The project Sustainable Action on Vehicle Energy (SAVE) at the University of Warwick is led by Professor Paul Jennings and Dr Peter Jones; leading the modelling programme is senior research fellow Dr Andy McGordon and post doctoral research fellow, Dr Caizhen Cheng. They are using Dymola, a multi-domain physical modelling and simulation tool, to study the impact of driver behaviour on vehicle emissions (especially from hybrid vehicles), asking such questions as “how do drivers cruise on a motorway?” and “how do we positively influence behaviour to reduce emissions?”

Vehicle emissions are currently evaluated using simple velocity/time simulations, but as Dr Andy McGordon says: “The problem with drive cycles, such as NEDC, which calculate drive cycle emissions is that no one actually drives like that. In fact it’s almost impossible to drive like the velocity to time simulation. When approaching a set of traffic lights the driver doesn’t think right I’m 20 seconds from stopping. Drivers judge in distance – I need to stop in 200 metres. Replacing time with distance is the first difference in our research.

“Where we’re headed is to be able to model the effect of real world driver behaviour, and integrate this into transport models as well as individual vehicle models. Current transport models, although they are effectively distance based simulations, do not include the effects of real world behaviour- each vehicle is effectively computer controlled and behaves in an idealistic manner. This can result in errors in predicted energy use.

Dr Caizhen Cheng is using Dymola to model the hybrid vehicle architecture. Previously researchers at Warwick have used the university’s own simulation tool called  WARPSTAR, which was a drive cycle-led model used to analyse fuel economy of hybrid vehicle architectures over a range of drive cycles. Dr Cheng explained: “The project team chose Dymola because of its ease of use and the power that it gives us in being able to model and simulate all systems in one tool, for example mechanical, electrical and control systems.

Dr McGordon said: “To include the driver model, the vehicle model has to become forward facing. We had the option to just make WARPSTAR forward facing within the Matlab Simulink environment or to make it forward facing and better in terms of  capability for these highly dynamic real world driving models.

Dr Cheng picked up the reasons why Dymola was chosen for the project: “We felt it would be better to use Dymola to do the physical modelling. It can give different fidelities for vehicle component modelling, and the models can be swapped easily to evaluate the effect of different fidelity components on the whole system.”

“I used to develop in Simulink, which is really time consuming, and also I need to verify everything because it has not been validated by other people, it has only been developed by me.  However in Dymola there are a lot of libraries which have been developed and validated by third parties.”

Dr McGordon continued: “There are other advantages through using Dymola. For example, there is continuous checking of the structure of the model  – it checks all the connections and the equations with the variables. Also, Dymola includes models of complex parts like clutches which were extremely difficult for us to build in WARPSTAR in the Simulink environment. We’ve actually integrated Dymola and Simulink so that they are complementary, and we get the best possible out of the physical model and the controller. And obviously for the driver we’re interested in the outputs from the controller.”

It only took Dr Cheng a few weeks to be able to competently master use of Dymola and just two days of formal training with Claytex. Dr Cheng continues: “On the project we have already saved so much time using Dymola. One big advantage is the libraries that are available. We have used the Powertrain and SmartElectric libraries. Libraries mean significant time savings.”

The project, which is viewed by research staff as medium term research looking out three to five years finishes in September 2011 and is being carried out in collaboration with Jaguar Land Rover, Arup  and Froude Hofmann. The project is supported by the Engineering and Physical Sciences Research Council (EPSRC), UK, through the Warwick Innovative Manufacturing Research Centre.

Dymola is provided to the University of Warwick by Claytex (www.claytex.com)

About SAVE
SAVE is a University of Warwick Innovative Manufacturing Research Centre (IMRC) project.

SAVE Project Synopsis
A project to create tools and aid decision-making for future eco-friendly vehicle technology, it is intended that this work will enhance understanding and promote reduction of energy usage in the transport sector.

For the first time a single modelling framework will be developed for all hybrid vehicle architectures which will link individual vehicle models with driver models, transport models and city electrical energy models.

Currently there is no modelling package that compares different vehicle architectures directly and that includes the capability to study the influence of real-world driver behaviour on energy usage. The project team will create such a capability and integrate it into the vehicle design process.

