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White PaperA plan to succeedReal-time simulations are becoming more and more important to assess the performance of a vehicle at an early stage of development, when no physical prototypes are available yet. Typical applications of real-time analyses are hardware-in-the-loop tests and driving simulators. A digital model of the car must be able to exchange input and output data with a physical system, either an electronic control unit or a human driver, without any delays due to the solving process. Several real-time solutions have been presented in the past, mostly based on simplified models which cannot predict with the required accuracy the effects of coupling a real, complex system with a virtual model. LMS Virtual.Lab™ Motion Real-Time Solver enables you to solve detailed, high-fidelity multi-body models in real-time, for more accurate HiL tests and Driving Simulators with direct, real-time access to any output of the model. Thanks to advanced co-simulation capa-bilities, LMS Virtual.Lab Motion Solver can also work with 1D representations of car subsystems, to further increase the level of accuracy of real-time analyses.

Siemens PLM Software

Real-time simulation of high-fidelity mechatronic systemsSpeed and accuracy when it truly matters

1A white paper issued by: Siemens PLM Software.

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 2

Contents

Executive summary ..........................................................3

Current state of real-time simulations .............................4

Breakthrough solutions for real-time applications ...........5

LMS Virtual.Lab Motion Real-Time Solver features ................................................6Deterministic implicit solver ...............................................6Parallel processing .............................................................6Adding 1D models of electric, hydraulic, pneumatic subsystems .......................................................7

Industrial validation examples .........................................8

Conclusion .....................................................................10

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 3

Executive summaryDigitalization and the virtual modeling of prototypes have become more and more important in the vehicle develop-ment process. In this context, multi-body simulation (MBS) is a standard tool used in the automotive industry design cycle. High-fidelity models are usually required to accurately cap-ture all relevant phenomena in vehicle dynamics.

The introduction of advanced driving assistance systems (ADAS) for performance and safety has transformed vehicles into true mechatronic systems that are usually designed, val-idated and optimized during experiments using physical pro-totypes. However, experimental tests are typically expensive, time-consuming and do not easily allow for any quick modifi-cation to the mechanical system. More importantly, such tests require a physical prototype of the mechanical system, which is not available until late in the design cycle, leaving little time to evaluate design alternatives. On the other hand, full virtual prototyping is often not feasible in practice because it requires the availability of a numerical model of the controller. Because ADAS control systems are commonly provided by Tier 1 suppliers, automotive OEMs rarely have full access to the control algorithms due to the need to pro-tect intellectual property.

The most viable approach to assess the correct interaction of ADAS with the mechanical system is the use of a hardware-in-the-loop setup (HiL). Currently, HiL testing is the de facto standard for electronic control unit (ECU) verification and validation at the majority of commercial vehicle OEMs and Tier 1 suppliers. With HiL, the physical hardware of the con-troller interacts with a numerical model of the mechanical system through sensors and actuators. (Figure 1 and 2). This implies that the numerical model should provide simulation results in real-time. Under normal operating conditions, the hardware controllers obtain information on the vehicle’s sta-tus from sensors at a fixed communication rate. A communi-cation interval of 1ms has become the industry standard in automotive applications. So, to mimic working conditions of the controller, the numerical model should provide accurate simulation results at the same communication interval of 1ms, taking into account the effect of the controller actions on vehicle response.

Figure 1: Example of HiL application, the early tuning of physical ECU for ABS/ESC system.

Supplier ABS/ESC

OEM vehicle model

Figure 2: A driving simulator, or driver-in-the-loop application.

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 4

Current state of real-time simulationsTypically, high-fidelity models are used in the design of the mechanical system for offline analyses. To achieve accurate results, it is often necessary to resort to modeling schemes in which a large number of degrees of freedom (DOFs) are defined. For example, automobile suspension models used for the prediction of elasto-kinematic characteristics usually include nonlinear compliances modeled as bushing force ele-ments between parts. Because of this required complexity, most high-fidelity models can easily have more than 150 DOFs, which presents a significant performance challenge for most MBS solvers. Current industry practice is to derive a reduced version from a high-fidelity MBS model to be used for design and validation of control systems, as well as for driving simulators. Model simplification is typically achieved by removing bodies and degrees of freedom until the model has been reduced enough in size to execute in real-time. With regard to suspensions, CAE analysts usually replace compliant connections with simpler kinematic constraints, or even use complex look-up tables to represent elasto-kine-matic characteristic curves. Such curves include position and orientation of wheels under large displacements (wheel ver-tical travels, steering angles) and wheel loads (longitudinal, lateral, aligning moments etc.). With these simplifications, typical models for real-time driving dynamics simulations have between 15 and 20 DOFs (Figure 3).

