3D

Sensor Simulation

Visualisa

tionROSE Algorithm

Trigger Decision

Verification

plemented. As the roof structure is assumed to be stiff enough to prevent collapsing, no intru-sions of the roof are taken into account (compare Friedman and Friedman 1998).

In order to be able to make estimations about the occupant behavior when the vehicle is roll-ing on its bodywork the exterior contours of the vehicle were also included in the MADYMOcontact modeling.

Figure 3.6: Dummy sitting in the passenger compartment

4 ACC – a New Active Safety System?

Derek Ward

Adaptive Cruise Control (ACC) is currently one of the main control system focuses of vehicleand vehicle component manufacturers. At the moment it is seen as a comfort system, reducingdriver fatigue in heavy traffic, but it has the potential to provide a number of safety relatedfeatures in the near future and of becoming an integral part of an Intelligent Transport System(ITS) (Nissan 1998, Ludmann and Weilkes 1999). Current (production) systems extend theconcept of the traditional cruise control by adding a radar unit to detect vehicles travelling inthe traffic ahead. This information is processed by a controller that determines whether thevehicle ahead is travelling slower, and whether it may be necessary to decelerate in order tomaintain a safe following distance. When the slower vehicle moves out of the way or speedsup, the ACC controller accelerates the host vehicle back to its previously set cruising speed.Current units only function down to speeds in the area of 30 km/h, similar to traditional cruisecontrol systems, partly due to system limitations and partly due to the fact that ACC is cur-rently only seen as a comfort system and not a safety system.

The major features of ACC can be summarised as follows:�

With automatic acceleration or deceleration (with braking capacity limited by comfortcriteria) the host vehicle is able to maintain a pre-selected following distance behind apreceding vehicle or to drive at a set maximum vehicle velocity.

The range of conditions under which the system can be operated has been expended by

using a radar which is capable of recognising traffic in front of the vehicle regardless ofrain or other inclement weather conditions.

� ACC assists the driver, helping to co-ordinate driving behaviour with traffic flow, and in

doing so reduces driver fatigue and stress.

� The system contributes to improved traffic efficiency, safety and environmental friendli-

ness by homogenising the flow of traffic, especially under inter-urban driving conditions

Incorporating ACC into an existing vehicle simulation system allows faster system develop-ment and removes safety issues from system testing as the vehicle model, complete with con-trol systems, can be tested on the computer rather than on the street. An ACC system, whichin this implementation also requires a vehicle dynamics controller (VDC or ESP™), has beenincorporated into the vehicle simulation package FASIM_C++. The complete system is also atypical example of a mechatronic system.

4.1 Why Simulate with ACC?

A complete simulation provides a powerful tool to develop ACC and extend it’s functionality,reducing development time and removing the risk to personnel that occurs when such vehiclecontrol systems are tested under driving conditions (normal or otherwise).

Figure 4.7: Three snapshots from an animation of a Braking-to-Standstill simulation.

4.2 Extending ACC

ACC is not a new system. It is already well developed, and recently been made available onsome production cars, so why bother developing a simulation system now? One reason is thatthe technology is not yet mature. There is a lot of room for improvement, especially with per-formance at low speeds (current systems are not capable of braking to standstill). A second,and more compelling reason, is that there is a lot of scope to develop new extended featuresfor ACC so that it can be used as a stepping stone for the development of an Advanced DriverAssistance (ADA) system. This development could be carried out faster and more safely if at

least the initial feature evaluation and development could be carried out using an advancedsimulation package such as FASIM_C++.

Possible extensions to ACC, to start to provide some of the features of a fully fledged ADAsystem include (Ward et al. 1999):

4.2.1 Active steering

Various manufacturers are researching steer-by-wire steering systems and lane recognitionsystems. An obvious extension of ACC would be to combine it with an active steering sys-tem, initially for simpler tasks such as maintaining position is a lane, but later, in combination

t = 0s t = 4.0s t = 7.0s

with new sensing schemes and systems, for autonomous driving. The environment model inFASIM_C++ allows easy extension to provide lane information to ACC for such an extension.