By developing ways of linking individual vehicle models with transport models and city electrical energy models, it is hoped that researchers may use the resulting knowledge to inform users on wider intelligent transport options using criteria such as minimum energy per person per kilometre. This will benefit not only manufacturers but will enable the optimal reduction of a city’s overall carbon footprint, reducing fossil fuel usage within vehicles and improving emissions of noxious gasses within city centres. Several potential methods for influencing driver behaviour will be considered.

SAVE Project Aims
•    To develop a family of Hybrid Vehicle powertrain models, exercised in a common structure (each having the degree of fidelity required to support a particular design decision).
•    To develop methodologies for control strategy optimisation of individual vehicles based upon likely real world fuel consumption and local and global emissions.
•    To link the vehicle models to transport and city energy models, enabling optimisation of fleet CO2 emissions.
•    To create a model which appropriately represents real world driver behaviour, and to integrate it into the vehicle design process.
•    To understand, assess and demonstrate options for the promotion of reduced energy usage and local emissions from individual vehicles and fleets of vehicles.
•    To disseminate the research results, ensuring that the research process is designed to maximise the impact of outputs across the IEV (intelligent and eco-friendly vehicle) sector.

Dymola is an important tool in all of the areas that the event addresses – everything from active safety, to the development of chassis control systems and hybrid vehicles.

But we’ll be doing much more than promoting Dymola. It’s a real opportunity to hear about new initiatives and blue-sky thinking, and a chance to swap ideas with some of the brightest engineers in the business. You can look at the programmes here.

We hope to see you there. I was introduced to vehicle control systems as a student at Loughborough University, so it’ll be good to be back at the old place and see how it’s changed.

AVEC10
22-26 August
Loughborough University, UK

The guide features case studies on some of the many Dymola success stories, gives a complete overview to the extensive libraries details industry applications.

To get your copy call Claytex on 01926 885900. Dymola product guide and brochure

This one day event is free to attend and brings together users and developers of Dassault Sytemes solutions. Use this opportunity to learn, share and collaborate through the domain specific DS and customer presentations, live technical showcase and plenary sessions.

Claytex will be available throughout the event to demonstrate the capabilities of Dymola at our stand within the exhibition area.  We will also be presenting during the afternoon technical sessions as part of the Simulation track.  The agenda is available here.

Register here to reserve your place and using the booking reference CLAMD.

We look forward to welcoming you to the DS PLM Forum 2010!

rFactor Pro is a high fidelity vehicle simulator with game-quality graphics and sound engines for human in the loop simulation.  The architecture of the simulator allows the physics engine to be replaced with models developed using Dymola. This enables the same vehicle models to be used in the design office and on the simulator.

The rFactor Pro product is the outcome of a major development partnership with a high budget racing team. The team demanded a simulator that is not only physically accurate, but which is also visually and aurally realistic. rFactor Pro creates detailed 3D models of tracks and vehicles and accurately models their vehicle dynamics. That means engineers and designers have a complete solution: virtualized engineering development, setup evaluation, driver training, race engineer training and strategy evaluation.

The component orientated physical modelling capability offered by Dymola enables the vehicle dynamics models to keep pace with even the fastest pace of racing development. The real-time simulation capabilities of Dymola then allow these multibody models to be compiled for simulation in real-time speeds - and with a full motion simulator. Any changes can be easily updated for use on the simulator, so the team can always work with the latest design iteration of the race car. For the engineers, the experience is better than actually driving the car, as they have continuous access to all the data they need.

Innovation like this wins championships.

Why is Dymola at MathWorks?
Many Simulink users also use Dymola. The two technologies complement one another very well, so are often used together. In fact, several Dymola customers are presenting here, so it’s good to catch up with what they’ve been doing.

How do Simulink users get business benefits from Dymola?
Simulink is very good for control system development but Dymola can often save time when modelling the physical parts of the system. For physial modelling, Simulink also has a relatively low complexity level: you can reach a limit beyond which it is very difficult to add more details to a model. With Dymola, it’s very easy to keep refining the model and adding more and more details.

So people are using Simulink and Dymola at the same time?
Yes – the interface is very straightforward. It’s very easy to import and export models from one platform to the other, and you can work on model parameters wherever you choose. Everything is also FMI-compliant. You can find more detailed information at http://www.claytex.com/dymola/simulink-interface/

What are the triggers that might make users reach out for Dymola?
The introduction of new technology into a system can make it very difficult to try and model in Simulink. It can also be very time consuming to try and derive the system equations as an ode for implementation in Simulink.

There might also be a desire to reuse the same physical model throughout the development process, so that everyone can use a common model. Or there could be a need to make the physical model more complex to help investigate a problem observed during testing.

Can you give an example of where Dymola and Simulink are being used together?
There are many. One of the highest profile is the SAVE project. Researchers at the University of Warwick are running a two-year research programme to simulate the ‘real-world’ impact of CO2 emissions and energy needs for vehicles using real world driver behaviour.

Who can I contact to find out more?
Talk with Mike Dempsey or any of his team. Mike is managing director of Claytex, a Dassault Systemes partner for Dymola multi-domain engineering modelling and simulation software in the UK, Belgium, Luxembourg, Netherlands, Ireland and South Africa.

Claytex offers software licenses, training and specialist simulation consultancy services to the aerospace, automotive and energy industries – so they can probably answer any query you may have!

All trademarks acknowledged.

In this 4th article in the series “Getting more from simulation” we will explore how component orientated physical modelling supports the extensive reuse of models to perform different analyses.

In earlier articles in this series we discussed how traditional modelling approaches developed around the use of block diagrams and programming languages were only able to represent one interpretation of a model for one type of analysis.  If alternative representations are required using these approaches then the model developer has to start again and redevelop the model for that purpose.

In contrast, Dymola allows you to create a model once and then reuse it in many different situations and to carry out different types of analysis.

In the model diagram below, we see an electric motor driving a load inertia via a gearbox that is operating in closed loop control to follow a set speed-time profile.  In this case an idealised voltage source is used to power the electrical circuit and the voltage is determined by the PID controller.  This model will enable us to calibrate the control system to achieve the desired response from the system.  This causes Dymola to solve the equations to generate a forward dynamic model, i.e. the mechanical system responds as torque is applied to it.

Suppose we want to approach the problem from a different perspective and we want to explore how the motor power requirement is affected by changes in the compliance of the mechanical system.  Using the model shown above this would be a time consuming process as the control system would need to be recalibrated for each change being considered.

Using Dymola we can explore these design changes by removing the control system from the model and using a special component that inverts the model equations.  Adding this block as shown below causes the output speed of the load inertia to follow the speed-time profile set in the smoothRamp and it calculates the voltage required to achieve this.  This causes Dymola to solve the equations in a different way and generates an inverse dynamic model, i.e. it calculates the torque required to follow a specified speed-time profile.

The physical model is reused without modification, all we have done is disconnect the existing control system model and add two new components to the model to actuate it in a different way.

Using this approach we can quickly evaluate the effect on the motor power requirement due to changes in the stiffness and damping characteristics of the gearbox.  In fact, we could use this approach to look at the effect of changing any parameter within the physical model on the power requirement.

We can also use this approach to learn and understand what our control system must do to achieve the target system response and design an appropriate algorithm.

To use this approach using traditional block diagram modelling tools or normally programming languages would require the model developer to completely rewrite the physical model from first principles.  This process is not only time consuming and error prone but it would also leave the engineer with two models to maintain and update each time the system is updated.

Through this series of 4 articles we have explored how using a component orientated physical modelling approach can accelerate the model development process and deliver improvements in efficiency through model reuse.

If you would like to learn more about Dymola visit www.claytex.com

Written by Mike Dempsey, Claytex

In this 3rd article in the series “Getting more from simulation” we will compare the component orientated, physical modelling approach used in Dymola with the traditional block diagram modelling approach used in tools like Simulink.Dymola supports a component orientated approach to modelling where each object in the model diagram represents a physical part in the system.  Simulink, on the other hand, supports a block diagram approach to modelling that requires the user to manually rearrange the equations defining the system in to a block diagram.  The block diagram is then a graphical representation of the equations solved to perform a specific calculation.

