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Integrated simulation of a e4WD vehicle using Modelica
7th IFAC Symposium on Advances in Automotive Control The International Federation of Automatic Control September 4-7, 2013. Tokyo, Japan
Integrated simulation of a e4WD vehicle using Modelica J. Andreasson*, D. Andersson**, J. Batteh***, J. Gohl****, J. Griffin***** I. Krueger******
*Modelon, SE-22370 Lund, Sweden (Tel: +46-462862200; e-mail: [email protected]) **Modelon, SE-22370 Lund, Sweden (e-mail: [email protected]) ***Modelon, Ann Arbor, MI 48104, USA (e-mail: [email protected]) **** Modelon, Ann Arbor, MI 48104, USA (e-mail: [email protected]) ***** Modelon, SE-22370 Lund, Sweden ([email protected]) ****** Modelon, Hamburg, Germany (e-mail: [email protected])
Abstract: In this work, a Modelica representation of an e4WD vehicle is derived and evaluated for a range of different aspects; including launch performance, lateral stability, thermal management, and driver-in-the-loop performance. The paper explains how the modeling approach supports adaption of subsystem fidelity and system focus to fit the different aspects, and different engineering domains. Keywords: hybrid vehicles, engine management, vehicle dynamics, automotive emissions
introduced too late, prediction granularity is lost. We refer to this capability as multi-fidelity. There are also different engineering perspectives to take into consideration; an electrical engineer may want detailed models of the electrical systems and a simple representation of the rest of the vehicle, whereas other engineers have different focus. We refer to this as multi-perspective.
1. INTRODUCTION The introduction of hybrid powertrains is mainly motivated by improved fuel economy and reduced environmental impact, while increased cost and complexity are the main drawbacks. It also opens up for several new potential advantages to create added value such as improved performance and driver experience. There is also potential to save cost due to down-sizing the engine. A hybrid-electric powertrain can be seen as a first step towards electric cars, so engineering know-how in this domain may also be considered to be of strategic importance in a wider scope.
Finally, the toolchain must be able to support the different engineering activities during the development process, such as virtual experimentation, concept innovation, design space exploration, robust design, performance optimization, database interaction, hardware/driver-in-the-loop evaluation. This requires that the models can be exported outside the original environment. We refer to this as deployment.
From an engineering perspective, the development of hybrids requires the interaction between additional subsystems to be understood and optimized. Compared to a conventional vehicle, the additional degrees of freedom for hardware configuration, component sizing and control design pose additional challenges. For a model-based approach, this requires that the tool chain is able to handle complex systems consisting of physics from several different domains, we refer to this as multi-domain. Take the automatic engine start-stop functionality as an example. It must be swift enough to not annoy the driver, and the strategy must take into account the impact on engine temperature and after treatment system as well as the climate comfort of the passengers. This engineering problem involves the electrical system, the mechanics and the combustion process of the engine, the cooling and air conditioning cycles, and the control logic.
In this work, Modelica and FMI technologies (described briefly in Section 2) are used to address these requirements by an integrated model-based engineering approach for the hybrid adoption of the vehicle illustrated in Figure 1. The paper is laid out so that Section 3 explains the vehicle model and architecture, Section 4-6 explains how the multi-domain, -fidelity, and -perspective criteria are met by examples from engine downsizing, thermal management, and performance. Section 7 explains some deployment options and Section 8 finally summarizes the work.
A second but just as important requirement on the tool chain is the ability to support interaction of models with different fidelity levels; if you bring in a too complex model too early in the design process, you will not be able to populate the model with adequate parameters. The additional engineering work required to generate these parameters essentially means that the model-based support is introduced later in the process. If on the other hand fidelity is neglected or 978-3-902823-48-9/2013 © IFAC
Figure 1. The complete e4WD vehicle model with different subsystems indicated. 440
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IFAC AAC 2013 September 4-7, 2013. Tokyo, Japan
2. MODELICA AND FMI The basic idea of Modelica stems from the 1970’s, Elmqvist (1978), and was to use general equations, objects, and connections to allow model developers to look at modelling from a physical perspective, instead of a mathematical one. This allows for reconfigurable, component-oriented modelling of complex multi-domain systems. The approach has been used in the automotive industry for a long time, e.g. Soejima (2000), and since the mid 1990’s, efforts have been unified under Modelica association. As a result, a common language definition is available that is being supported by an increasing number of tools, which make Modelica models and model libraries to be accessible for a larger audience. In this work Dymola is used, Dymola (2012).