4.2.2 Obstruction Recognition and Avoidance

This would involve recognising stationary obstructions on the roadway (not only other vehi-cles) and calculating a safe method for avoiding them. This could be braking, steering aroundthem, or in the case of a smaller obstacle such as a small rock that would easily clear the un-derside of the vehicle, driving over the obstacle such that the tyres travel either side of it, andtyre damage is avoided. This involves use of the active steering system, and ACC sensors toboth calculate the size of the obstacle, and recognise the state of the traffic so that such asteering manoeuvre is only carried out when there is no danger from or to other vehicles.

4.2.3 Steering in the Lane

A slight variation on the use of active steering for obstacle avoidance, steering in the lanewould allow the vehicle to steer around the rear end of a vehicle turning off into a side road,or provide extra room to a bicycle or pedestrian travelling at the side of the road.

4.2.4 Stop and Go

Braking to standstill has already been mentioned as a logical progression in the developmentof current ACC systems. The addition of the ability to start again once the vehicle has stoppedwould take this one step further and make travelling in a traffic jam or in slow traffic muchmore comfortable, and less stressful or tiring for the driver. Driver input may be required toindicate that it is safe to move off again in early systems, but the ultimate system would becompletely autonomous.

All this points to the development and extension of ACC being a logical step in the develop-ment of Intelligent Transportation Systems (ITS). One of the big advantages of this is thatmany of these extended features of ACC can be put in place before the infrastructure requiredfor a complete ITS system with completely autonomous driving has been put in place. Forexample, lane recognition systems are already under development that use optical systems incombination with the drivers steering inputs to calculate the intended vehicle path, and deter-mine whether another vehicle stands in this path, and so whether or not a response is requiredfrom ACC.

5 Simulation of Hydraulic Power Steer ing Systems

Olav Lange

Commonly used full vehicle CAE models are based on multibody dynamics theory, imple-mented in commercial CAE analysis packages like ADAMS. Subsystems and componentssuch as hydraulic power steering systems or shock absorbers are normally represented asstatic characteristics. Practical development work shows, however, that this representation isnot sufficient to represent their true effect on the overall vehicle dynamics performance.

In general the supplier of the subsystem has a detailed CAE model before the product ismanufactured. The car company as well as the supplier wish to have a full vehicle model forsimulation, including the newest CAE models, to avoid or at least reduce the expensive itera-tive process required to optimise the design of the components with respect to the overallproduct.

To realise such an exchange of simulation models, it is necessary to implement interfacesbetween the assumed modular models. The input and output parameters must be defined, sothat they can be transferred from one submodel to another. This is especially difficult in casewhere different simulation packages are used.

Another problem is how to update the simulation model in the case where a component issuperseded by a new version. It must be investigated how many parameters are sufficient todescribe a subsystem or more practically, which tests and measurements must be carried outto achieve a complete set of parameters in order to update the model. Answering these ques-tions is the objective of a research project supported by the Ford Werke AG, Cologne. Thefirst step is the examination and understanding of the subsystem. This paper is focused on theunderstanding of a hydraulic power steering system.

Steer ing system

The steering system has a great influence on the dynamic behaviour of a vehicle. It is the linkbetween the environment, i.e. the road, and the driver. There are two main points of interest,which will be analysed: Firstly, how does the vehicle react to a steering wheel angle variation,and secondly, what is the reaction to an external force or torque at the tires, which may becaused by a fault in the road surface or an out of balance tire?

The first question may be answered using a simple steering system model, such as staticcurves. The reaction to an out of balance tire, the so called nibble effect, or wheel fight, driv-ing a curve on cobbled pavement, all cause high frequency events. From these observations it

Rack

Torsion Bar

Pinion

Steering Wheel

Steering Torque

α Boost ForceF=F( )α

Figure 5.1: Schematic View of a Steering System

is evident that it is no longer possible to neglect the dynamic behaviour of the hydraulic sys-tem.

The hydraulic power steering system consists of two parts,

• the mechanical part (Fig. 5.1) and

• the hydraulic part (Fig. 5.2).