Low-fidelity models require a lower computational load, yet unavoidably reach a lower accuracy compared to high-fidel-ity models. Low-fidelity models can only offer acceptable simulation accuracy up to approximately 5Hz, typically cover-ing the roll, pitch and heave modes of the overall vehicle. High-fidelity modeling extends the model validity up to approximately 30Hz. This frequency range also includes dynamic effects from the finite stiffness of the bushings in the suspension and ADAS systems which can interact in an undesired manner with such dynamics. In this case, to detect and solve such problems in a timely manner, HiL tests with high-fidelity models of vehicle dynamics are needed.

In summary, the need to reduce an MBS model to run in real-time has three main negative effects:

1. Less accurate results from real-time simulations due to model simplification

2. Loss of most parameters from the MBS model, such as suspension geometry or bushings stiffness which become no longer available in the real-time model; any modifica-tion in the offline model thus requires a new reduction process

3. Additional effort for modeling and managing different models (one for offline, one for real-time) of the same vehicle

Figure 3: Today’s practice calls for different models for offline and real-time analyses.

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 5

LMS Virtual.Lab™ Motion software provides you with the ability to execute high-fidelity MBS models in real-time using the LMS Virtual.Lab Motion Real-Time Solver software. The obvious advantages of this solution are:

• The same high-fidelity model suitable for both offline and real-time simulations

• No loss of accuracy from offline to real-time simulations

• Easy “what-if” analyses by directly accessing physical parameters in the real-time model

Moreover, through a tight integration between LMS Virtual.Lab Motion and LMS Imagine.Lab™ Amesim™ software (Figure 4), detailed 1D representations of complex subsys-tems such as electro-hydraulic components and control log-ics can be added to 3D models for accurate real-time simulations of full mechatronic systems.

Breakthrough solutions for real-time applications

Figure 4: 1D submodels increase the fidelity of real-time analyses, e.g. EPAS – Electric Powered Assist Systems.

Figure 5: How the ride and handling assessment of a car can change by simulating a detailed model in real-time.

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 6

Three major features of the LMS Virtual.Lab Motion Real-Time Solver software include the deterministic implicit solver, parallel processing and the ability to add models of electric, hydraulic and pneumatic subsystems.

Deterministic implicit solverIn “hard real-time“ simulations the turnaround time of any time step, i.e. the time required to compute the system states at the next discretized point in time, must be known a priori to be shorter than the simulated time step (Figure 6). In other words, a real-time solver must be deterministic by definition.

Implicit schemes make sure the accelerations also satisfy the nonlinear differential algebraic equations (DAEs) of the model at the next discretized time point, thus requiring an a priori unknown number of iterations to reach a converged solution. Furthermore, in offline multi-body simulations, the order of the time integration scheme is also varied to opti-mally fit the numerical model along with the time step size.

In short, standard implicit solvers are not deterministic; they take a different amount of processing time per time step, which makes such solvers unsuitable for real-time simula-tions. To deliver simulation results in real-time, implicit time integration schemes need to be made deterministic. LMS Virtual.Lab Motion Real-Time Solver is a deterministic implicit solver, limiting the number of iterations and fixing the order of the time integration scheme. This enables hard real-time simulations with the highest numerical stability and results accuracy.

Parallel processingThe trend in computing is toward parallel processing on shared-memory processors (SMP) instead of attempting to achieve ever faster clock speeds. To get high-fidelity MBS models to run in real-time, you need to adapt the solution to take advantage of multiple cores.