Figure 2 Vehicle-centered view of the vehicle architecture used. Table 1. Subsystem fidelity levels
Engine
Mean value engine model with air path
Map based
Transmission
Composed of gears, clutches and electric
Power transfer based on gear ratio (fixed
Multi-body representation with elastic components
Longitudinal motion only
Electrical systems
Thermal-dependent losses
Constant loss factors
Thermal
Thermal dynamics and coolant loops
Constant temperature
For the transmission model, the low-fidelity representation is a power transfer that follows a desired gear ratio input. This means that the model can be used to represent both conventional and continuously variable transmissions. For hybrid configurations, the model is completed with a component that applies electric power or torque. No particular parameterization except gear ratio and losses is required which makes the model easy to deploy in early stages. The model is also suitable when no detailed resolution of shift dynamics is required. The detailed level model contains separate models for the gears, clutches and electric machines as illustrated in Figure 3. In this work, two different hybrid layouts were investigated (top and middle), the dual clutch and standard conventional transmission were used for the original vehicle without hybrid drive.
The vehicle model used in this work has been implemented using the Modelica Vehicle Dynamics Library. It is composed as a hierarchy with subsystem representation for engine, transmission, driveline, chassis, and brake system as illustrated in Figure 2. The model architecture allows for interchange of individual subsystems and components, both to change configuration and complexity level. Thus, the same model is used in the sequel, with different subsystem configurations. For each subsystem, two levels of fidelity are used as summarized in Table 1.
Low-fidelity
Chassis
The low-fidelity model comes from VDL and is defined by a map that relates throttle position and RPM to engine torque and fuel consumption. Air path dynamics is approximated by a first order filter on throttle position, and no calculation of emissions is included.
3. VEHICLE MODEL
High-fidelity
or variable)
The high-fidelity engine representation is based on a Mean Value Engine Model (MVEM) from the Modelica Engine Dynamics Library (EDL). This model allows for detailed investigation of engine and exhaust temperatures which allows for integrated control design of energy and thermal management. Dahl (2012) gives a more detailed description of the implementation and validation.
The FMI standard, FMI (2013), defines a standardized interface to simulation models, where a model defined according to the standard are referred to as an FMU. The standard was initiated by Daimler in order to be able to combine models from different simulation tools to perform integrated simulations. The standard supports both FMUs with built-in integrators for co-simulation and/or stand-alone application, and FMUs that can be simulated with external integrators for integration in other simulation environments, referred to as model exchange FMU.
Subsystem
machines
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of this system is the heat exchanger stack which exchanges the heat generated by the vehicle components (engine, transmission, battery, etc.) with the ambient air. The model also includes a parameterized model of the air flow through the stack that is based on multiple boundary conditions. In this model these boundary conditions include the vehicle speed and fan speed. The heat from the vehicle components is carried to the heat exchangers by fluid media that includes the fluid flow dynamics due to the fluid circuit. It also includes the fluid pumps, restrictions, thermostat, etc. This is shown in the Figure 5. Additional heat transfer occurs between the components and ambient through normal underhood and under-body conduction/convection. Finally active controls for cooling system components like the fan and grill shutters are also included. This allows the controls engineer to develop the control systems using a full model-based design approach.
Figure 3. High-fidelity transmission models: Prius topology (top), Alison topology (middle), and dual clutch transmission (bottom). The driveline of the original vehicle is a traditional rear axle drive layout. An additional e4WD variant was completed with an electric motor that drives the front wheel via a differential and two shafts, Figure 4.
Figure 4. Driveline layout with front electric drive and rear conventional drive. The high-fidelity chassis model is the same as for the original vehicle, with full geometric and compliance characteristics. This model is able to capture the chassis and tire contribution to the handling and drivability simulations. Since the parameterization was already available the choice was easy. In the case of a new development without detailed suspension data available, behavioral representations of the suspensions may be a better choice. For the low fidelity chassis model, only the longitudinal degree of freedom is used and suspension and tire slip dynamics are neglected.