Mechanical par t

The driver normally applies the steering torque by turning the steering wheel. This torquecauses a twist in the torsion bar, which is the weak part in the inputshaft. This twist angle isthe input value for the hydraulics. The rotation of the inputshaft is transformed into transla-tional movement by the rack and pinion mechanism. The force caused by friction at the tiresand in the suspension is applied in the opposite direction to the movement. The hydraulic partproduces a boost force which should counteract the friction force to keep the required steeringinput torque low.

Hydraulic par t

One method used to model a hydraulic system is the analogy to electrical circuits (see Ulrichet al. 1998). The compressibility of the fluid and the elasticity of the hoses is represented bycapacitances, the inertia of the fluid can be modelled as inductivities and the friction as eitherlaminar or turbulent resistances.

In conventional power assisted steering systems the assistance force at the rack is a functionof the applied steering torque. The twist in the torsion bar is the input of the valve, which isrepresented by the measurement bridge (Rgap1, Rgap2, Rgap3 and Rgap4) in Fig. 5.2. It controlsthe volume flow to the different chambers in the cylinder.

L R

C C

L R

C C

Rgap2

Rgap4 Rgap3

Rgap1

L R

C C

RR

L R

C C

RR

R R

R R

Q3QQ2 5

7

6

8

10Q

Q

Q

Q

Q

9

Obstacle

Hose_in Hose_out

Hose

Pump

Pump

HoseToTank

Tank_inTank_out

Tank

P

Q

PP

PP PP

PP

PP

P

1

2 3

4

Q

QQ

Q

Q Q Q

Q

Q

Q

C

C

C

C

CC

C

P

P P

f( )α

f( )α

f( )α

f( )α

gap1 gap2

gap3gap4

4

QD

BV

1

109 6

78

Cylinder

5

Figure 5.2: Hydraulic Part of the Steering System

The inputs from the mechanical to the hydraulic system are the cylinder position, the cylinder

velocity and the twist angle α. The output is the assistance force acting on the piston. Thesefour connections are the interface that can be used to implement a detailed hydraulic model ina complete vehicle model.

The CAE model introduced has a defined level of complexity. Here the hoses are representedusing concentrated parameters. On the one hand the complexity could be easily increased, buton the other hand the ‘simple’ model given already has plenty of parameters, which must bedetermined. One aim is to decrease the number of parameters without loosing the significantcharacteristics. This can be achieved using non-physical parameter models or methods likesoft computing.

6 Hardware-in-the-Loop Real Time Simulation

Uwe Roll

Real Time Simulation is becoming more and more important for testing of electronic controlunit (ECU) software in complex mechatronic systems. Efficient and reliable test and releaseof ECU software for such systems cannot be achieved using expensive and time consumingin-vehicle testing only. Parallel application of in-vehicle tests, offline simulation and real timesimulation is essential for adequate software verification within required cost and time frames(Figure 6.1). In our case real time simulation is used for testing safety software in automotiveECUs. It continually checks inputand output signals for plausibilityand consistency. Figure 6.2 gives anoverview of the components of thetest bench, which uses a standardworkstation and a real time I/O com-puter.

The complete I/O is handled by alow cost real time computer withcomparably low computing power. Itis based on a VME bus system witha Motorola 68040 CPU, a dedicatedreal time operating system (OS9)and application specific I/O cards.For the workstation there is only onedifference between offline applica-tion and real time application. In the real time applications the workstation must be able to

calculate one simulation step ∆t in less than or equal to real time. This is essentially a matterof computing power. In this case the workstation is a DEC Alpha 600 5/333 workstation with333 MHz clock speed. In order to model the dynamics of the mechanical subsystems of thevehicle with sufficient accuracy integration steps of less than 1 ms (and even as small as

Figure 6.1: Hardware-in-the-Loop Concept

(Roll et al. 1999)

0.1 ms for the hydraulic components) are required. Data from the controller, sensors, and ac-tuators must also be transmitted within this time interval.The real time simulation is carriedout with the same program as is usedfor the offline simulation. This ispossible due to the high performanceof the DEC Alpha station and theefficient algorithms used in thesimulation package FASIM, a modu-lar simulation package for non-linearvehicle dynamics. FASIM was devel-oped over several years for offlinesimulation, and the code is mainlywritten in FORTRAN using doubleprecision calculations. To ensure thatthe model is valid for the completerange of operating conditions themodel has been validated. This means that extensive in-vehicle measurements were per-formed and compared with simulated data. For a rear wheel driven vehicle, the vehicle modelconsists of 41 first order differential equations. In the offline simulation models with up to 70first order differential equations are available. So in offline simulation even more powerfulmodels exist, but today’s workstations still do not provide the performance required to runthem in real time.