Partitioning the solution method to take advantage of multi-ple processors is a nontrivial task in multi-body dynamics because of the coupling between DOFs due to constraints and force elements such as bushings, springs and dampers. In an implicit integration setting, this difficulty is com-pounded by the fact that the coupling between the equa-tions of motion is not limited to the constraints, but is also present in the force terms.

High-fidelity real-time simulations can be obtained by divid-ing the model into two or more pieces. Each submodel is solved on a different processor core in co-simulation with the others. The subdivision can be a cut-joint approach, but for vehicles it might be simpler to cut the chassis body and to have a separate chassis body piece on each processor. To this end, a new bushing-like element called “ModelLink“ has been made available with LMS Virtual.Lab Motion. With user-defined stiffness and damping properties, it is used to con-nect the different sections of a model which will be solved in parallel, ensuring the proper communications among all pro-cessor cores involved in the co-simulation (Figure 7).

Figure 6: LMS Virtual.Lab Motion Real-Time Solver: turnaround time per integration step.

LMS Virtual.Lab Motion Real-Time Solver features

Most current real-time solvers are explicit. Such solvers com-pute the system states at the next time step by only consid-ering the accelerations at the current (and possibly past) discretized time point. Although explicit schemes guarantee a deterministic computational load, the inclusion of compli-ant elements such as bushings can result in numerically stiff systems. In such cases the implicit schemes are preferred because of the schemes’ superior numerical stability and accuracy.

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 7

Virtual.LabMotion model

Solverinputfile 3

Solverinputfile 2

Solverinputfile 1

C-code.dll

C-code.dll

C-code.dll

Each section is then managed as a standalone model, for which a C-code version is generated (Figure 8-9). In addition to the description of the (sub)model, each C-version can also contain the solver code to run the model, such as on HiL sys-tems. C-code files exported from LMS Virtual.Lab Motion can then be compiled into multiple formats (DLL, EXE, Matlab S-functions MEXW32 files).

Adding 1D models of electric, hydraulic, pneumatic subsystemsSimulations with LMS Virtual.Lab Motion Real-Time Solver can also include 1D representations of vehicle subsystems. LMS Imagine.Lab Amesim software provides a large set of libraries for detailed modeling of mechanical, electric and hydraulic systems, together with a dedicated interface with LMS Virtual.Lab Motion to easily interface 3D and 1D models. Although standard applications do require the same integra-tion step (typically 1ms = 1kHz) to be used by all subsystems involved in the real-time co-simulation, LMS Virtual.Lab Motion Real-Time Solver capabilities have been expanded to also manage multi-rate simulations to cover specific require-ments from high-frequency subsystems such as engine com-bustion loads, for which a frequency rate up to 5kHz can be needed to reach the desired results accuracy.

Figure 8: Parallel solution process scheme.

Figure 7: Automobile model split for parallel solution on 2 processor cores.

Standardproduction

model

Make modelready for C-code

solver

Split model:add model link

Parallelsolverinputfiles

C-codesolver

Generate C-code Build C-code executablenmake /f makefile.ccode

Generateanimation results

(optionalpost processing)

C-codefile

Compile

.exe

SIL/HiL

Figure 9: Overall process for C-code generation.

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 8

Industrial validation examplesLMS Virtual.Lab Motion Real-Time Solver has been validated through several industrial scenarios.

A customer-based full-vehicle model (Figure 10) was used to test the new LMS Virtual.Lab Motion Real-Time Solver. Bushing compliances and full nonlinear kinematics of sus-pensions were taken into account. There were 146 DOFs determined by 181 bodies.

Figure 10: Full-vehicle MBS model.

The full model has been divided in two subsystems by split-ting the chassis part into a front and rear chassis, connected by a ModelLink bushing element to allow the solving paral-lelization process. The goal of this project was to validate handling scenarios, so a 10s J-turn maneuver was simulated through a kinematic step steering input. No driving torques were applied at the wheels, so vehicle speed was expected to decrease during the cornering maneuver.

Offline analysis results of the original high-fidelity model, calculated with the standard solver, were compared with those from the adapted model solved using LMS Virtual.Lab Motion Real-Time Solver. As shown by plots in Figure 11, all main handling characteristics were predicted with the same accuracy as offline simulation. A small difference can be seen only on the roll angle of rear chassis, due to the finite stiff-ness of the bushing element connecting the two halves of the chassis.