Figure 5. Thermal centered layout of the vehicle, here everything from Figure 2 is lumped into one component, which gives a clearer overview from the thermal management perspective 4. ENGINE DOWNSIZING The primary driver for the introduction of hybrid technology in vehicles is increased fuel efficiency; by means of energy recovery, ability to have the engine operate under better conditions and allowing for the engine to be dimensioned after mean instead of peak power, so called downsizing. The size matching of engine, electric machines and battery is a decision that has significant impact on the over-all vehicle design and can range from an integrated starter-generator to ability to drive in zero-emissions mode. However, already the decision to select hybrid-electric technology is supported by engineering work; other alternatives are for example supercharging and flywheel.
The low-fidelity electrical system uses a generic buffer with constant efficiency for charge/discharge and electrical machines with behavioural parameterization such as rated torque and power. This allows for straightforward parameterization allowing elaboration on dimensioning without having to consider and/or understand electrical systems in more detail. The high fidelity model includes a model of the battery stack, allowing for studies on how usage and temperature affects efficiency and wear. The low-fidelity thermal system provides constant temperature to all temperature dependent components, and as such it represents an ideal cooling circuit. The high-fidelity model is depicted in Figure 5 and contains a system representation of the thermal system of the full vehicle, including cooling of the engine, transmission and electrical system.
To compare these three approaches, a mean-value engine model (MVEM) from the Engine Dynamics Library is used as a base, and it is configured with 1) turbo charger with electrical support, so called e-boost, 2) an integrated flywheel and 3) a motor/generator with battery.
The thermal system itself includes all the main components of a Vehicle Thermal Management (VTM) system. The core 442
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Figure 7. Accumulated fuel consumption (left) and state of charge (right) for configurations 1 (thick blue), 2 (dashed red) and 3 (green). 5. THERMAL MANAGEMENT As shown in section 3, a multi-perspective approach is possible for the models. As illustrated by the thermal management layout, the complete vehicle and thermal dynamics can be modelled together. This allows closed-loop simulations of the vehicle dynamics together with the thermal system dynamics. The model however is structured with the particular domain of interest at the top level (multiperspective). In this case, since the focus of the thermal system engineer is on the thermal system components, these reside at the top level of the model. The rest of the vehicle systems (including a driver model) exist but remain at lower levels of the model. A portion of this hierarchy is shown in the Figure 8.
Figure 8. Vehicle Thermal Management model hierarchy for the e4WD
Figure 6. Engine model with three different configurations for engine downsizing comparison; turbo with e-boost (top), flywheel (middle) and electric machine integrated at the crank-shaft (bottom).
As an illustration of the capability of this type of model, closed loop drive cycle simulations were performed. Drive cycles are often used for fuel economy studies but are also suited for thermal management design. In these studies the driver model follows a vehicle longitudinal speed trace which generates heat within the vehicle component models. This heat must be managed by the cooling system.
Figure 7 shows simulation results of repeated EUDC cycles. Here, since configurations 2 and 3 have the ability to regenerate brake energy, fuel consumption is better as expected. Configuration 2 performs better than 3 since the losses of the flywheel are lower than the electric machinebattery combination. The battery adds a significant contribution to the losses as the power output enforces the high internal currents in the battery. This suggest to either increase the battery or to complement with a more dynamic buffer, e.g. a super capacitor.
Since the focus of the simulations is the thermal system performance, the high-fidelity thermal system model is used. Also, since high frequency vehicle dynamics do not contribute appreciably to heat generation (note that the suspension dampers are not connected to the cooling system in this version of the thermal system) the low-fidelity chassis model is adequate for these tests. Recall that the low fidelity chassis model includes only longitudinal vehicle dynamics, as described in section 3. The powertrain in these simulations is the conventional rear wheel drive with front wheel electric assist (e4WD). The tests compare alternate configurations of the thermal management system. In the first case the high voltage 443
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electric battery is assumed to be passively cooled through convective heat transfer with the vehicle under-body. In the second case, the high voltage battery is isolated from the under-body but includes an active fluid coolant loop in the heat exchanger stack. This slight difference can create some unexpected results, illustrating the benefit of model-based design.
the increase in battery temperature in addition to its heat generation. Second, this had an unexpected result of affecting the transmission oil temperature since the transmission is also connected to the under-body. Third, the battery SOC benefitted from the reduced temperature for the actively cooled system and remained higher through the simulation. 6. DRIVING PERFORMANCE
Figure 9 show some of the results of these simulations using the US EPA FTP (2013) drive cycle. This drive cycle was chosen because it includes moderate speeds and frequent start/stop. In these figures the blue trajectories are the passively cooled version and the red trajectories are the actively cooled version as described above.
By the introduction of partial electric propulsion, new possibilities to improve the driving experience open up. For the vehicle in this work, where the additional electric four wheel drive is considered, acceleration should consider traction blending of combustion engine and electric machines, as well as front/rear torque distribution. Correspondingly for a deceleration event, there is a split between mechanical friction brakes and electric machines. Common for both cases is that the blending must fit with the over-all energy management strategy and fulfil requirements with respect to drivability, performance, and safety. In this Section, launch and acceleration analysis is used to illustrate the evaluation; other examples can be found in Griffin (2012). The experiment is performed on a skid-pad with 50m radius and the friction coefficient lowered to 70% of nominal. The vehicle launch event is set up with a driver that starts from stand still with applied brakes, by releasing the brakes and then pressing the accelerator pedal. In this case, the engine is in idle mode but also start-stop functionality can be evaluated using the same set-up, see e.g. Griffin (2012). Figure 10 shows a screen shot from the event. Here, acceleration and velocity profile constitutes the key performance indicators, while jerk (acceleration change rate), response time and peak acceleration are key indicators for drivability and comfort. Figure 11 compares the original rear wheel drive vehicle with the e4WD version. As seen from the acceleration and velocity profile, the e4WD outperforms the original vehicle, mainly due to improved grip, and significantly more available torque from stand-still. Additionally, the feel of the e4WD will be much better, as it provides faster response time, smoother acceleration with lower peak, and thereby also a lower jerk.
Figure 9. Collection of various results from FTP drive cycle simulation for the e4WD Vehicle Thermal Management model There are a few interesting results to note. First, since the battery was connected to the under-body for the passively cooled version, heat (which can flow in either direction) flowed to the battery during the simulation. This contributed
Figure 10. Animation snap shot from the driving performance event; vectors indicate tire forces.
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any simulation tool. In this case it is run from Excel using the FMI add-in for Excel, FMI Add-in (2013). This allows a component designer to evaluate the performance of different component sizes without the need of the full Modelica tool. The last example considers the import of a full vehicle FMU to a driver-in-the-loop-environment using FMI Library, FMI Library (2013). The vehicle model is compiled with a fixedstep integrator that is in-lined with the code for maximum efficiency. As a result, complex models with high-fidelity can be run in real-time on the target platform.
Figure 11. Comparison of launch and acceleration performance in a curve with radius 50m and 70% road friction for the original vehicle (thin lines) and the e4WD vehicle (thick lines). Left: velocity [m/s], right: acceleration [m/s2] (solid) and jerk [m/s3] (dashed).
8. SUMMARY This paper has illustrated how Modelica and FMI technologies is and can be used the automotive industry to allow for an integrated view of many different aspects of vehicle development, illustrated by the addition of electric front axle drive to a conventional rear axle drive vehicle. Here, the following capabilities of the technologies key enablers:
7. DEPLOYMENT Since Modelica tool technology includes features like symbolic manipulation and index reduction, the original DAE model can be presented as an index-1 ODE to solvers. Thus, the generated models are suitable for integration with other models, both using model export and co-simulation. The ability to export models outside the original tool also opens up for a wider deployment of the models within an organization.
In this work, deployment was addressed using FMI compliant tools. Figure 12 shows an overview of the deployment schemes used in this work. For the extended control design of the motor management, the vehicle model with driver is imported as a plant model in Simulink® using the FMI Toolbox for MATLAB®, FMI Toolbox (2013). This allows for control designers to access a high-fidelity plant model to test and evaluate their designs while remaining in the environment familiar to them, and without having to learn or understand an additional tool. Furthermore, the controller from Simulink® can be exported as an FMU for import in the Modelica tool using the same tool.
To cover several physical domains and their interaction in the same system model, To create plug and play compatible component and system models, To design system architectures, To perform component based validation, To work with different fidelity levels within the same framework, and To export models to other tools and applications. REFERENCES
Dahl, J., