Mechatronic Design in Automotive Systems

Copyright ® IFAC Mechatronic Systems, Darmstadt, Germany, 2000

MECHA TRONIC DESIGN IN AUTOMOTIVE SYSTEMS

M. Hiller, R. Bardini, T. Bertram, M. Torlo and D. Ward

Mechatronics Laboratory, Gerhard-Mercator-Universitiit Duisburg Lotharstr. 1, D-47057 Duisburg Phone: +49 (0) 203 379 - 2199, Fax: +49 (0) 203 379 - 4143 {hiller, bardini, bertram, to rio, ward}@mechatronik.uni-duisburg.de

Abstract: This paper gives an overview of current industry based projects in the field of vehicle modeling and simulation for the mechatronic design of automotive systems. It shows the wide range of applications for analysis and synthesis during the development process, including vehicle systems, vehicle dynamics, occupant safety, adaptive cruise control, hardware in the loop and fault tolerant real-time systems. Copyright @2000 IFAC Keywords: Modeling, Simulation, and Control of Multibody Kinematics and Dynamics, Active and Passive Safety in Automotive Systems.

1. INTRODUCTION

mobiles. The automobile in the mind of the younger generation is no more than a powerful computer. It is important to keep in perspective the fact that automobiles are primarily mechanical products with mechanical functionality. Electrical assemblies and the embedded software are only enabling technologies, and not the critical vehicle functions themselves. However, sophisticated functions such as engine management, traction control, and active vehicle dynamics can only be implemented today by the judicious combination of the mechatronic technologies.

Something historic is happening in the automobile business. It will affect the transportation world in the same way that the invention of the internal combustion engine affected personal transportation. The process is happening overnight and is influencing the whole industry. It involves an entirely new automobile development and manufacturing process, and requires a fundamental shift in thinking. From thinking of the car as a mechanical device that carries some electronic controls to thinking of the car as an mechatronic device (Fig. 1). This means a device where the mechanical, electrical, and software parts are fully integrated (Dickinson 1996, DesJardin 1996).

Among the various current developments in the electronics field, the trend towards networking existing and newly developed systems is playing a prominent role. While linking control systems for active safety The main driving force for this shift in our thinking is has already been employed for some years, the next step in this evolution is the integration of systems, the expectations of the consumer. Consumers already expect the same I I aimed at the ~ user' s wish for things from their ~Oincreased safety, automobiles as mechanical device mechatronic device they do from improved securivehicle ty systems, redutheir other consumer electronics power unit motion ced power conproducts. They sumption, reexpect safety, sponsible ecosecurity, reliabillogical friendliness, comfort, ity, ease of operation, comfort, and growing entertainment, multimedia caand value for pabilities. Thus, money. Furtherelectronic systems which were more they expect Fig. 1. The automobile as a mechatronic device. what they can get elsewhere, for example in their up until now essentially autonomous are now growing together. This process is mainly driven by dehome or in their office, to be available in their auto-

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mand for improved functionality and the need to limit costs. Extended system interaction helps to make more intelligent use of what is already installed and can even simplify present installations. The traditional example of interaction in the area of active safety is the link between the traction control and engine management systems for torque control. The engineering of such a new, interconnected system poses great challenges - in particular for guarantying its reliability, safety, and acceptance by the car user. The network has to be set up systematically to achieve advantages going beyond the sum of the components, and to avoid mutual disturbance. On top of that, each network component must be able to work in a wide variety of configurations where varying contributions from different sources come together. Therefore, the complete network must be scalable from a low level of functionality and cost, via numerous customer oriented variants, up to the future state-of-the-art in automotive electronics. To deal with these challenges, and the long and complex supply chain associated with them, the automotive industry has been converging on development processes where the systematic modeling and simulation play a major role.

Fig. 2. Modular structure ofFASIM_C++. equations of motion is part of the modules and only at the beginning of simulation is it evaluated. The topology of the vehicle, which describes the kinematical topology of the individual modules, is shown in Fig. 3. For reasons of clarity the modules engine, engine suspension, power train, braking system, driver and environment are not shown. Using this modeling technique it is possible to decide during runtime which configuration of a vehicle is used without any recompilation of the program. FASIM_C++ contains a large library of different vehi-

This contribution is divided into 6 main sections. Section 2 briefly describes modeling vehicles in FASIM_C++, and how they are modeled as mechatronic systems, incorporating multibody kinematics and dynamics, hydraulics, controllers sensors, data management systems, and environmental conditions. The next three sections illustrate application of the complex vehicle dynamic simulation, to roll over simulation (Section 3), ACC (Section 4), and hardware-in-the-loop simulation with the complex vehicle model (Section 5) and makes some remarks with regard to the fault tolerant integration of a decoupled control system into the vehicle. Section 6 gives some conclusions. 4; .... , LII