A second validation project, performed in a hybrid pow-ertrain control scenario, has confirmed the results. A detailed LMS Virtual.Lab Motion model was used to simulate a straight line standing start acceleration and deceleration event, with a focus on the longitudinal dynamics of the vehi-cle. A path-following steering control was used to follow a straight line, whereas the front axle was driven by a torque signal from physical tests. Also in this case, LMS Virtual.Lab Motion Real-Time Solver was proven to provide highly accu-rate results when compared with offline simulations.

Lateral acceleration Yaw rate Roll angle Vehicle speed

— Offline Real-time: — front chassis — rear chassis

Figure 11: Simulation results.

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 9

LMS Virtual.Lab Motion

EPAS

Powertrain ABS/ESC

LMS Imagine.Lab Amesim

DILinput

Steering input

Brakeinput

Throttleinput

Vehicle sensors

Targ

et

Figure 13: 1D-3D hybrid model performing in co-simulation on RT hardware.

More specifically, a 1D representation was used for the fol-lowing subsystems:

• Braking, including ABS/ESC control logics

• Electric powered steering assist

• Driveline

• High-frequency engine (running at a fixed integration step of 0.2ms)

The real-time co-simulation between LMS Virtual.Lab Motion and LMS Imagine.Lab Amesim has been evaluated in a driver-in-the-loop scenario, in which inputs from the driver (steering, throttle, brake, transmission gear) were used to drive the full-vehicle model to obtain real-time output data (streaming more than 120 channels in real-time to the oper-ating system) which could be used as input for a driving sim-ulator system (Figure 14).

Figure 12: Turnaround time for both processor cores. The real-time solution was performed using a DS1006 Quadcore dSPACE machine. The duration of the analysis was set to 20s, with a fixed integration time step equal to 1ms. For all time steps the turnaround time was lower than the 1ms time step, with still some margin left to allow for an increased complexity of the model (Figure 12).

A validation study on a driving simulator was also carried out for another customer, in which several subsystems, modeled as 1D representations using LMS Imagine.Lab Amesim, were simulated together with a detailed 3D full-vehicle LMS Virtual.Lab Motion model (Figure 13).

Driver-in-the-loop

Brakes

>100channels

EPAS

Real-time hardware

Transmission/differential

High frequency engine

Steerthrottlebrake

Chassis

LMS Imagine.Lab Amesim

LMS Virtual.Lab Motiondriving dynamics

CAE environment

Figure 14: Multi-rate 1D/multi-physics and 3D multi-body models performing on RT hardware.

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 10

ConclusionHardware-in-the-loop testing of ECUs for active control systems, as well as driving simulators, are typical applications in which real-time solv-ing capabilities are required.

Because of several breakthrough technologies, LMS Virtual.Lab Motion Real-Time Solver ena-bles you to run real-time simulations on detailed, high-fidelity MBS models, also sup-porting hybrid 3D-1D multi-physics, multi-rate systems running on most common real-time hardware platforms.

Model split

EPS

Real-timehardwarenot platform

specific

ABS and ESC

Powertrain

Modelgranularity

control

LMS Virtual.Lab Motion3D multi-body vehicle model• Handle up to 20Hz with ~160DOFs• Split models for parallel processing

LMS Imagine.Lab Amesim1D multi-physics system model• Create comprehensive systems with standard and validated libraries• Build and incorporate mechatronic systems• Manage model granularity depending on intended use

White paper | Real-time simulation of high-fidelity mechatronic systems

A white paper issued by: Siemens PLM Software. 11

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About Siemens PLM SoftwareSiemens PLM Software, a business unit of the Siemens Industry Automation Division, is a world-leading provider of product lifecycle management (PLM) software, systems and services with nine million licensed seats and 77,000 customers worldwide. Headquartered in Plano, Texas, Siemens PLM Software helps thousands of companies make great products by optimizing their lifecycle processes, from planning and development through manufacturing and support. Our HD-PLM vision is to give everyone involved in making a product the information they need, when they need it, to make the smartest decision. For more information on Siemens PLM Software products and services, visit www.siemens.com/plm.