The example below illustrates a simple system model built in Dymola.  The system consists of an electric motor connected to a 1D rotational inertia and a load inertia via a gearbox.  The voltage supplied to the motor is determined using a PID controller that aims to match the rotational speed of the load inertia to the set-point defined in the step function.

To create the same diagram using a block diagram approach would require the modeller to first determine all the equations required to model each part of the system and to then manually rearrange these in to the correct calculation order.  Using Dymola, the process of rearranging the equations is taken care of automatically using symbolic manipulation.  Working through this process would yield the following block diagram.

The two models shown above are identical and produce the same results.The real advantage of the component orientated modelling approach  can be realised if we consider what would happen at the next stage of the project.  Typically, as a project progresses the models need to include more detail to support the next phase of development.  In this example, a simple enhancement that would need to be made to the model is to account for compliance in the gearbox as this would introduce oscillations during transients between the speed set-points.

To make this change using Dymola and the component orientated modelling approach we would simply drag and drop a spring-damper block from the library in to the model and connect it between the gearbox and load inertia as shown below.  This change can be accomplished very quickly and the new model can be available in matter of seconds.

To update the block diagram version of the model you would first have to discard half of the model from the first stage because this solution to the model equations doesn’t include the compliance in the gearbox. The model developer has to determine the new equations for the compliant system and create the block diagram highlighted in red.  This will take even an expert mathematical  modeller time to complete and open the door to potential manual error,  whereas the alteration using Dymola could be achieved quickly without detailed knowledge of the mathematics required.

The traditional approach described here has been born from the requirement for engineers to do more simulation so that businesses can reduce costs.  Engineers rightly choose a tool they were familiar with and had access too, in Simulink.  The concern is that this tool was designed for the very specific purpose of designing control systems.  It does it very well and is why Dymola has a seamless interface with the software and can appear as a Simulink block in any model.

Dymola is however the best tool for simulation.  The reason is that, as Simulink was designed from the ground up for control system engineers, Dymola was designed from the ground up for simulation engineers.  Simulation happens across every functional department within a development team (mechanical, hydraulic, pneumatic, electrical, systems etc) and so Dymola is capable to serve every functional department within a development team too.In the next article in this series we will explore how the component orientated modelling approach enables the extensive reuse of models to perform different types of analysis.

The next article in the series is available here.

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Contact
Mike Dempsey
www.claytex.com
Tel: 01926 885900

Written by Mike Dempsey, Claytex

In this 2nd article in the series “Getting more from simulation” we explore how the component orientated, physical modelling approach used in Dymola simplifies model creation and accelerates the model development process.

What a component orientated modelling approach means in practice is that each object in the model diagram represents a real physical part in the system (e.g. a pipe, resistor or gearbox).  The Dymola modelling environment frees the engineer to focus on what’s important (i.e. designing and developing the overall system and specifying the individual components) rather than being forced into wasting time developing and debugging an abstract block diagram representing one interpretation of the system.  With Dymola there is no requirement for the user to waste time manually rearranging equations, it’s all handled within the next-generation symbolic manipulation engine which comes as standard.

Model creation in Dymola is performed using a drag-and-drop approach.  Component models from all engineering domains are available in libraries (shown on the left hand side in the screenshot below) and by simply dragging and dropping the required components in to the model diagram the schematic of the system can be created.

The system consists of an electric motor connected to a 1D rotational inertia and a load inertia via a gearbox.  The voltage supplied to the motor is determined using a PID controller that aims to match the rotational speed of the load inertia to the set-point defined in the step function.

Components from different engineering domains have different types of connectors that represent the appropriate physical connection between the components.  For example, the connection between the motorInertia and gearbox is a 1D rotational mechanical connection and defines the relationship between the angle of rotation of the two components and the torque acting on them.  The connections are acausal which means there is no definition of the signal flow in the connection.

To set the component parameters, simply double click on the part and a dialog box showing the parameters appears.

One the model schematic has been created and the parameters specified we can simulate the model.  The first step in the simulation process is for Dymola to transform the model diagram in to efficient simulation code which it does through a process known as symbolic manipulation.  To appreciate what symbolic manipulation does, we first need to explain how a component model is defined in Dymola.

Dymola uses the Modelica modelling language to define components.  Modelica is an open standard modelling language designed for component orientated modelling and is developed and maintained by the Modelica Association.  In the above system model we have used 1D rotational inertias (called motorInertia and load in the diagram) and the Modelica definition of this component is shown below.  Modelica is a fully object orientated modelling language and you can find out more at http://www.claytex.com/dymola/modelica-modelling-language/

The most important feature of Modelica for this discussion is to notice how the model equations are actually written.  The final equation implements Newtons 2nd Law in an instantly recognisable form, i.e. the sum of the torques acting on the object is equal to its inertia times acceleration.  Modelica supports equations of the form expression = expression rather than the traditional programming approach that only supports equations of the form variable = expression.

To transform the system model from Modelica code in to efficient simulation code, Dymola uses symbolic manipulation.  Symbolic manipulation applies computer algebra techniques to automatically rearrange the model equations in to the required solution.  The animation below explains the basic steps of this process:

In reality, symbolic manipulation is able to deliver more than simply rearranging the model equations in to the appropriate solution.  It also simplifies the resulting system of equations through the application of advanced mathematical techniques to handle linear and non-linear systems of equations.  Using our example system model, symbolic manipulation manipulates the equations as follows:

Original model
Total number of equations:    86
Sizes of linear systems of equations:    4
Translated model (equations to be calculated at each time step)
State variables:    7
Time-varying variables:    23
Sizes of linear systems of equations:    0

Symbolic manipulation has been able to reduce the size of the model from 86 equations to just 30 equations that need to be calculated at every time step and in whilst achieving this reduction it has also been able to eliminate the linear system of equations.  This reduction in the number of equations to be calculated at each time step will deliver improvements in simulation performance.

In the next article in this series we will look at how the component orientated modelling approach compares to the traditional block diagram modelling approach, this can be found here.

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Contact
Mike Dempsey
www.claytex.com
Tel: 01926 885900

By Mike Dempsey

It’s time simulation engineers started using the right tool for the job.  One of the problems of using traditional simulation tools has been knowing how to interpret what happens in reality and translate that into the abstract representations needed for use in simulation tools.

Through this series of articles we will explore the Dymola approach to modelling and simulation to illustrate the key differences and advantages that can be realised by approaching the modelling tasks from a new perspective.  This article will provide an overview of the modelling approach used by Dymola and subsequent articles will contrast this with a traditional block diagram or programming approach and explore the benefits this new approach offers.

Models in traditional simulation tools, such as Simulink, or that are written by hand in C-code or Fortran,  typically represent one interpretation of a real system and can only be used to simulate one condition so when an engineer needs to evaluate another condition a totally new simulation model is required.   For example, if a vehicle model has been developed to calculate the engine speed and load operating points over a drive cycle then, typically, it could not be used to predict how the vehicle will respond to a driver input.  A new model would have to be created for this task.

The result for the engineer of using these cumbersome methods is more work in a) building all the different models, and b) in keeping them all up to date. The result for the business are bespoke models produced to the individual engineer’s interpretation of the system, that are often difficult for others to interpret and limited in usefulness and accuracy by the engineers time, skill and capability.

Of most significance to both the individual engineer and the business is that sharing and maintaining models between individuals, inside and outside of the immediate team (including different functional teams or with customers and suppliers) is cumbersome at best, impossible at worst.

The Dymola approach is different.  Dymola, a Dassualt Systemes software product, supports a component orientated, physical modelling approach.  What this means in practice is that each object in the model diagram represents a real physical part in the system (e.g. a pipe, resistor or shaft, etc.) and that component and system models can be reused to carry out many different types of analysis as we will explore in the next articles.

The Dymola modelling environment frees the engineer to focus on what’s important (i.e. designing and developing the overall system and specifying the individual components) rather than being forced into wasting time developing and debugging an abstract block diagram representing one interpretation of the system.  With Dymola there is no requirement for the user to employ vast amounts of resource and time manually rearranging equations, it’s all handled within the next-generation symbolic manipulation engine which comes as standard.

In the next article in this series we will look at how the component orientated modelling approach simplifies the model creation process and how symbolic manipulation transforms this in to efficient simulation code, this can be found here.

Contact
Mike Dempsey
www.claytex.com
Tel: 01926 885900