Fig. 3. Kinematical topology. 2. VEHICLE DYNAMICS SIMULATION

cle modules such as wheel suspensions, tire models, powertrains, engines, engine suspensions, controllers, sensors, elasticities, a rigid or flexible car body, several hydraulic braking systems, a driver and an environment model. The similar structure of the modules makes it easy to expand the library by adding new modules. The equations of motion are based on D'Alembert's principle:

Development of vehicle controllers requires an appropriate model of the vehicle dynamics, all built into a versatile simulation environment. This simulation environment has to be able to simulate different vehicle types or models without any recompilation. The vehicle model has to have a modular form so that single components of the vehicle may be exchanged depending on the simulation tasks. Thus, models of the vehicle dynamics with differing levels of complexity can be defined covering correspondent physical effects with the desired accuracy. The modular structure of a vehicle model in FASIM_C++ is shown in Fig. 2 using the example of a passenger car. The structure presented does not show the construction details of the modules, e.g. which kind of front axle is used. During initialization this is not important, because the required information for generating the

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any relevant accident parameter. For this reason future sensor concepts must supply information about vehicle stability. approaching obstacles. vehicle interior conditions. accident type and crash severity (Grosch et al. 1996).

Due to the constraints in the system, the virtual displacements are not independent. To generate the equations of motion in minimal coordinates the choice of f independent generalized coordinates ql> q2, ... ,qr is necessary, corresponding to the number of degrees of freedom in the system. The equations of motion of the mechanical system in minimal coordinates can then be written as:

Computer simulation plays an important role in the development of a rollover detection system (Hiller. and Bardini 1998). Vehicle dynamics simulation. for example. provides the possibility to test in advance various sensors and algorithms for rollover detection. Furthermore. occupant simulation can be used to establish trigger times for rollover detection. For occupant simulation the commercial simulation toolset MADYMO (Lupker 1996) is used. FASIM_C++ and MADYMO have been combined to form an application and development environment for the rollover detection system from Robert Bosch GmbH (Mehler et al. 1998). Some special enhancements have been made in FASIM_C++ for conducting rollover simulations. Firstly the sensor. including the 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 simulation of embankment and ramp maneuvers it is now possible to configure surfaces like those shown in Fig. 4.

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

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Fig. 4. Example of the surface contour used for rollover simulation.

3. ROLLOVER SIMULATION

The application of the modeling techniques to the analysis of real world vehicle problems shall be illustrated by the simulation of an embankment rollover. As leaving the road is statistically the most likely cause of a rollover situation. it is very important that this maneuver is detected by a rollover protective system.

An industrial application of FASIM_C++ lies in the field of passive vehicle safety. Featuring front airbags. side airbags. seat belt pretensioners and load limiters existing restraint systems provide a high level of protection. Additionally so now that knee airbags and head protecting side airbags are starting to come onto market. For the activation of these protective devices comprehensive sensor systems are required which can react with the appropriate deployment of restraint systems. taking into account

Therefore. a full scale rollover test with a middle class car has been investigated in detail. As shown in Fig. 5 a very good correlation between the simulation and the real experiment has been achieved. Only when the car body hits the ground does the simulation yield incorrect results. as the contact interactions between the exterior of the vehicle and the environment have not been modeled. Since this phase of the rollover is no longer of importance for rollover detection these errors have been neglected. When the car

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Incorporating ACC into an existing vehicle simulation system allows faster system development and removes safety issues from system testing as the vehicle model, complete with control systems, can be tested on the computer rather than on the street. An ACC system, which in this implementation also requires a vehicle dynamics controller (ESP) , has been incorporated into the vehicle simulation package FASlr-CC++.

body hits the ground rollover detection must have taken place long ago. The sensor module that has been implemented in the vehicle is fed with longitudinal and lateral acceleration and angular velocity data during simulation by the chassis module, and returns the trigger signal for controlling the rollover protective devices. The instant of rollover detection is visualized by a cone which has been added to the animation and which becomes visible above the hood when the sensor triggers (Fig. 5). With the validated model it is possible to perform parameter studies in order to optimize the rollover sensing concept, and to establish trigger times for roll over protective devices such as seat beIt pretensioners and window airbags.