Due to the ever increasing performance of modern workstations it is now possible to run ex-isting offline simulation packages in “ real time”. These models can be made available for realtime simulation with HIL.

With minor interventions in the operating system and the addition of an I/O computer, realtime simulation and offline simulation can run on the same workstation. In this way the highexpenditure required to port the software is removed and the leading edge technology of high-end workstations is made available for real time simulation.

7 Fault Tolerant Integration of Multiple Controllers in Modern Vehicles

Marc Torlo

Most innovations in the development of automotive electronic control units (ECU) for mod-ern cars is based on an iterative process. In this environment Hardware-In-the-Loop simula-tion offers a wide range of function tests in the laboratory under close to real conditions. Fur-thermore, it is possible to implement new algorithms and weight them correspondingly withvery short development cycles. Particularly in the development of Drive-by-wire systems itbecomes more and more important that different controllers have synchronous compatibility.The function of an ECU can be examined often only in the application environment with fur-

ECU

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Figure 6.2: Components of the Hardware-in-the-Loop Test Bench (Jedrkowiak et al. 1998)

ther controllers under realtime conditions. Special to the Drive-by-wire systems, these willincreasingly replace the mechanic and hydraulic systems in the car in the next few years, so itmust be possible to guarantee at every time that in the case of an error in one or more compo-nents, safe stopping of the vehicle remains possible. Consequently, the complete system musthave a fault tolerant design and have systems so that individual controllers are able to diag-nose an error and to initiate the corresponding countermeasures. In this case, the communica-tion between the individual modules occurs via a bus system which has an error resistant, re-dundant design. If an serious disturbance occurs on the bus, the communications of all re-maining participants must immediately be able to transferred onto a backup system (Figure7.1). In addition, the failed device(s) must be able to detach itself from the bus system.

Backup Bus

Controller

Bus

Arbiter

Figure 7.1: A Fault tolerant ECU

It must be checked continuously whether the data transmitted via the bus system was trans-mitted correctly and was still valid during time-critical processes. For example, in the case ofthe brakes in a Drive-by-wire system no delay in the resulting braking effect is allowed tooccur because a defective ECU is disturbing the communications on the bus system. Conse-quently, every command has a limited, temporal validity in addition to the requirement that itis actually correct. The communication of the individual components occurs at a tightlyclocked frequency, which assigns a time window to every element in which communicationsmay occur. One possible solution here is the Time-Triggered-Protocol (TTP) which evaluatesthe temporal behavior of the system components, as described, during each bus cycle. Asimilar procedure is also used in the mobile digital GSM communication systems (i.e. mobilecellular telephones). The individual transmission channels transmit the digital information in atime-multiplexed fashion in firmly assigned time slots. The ECUs in motor vehicles today areprimarily independent, and only transmit diagnostic functions or status information via a bussystem to the outside world. To exclude a possible system failure through erroneous commu-nications with the sensors and actuators, peripherals and controllers are often integrated intoone control unit (i.e. ABS and ESP). If a communications system has the described features,

the reuse of sensors and actuators for functions of a similar type is also conceivable (Figure7.2). An ECU would consequently be able to be reduced to the actual microcontroller and thecorresponding communication hardware. This would then be similar to a distributed computersystem where, in the case of the disturbance of single device, another undertakes its function,switching from a safety irrelevant functions such as the air conditioning to controlling thebrakes or some other important function that has failed.

BUS

RolloverSensor

Yaw RateSensor

4 xWheel Velocity

Sensors

BUSESP/ABS

ECU

MotorManagment

ECU

AirbagECU

Figure 7.2: Decouppled Control System

8 Tolerance Analysis in Computer Aided Design and Multibody Systems

Christian Hörsken

Today the modelling and simulation of designs created using CAD and multibody methods isfocused on exact geometrical parameters. However, in the real world parameters can only begiven with a tolerance around a mean value. These tolerances have an important influence onthe operation of mechanisms, and may not be neglected. If the geometrical tolerance chosenfor a part is too large, problems could occur with functionality and assembly. On the otherhand, if the tolerances chosen are too small, the difficulty and cost of production will be un-necessarily high. To get an optimum balance between these two contrary aspects there is aneed to consider and compute tolerances.

Parts and assemblies can be modelled as Computer Aided Design (CAD) drawings or asmultibody systems (MBS). Today, in both geometric approaches, tolerance analysis is notcommonly used. The basic elements of a multibody system are rigid bodies and joints. Simi-larly in CAD there exist construction elements which represent rigid bodies. The constraintsin CAD correspond to joints in a multibody system. This analogy is presented in more detailin Hörsken et al. 1998a. In this paper all methods are only presented for CAD, but the resultscan be applied in MBS as well.

In classic modelling, parts and assemblies are based on given fixed geometric parameters.When talking about tolerance analysis, the geometric parameters become intervals withincertain boundaries. The number of potential configurations is the reason that it is impossibleto simulate all of them. The output of a classical simulation depends on the given require-ments. In tolerance computing these output values vary within specific ranges. The output canbe subdivided into the following groups:

• Geometric tolerances: Computation of extreme geometric contours of parts, for all varia-tions of the geometric parameters, within the limit of the prescribed tolerances.

• Static and dynamic tolerances: Calculation of the variation of the forces arising in an as-sembly under the effect of parameter variations.

Several publications exists in this field (Hinze 1994), (Clément and Rivière 1998), (Salomonset al. 1995), (Taylor 1997), (Weber et al. 1998), (Alvarez et al. 1998), and (Hashimoto et al.1998) on Geometric Tolerances. To date static and dynamic tolerances have received only asmall amount of attention (Joskowicz and Sacks 1997), and (Hörsken et al. 1998b). Becausecomputer aided tolerancing (CAT) is more popular with CAD drawings than with multibodysystems, these articles that have been mentioned all deal with CAD designs. In CAT there aretwo major application phases:

1. In the design process the focus is on developing parts and assemblies with a very high oreven absolute reliability. A typical method for this task is worst-case tolerance analysis.

2. During production the physical tolerances of the parts are investigated and evaluated. Inthis phase mainly the method of statistical tolerance analysis is mainly used.

8.1 Tolerance Envelopes

In this section a method for worst-case tolerance analysis called tolerance envelopes is intro-duced (Hinze and Schultheiss 1998). At the moment this method is only suitable for planarCAD drawings and for computing geometric tolerances. Tolerance envelopes are an extensionof the Euclidean geometry with tolerance boundaries. Assume that a CAD drawing consists ofpoints, lines, and circles. A graph, that contains all construction elements, and their linkageswith each other is called a constraint graph. The tolerance envelopes of the construction ele-ments are defined as follows:

• Point: The tolerance envelope P of a point p is a regular convex quasi-polygonal set.

• Line: A line can be defined by two points. Let P and Q be the tolerance envelopes of the

two points. With a straight line ),( vpg , a point p , and a direction vector v , the tolerance

envelope of a line is defined as { }QqPpqpgQPG ∈∈= ,|),(),( .

• Circle: For a circle ),( rpc , a point p , and a radius r there is the tolerance envelope of

the circle can be defined as { }RrPprpcRPC ∈∈= ,|),(:),( .

Figure 8.1 and figure 8.2 show examples for the tolerance envelopes of a line and a circle.

Of course, there exist many more possibilities to define construction entities than just those,presented above. The intersection of construction elements requires the computation ofMinkowski sums of the tolerance envelopes. The overall computation of the tolerance enve-lope of a drawing traverses the constraint graph recursively.

8.2 Statistical Tolerance Analysis

A method for computing geometric, static, and dynamic tolerances is the statistical toleranceanalysis. Here the CAD drawing or the multibody system is formulated as an independentrandom experiment. The geometric tolerance parameters are the input of the simulation. Thechoice for the corresponding distribution of the random input numbers has to be given. Inter-esting values, for example position of parts, or forces in an assembly, are the output of theexperiment. With the Monte Carlo simulation a large number of simulations with different

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Figure 8.1: Tolerance envelope of aline

Figure 8.2: Tolerance envelope of acircle

Figure 8.3: Simulation results Figure 8.4: Relative frequencies

random input values are performed. The output values can be evaluated statistically. In Figure8.3 the typical simulation output of a requested force is shown. Each dot marks the result ofone random experiment. Figure 8.4 shows the relative frequencies of this Monte Carlo simu-lation. Depending on the given tolerance analysis task, high reliability or statistical evaluationduring production, the random distribution for the input values has to be chosen. In Hörskenand Hiller 1999 different distributions are investigated.

Acknowledgement: A major part of the work presented in this paper has been supported by RobertBosch GmbH, Stuttgart and Ford Werke AG, Collogne.

References

A. Alvarez, J. I . Barbero, A. R. Carr illo, A. Longo and T. Gutiérrez. A variational systemfor computer aided tolerance analysis of 3D mechanical assemblies. In D. Roller, editor,Simulation, Virtual Reality and Supercomputing Automotive Applications, Croydon, June1998. International Symposium on Automotive Technology & Automation, ISATA Düssel-dorf Trade Fair.

T. Ber tram, R. Bitzer , R. Mayer and A. Volkar t. 1998. CARTRONIC - An open architec-ture for networking the control systems of an automobile. SAE International Congress and

Exposition. Detroit/Michigan U.S.A., SAE 98200, 1-9.

T. Ber tram, P. Dominke and B. Müller . 1999. The safety-related aspect of CARTRONIC.SAE International Congress and Exposition. Detroit/Michigan U.S.A., SAE 1999-01-0488, 1-16.

A. Clément and A. Rivière. A mathematical and technological analysis of contacts. In New

Directions in Contact Analysis and Simulation. The Institute of Electrical and ElectronicsEngineers, Inc., May 1998. 1998 IEEE International Conference on Robotics and Automation,Workshop WS6.

L. DesJardin. 1996. A Day in the Life of Mechatronic Engineers 10 Years from Now. SAE

International Congress and Exposition. Detroit/Michigan U.S.A., SAE96C038, 327-330.

G. W. Dickinson. 1996. Cars and Trucks: The Ultimate Consumer Electronics Products.Automotive News World Congress, Detroit/Michigan U.S.A., 09.01.1996.

D. Fr iedman and K. Fr iedman. Roof Crush Versus Occupant Injury from 1988 to 1992NASS. SAE Technical Paper Series 980210. Warrendale, PA 1998.

L. Grösch, B. Mattes and D. Schramm. Smart Restraints. In Proceedings: Airbag 2000, 2nd

International Conference. Karlsruhe, Germany 1996

K. Hashimoto, T. Obayashi, T. Matsuoka and K. Kinoshita. Quality improvement by theintroduction of geometrical tolerance with the aid of variation simulation analysis. In D.Roller, editor, Simulation, Virtual Reality and Supercomputing Automotive Applications,Croydon, June 1998. International Symposium on Automotive Technology & Automation,ISATA Düsseldorf Trade Fair.

M. Hiller and R. Bardini. Vehicle and Occupant Dynamics Simulation – Important Tools inthe Development of Sensor Concepts for the Control of Restraint Systems. In Proceedings:8th German-Japanese Seminar on "Nonlinear Problems in Dynamical Systems - Theory andApplications": p. 15-39. Kobe, 1998.

M. Hiller and A. Kecskeméthy. Equations of Motion of Complex Multibody Systems UsingKinematical Differentials. In Transactions of the CSME, 13(4), 1989, 113-121

M. Hiller , C. Schuster and D. Adamski. FASIM_C++ - A Versatile Developing Environmentfor Vehicle Dynamics Simulation. Int. Journal of Crashworthiness, 2(1):1997, 91-105.

U. C. Hinze. A Contribution to Optimal Tolerancing in 2-Dimensional Computer Aided De-

sign. Dissertation, Johannes Kepler Universität Linz, Ordinariat Industriemathematik des In-stituts für Mathematik der Technisch-Naturwissenschaftlichen Fakultät, 1994.

U. C. Hinze and R. Schultheiss. On the concept of tolerance envelopes. In New Directions in

Contact Analysis and Simulation. The Institute of Electrical and Electronics Engineers, Inc.,May 1998. 1998 IEEE International Conference on Robotics and Automation, WorkshopWS6.

C. Hörsken, R. Schultheiss and M. Hiller . Statical contact analysis in computer aided de-sign using graph theory. In New Directions in Contact Analysis and Simulation} . The Instituteof Electrical and Electronics Engineers, Inc., May 1998. 1998 IEEE International Conferenceon Robotics and Automation, Workshop WS6.

C. Hörsken, R. Schultheiss and M. Hiller . Integration of static analysis into variational ge-ometry design using graph theory. In D. Roller, editor, Automotive Mechatronics Design and

Engineering, pages 379--386, Croydon, June 1998. International Symposium on AutomotiveTechnology & Automation, ISATA Düsseldorf Trade Fair.

C. Hörsken and M. Hiller . Statistical methods for tolerance analysis in multibody systemsand computer aided design. Submitted to the Euromech Colloquium 404, Lissabon, Septem-ber 1999.

A. Jedrkowiak, A. Hesselbar th, H. Bar tels and M. Hiller . Implementation of Real TimeSimulation with Hardware-in-the-Loop on a DEC Alpha Workstation. In Proceedings of Mo-

vic’98, Zürich, Switzerland, 1998

L. Joskowicz and E. Sacks. Computer-aided mechanical assembly design using configura-tion spaces. Technical report 97-001, Purdue University, 1997.

J. Ludmann and M. Weilkes. Fahrermodelle als Hilfsmittel für die Entwicklung von ACC-Systemen. Automobiltechnische Zeitschrift 101 (5), 1999, pp. 306-314

H. A. Lupker . MADYMO, a Versatile Tool for Vehicle Safety Analyses. In Proceedings: 6thInternational MADYMO Users’ Meeting: p. 139-145. Amsterdam, 1996

G. Mehler , B. Mattes, M. Henne, H.-P. Lang and W. Wottreng. Rollover Sensing (ROSE).In Proceedings: 2nd International Conference on Advanced Microsystems for AutomotiveApplications. Berlin 1998

Nissan Motor Co. L td. “Nissan Develops Adaptive Cruise Control System” , News Item,http://www.nissan.co.nz/nissa_bin/fullstory.html?id=12, 1998

U. Roll, A. Jedrkowiak, M. Tor lo and M. Hiller . Hardware-in-the-Loop-Echtzeitsimulationzum Funktions- und Sicherheitsnachweis komplexer mechatronischer Systeme. In Proceed-

ings of ASIM’99, Weimar, Germany, 1999

O. W. Salomons, H. J. J. Poer ink, F. van Slooten, F. J. A. M. van Houten and H. J. J.Kals. A computer aided tolerancing tool based on kinematic analogies. In Proceedings of the

4th CIRP Seminar on Computer Aided Tolerancing, April 1995.

W. A. Taylor . Process tolerancing: A solution to the dilemma of worst-case versus statisticaltolerancing. Journal of Quality Technology, September 1997.

H. Ulr ich, M. Böcker and R. Kett. Simulation zur Optimierung von Servolenksystemen, 1.Internationales Fluidtechnisches Kolloquium Aachen, 1998.

D. Ward, T. Ber tram and M. Hiller . “Vehicle Dynamics Simulation for the Development of

an Extended Adaptive Cruise Control” in Proceedings IEEE/ASME International Conferenceon Advanced Intelligent Mechatronics, AIM'99, Atlanta, U.S.A., September 19-22, yet to bepublished, 1999

C. Weber, W. Br itten and O. Thome. Feature based computer aided tolerancing - a steptowards simultaneous engineering. In D. Roller, editor, Automotive Mechatronics Design and

Engineering, pages 501-507, Croydon, June 1998. International Symposium on AutomotiveTechnology & Automation, ISATA Düsseldorf Trade Fair.