ACC is not a new system. It is already well developed, and recently been made available on some production cars, so why bother developing a simulation system now? One reason is that the technology is not yet mature. There is a lot of room for improvement, especially with performance 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 features for ACC so that it can be used as a stepping stone for the development of an Advanced Driver Assistance (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 advanced simulation package such as FASIM_C++. Possible extensions to ACC, to start to provide some of the features of a fully fledged ADA system include (Ward et a\. 1999):

Fig. 5. Comparison of simulation with full scale rollover.

4. ACC - A NEW ACTIVE SAFETY SYSTEM?

Active steering: Various manufacturers are researching steer-by-wire systems and lane recognition systems. An obvious extension of ACC would be to combine it with an active steering system, initially for simpler tasks such as maintaining position is a lane, but later, in combination with new sensing schemes and systems, for autonomous driving. The environment model in FASIM_C++ allows easy extension to provide lane information to ACC for such an extension. A slight variation on the use of active steering for obstacle avoidance, steering in the lane would 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 traveling at the side of the road.

Adaptive Cruise Control (ACC) is currently one of the main control system focuses of vehicle and vehicle component manufacturers. At the moment it is seen as a comfort system, reducing driver fatigue in heavy traffic, but it has the potential to provide a number of safety related features in the near future and of becoming an integral part of an Intelligent Transportation System (ITS). Current (production) systems extend the concept of the traditional cruise control by adding a radar unit to detect vehicles traveling in the traffic ahead. This information is processed by a controller that determines whether the vehicle ahead is traveling slower, and whether it may be necessary to decelerate in order to maintain a safe following distance. When the slower vehicle moves out of the way or speeds up, the ACC controller accelerates the host vehicle back to its previously set cruising speed.

Obstruction Recognition and A voidance: This would involve recognizing stationary obstructions on the roadway (not only other vehicles) and calculating a

Fig. 6. Three snapshots from an animation of a braking-to-standstill simulation.

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parably low computing power. It is based on a VME bus system with a Motorola 68040 CPU, a dedicated real-time operating system (OS9) and application specific IJO cards. In the real-time application the workstation must be able to calculate one simulation step ~t in less than or equal to real-time. In this case the workstation is a DEC Alpha 600 5/333 workstation with 333 MHz clock speed. In order to model the dynamics of the mechanical subsystems of the vehicle with sufficient accuracy integration steps of less than 1 ms (and even as small as 0.1 ms for the hydraulic components) are required.

safe method for avoiding them. This could be braking, steering around them, or in the case of a smaller obstacle such as a small rock that would easily clear the underside of the vehicle, driving over the obstacle such that the tires travel either side of it, and tire damage is avoided. This involves use of the active steering system, and ACC sensors to both calculate the size of the obstacle, and recognize the state of the traffic so that such a steering manoeuvre is only carried out when there is no danger from or to other vehicles.

Stop and Go: Braking to standstill (Fig. 6) has already been mentioned as a logical progression in the development of current ACC systems. The addition of the ability to start again once the vehicle has stopped would take this one step further and make traveling in a traffic jam or in slow traffic much more comfortable, and less stressful or tiring for the driver. Driver input may be required to indicate that it is safe to move off again in early systems, but the ultimate system would be completely autonomous.

The real-time simulation is carried out with the same program as is used for the offline simulation. To ensure that the model is valid for the complete range of operating conditions the model has been validated. This means that extensive in-vehicle measurements were performed and compared with simulated data. For a rear wheel driven vehicle, the vehicle model consists of 41 first order differential equations. In the offline simulation models with up to 70 first order differential equations are available.

5. HARDW ARE-IN-THE-LOOP REAL-TIME SIMULATION Real-time simulation is becoming more and more important for testing of electronic control unit (ECU) software in complex mechatronic systems. Efficient and reliable test and release of ECU software for such systems cannot be achieved using expensive and time consuming in-vehicle testing only. Parallel application of in-vehicle tests, oftline simulation and real-time simulation is essential for adequate software verification within required cost and time frames (Fig. 7). In our case real-time simulation is used for testing safety software in automotive ECUs. It continually checks input and output signals for plausibility and consistency. The complete IJO is handled by a low cost real-time computer with co mFunctional Verification ECU Scftwore

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Most innovations in the development of automotive ECUs for modern cars is based on an iterative process. In this environment Hardware-in-the-loop simulation offers a wide range of function tests in the laboratory under close-to-real conditions. Furthermore, it is possible to implement new algorithms and weight them correspondingly with very short development cycles. The function of an ECU can be examined often only in the application environment with further controllers under real-time conditions. Specific to the drive-by-wire systems, these will increasingly replace the mechanical and hydraulic systems in the car in the next few years, so it must be possible to guarantee at every time that in the case of an error in one or more components, safe stopping of the vehicle remains possible. Consequently, the complete system must have a fault tolerant design and have systems so that individual controllers are able to diagnose an error and to initiate the corresponding countermeasures. In this case, the communication between the individual modules occurs via a bus system which has an error resistant, redundant design. If an serious disturbance occurs on the bus, the communications of all remaining participants must immediately be able to transferred onto a backup system. In addition, the failed device(s) must be able to detach itself from the bus system. It must be checked continuously whether data transmitted via the bus system is transmitted correctly and is still valid during timecritical processes. For example, in the case of the brakes in a drive-by-wire system no delay in the resulting braking effect is allowed to occur because a defective ECU is disturbing the communications on the bus system.

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Fig. 7. Hardware-in-the-loop concept.

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analysis and synthesis during the development process, including vehicle systems, vehicle dynamics, occupant safety, adaptive cruise control, and hardware-in-the-loop and fault tolerant real-time systems.

Consequently, every command has a limited, temporal validity in addition to the requirement that it is actually correct. The communication of the individual components occurs at a tightly clocked frequency, which assigns a time window to every element in which communications may occur. One possible solution here is the Time-Triggered-Protocol (TTP) which evaluates the temporal behavior of the system components, as described, during each bus cycle. A similar procedure is also used in the mobile digital GSM communication systems (i.e. mobile cellular telephones). The individual transmission channels transmit the digital information in a timemultiplexed fashion in firmly assigned time slots. The ECUs in motor vehicles today are primarily independent, and only transmit diagnostic functions or status information via a bus system to the outside world. To exclude a possible system failure through erroneous communications with the sensors and actuators, peripherals and controllers are often integrated into one 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 (Fig. 8). An ECU would consequently be able to be reduced to the actual micro controller and the corresponding communication hardware. This would then be similar to a distributed computer system 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 the brakes or some other important function that has failed . RoIlover Sensor

The object-oriented design of the simulation environment FASIM_C++ allows easy adaptation of different extensions of the vehicle model. On the one hand side this leads to a remarkable variety of the vehicles that can be simulated, while on the other hand, the extension of the modular vehicle model is easy to manage. Additional mechanical or nonmechanical components (e.g. controllers, sensors) can easily appended. The vehicle model described has been implemented for the development of a rollover protective system, the extension of a state of the art ACC system, and for hardware-in-the-Ioop simulation.

Acknowledgement: A major part of the work presented in this paper has been supported by Robert Bosch GmbH, Stuttgart. REFERENCES DesJardin, L. (1996). A Day in the Life of Mechatronic Engineers 10 Years from Now. SAE International Congress and Exposition. DetroitiMichigan, U.S.A., SAE96C038. Dickinson, G. W. (1996). Cars and Trucks: The Ultimate Consumer Electronics Products. Automotive News World Congress, DetroitiMichigan, U.S.A. . Grosch, L., B. Mattes and D. Schramm (1996). Smart Restraints. Airbag 2000, 2nd International Conference . Karlsruhe, Germany. Hiller, M. and R. Bardini (1998). Vehicle and Occupant Dynamics Simulation - Important Tools in the Development of Sensor Concepts for the Control of Restraint Systems. 8th German-Japanese Seminar on "Nonlinear Problems in Dynamical Systems - Theory and Applications". Kobe, Japan. Hiller, M. and A. Kecskemethy (1989). Equations of Motion of Complex Multibody Systems Using Kinematical Differentials. Transactions of the CSME, 13(4). Lupker, H. A. (1996). MADYMO, a Versatile Tool for Vehicle Safety Analyses. 6th International MADYMO Users' Meeting. Amsterdam, Netherlands. Mehler, G., B. Mattes, M. Henne, H.-P. Lang and W. Wottreng (1998). Rollover Sensing (ROSE). 2nd International Conference on Advanced Microsystems for Automotive Applications. Berlin, Germany. Ward, D., T. Bertram and M. Hiller (1999). Vehicle Dynamics Simulation for the Development of an Extended Adaptive Cruise Control. IEEElASME International Conference on Advanced Intelligent Mechatronics, AIM'99. Atlanta, U.S .A.

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6. CONCLUSIONS This paper gives an overview of current industry based projects in the field of vehicle modeling and simulation for the mechatronic design of automotive systems. It shows the wide range of applications for

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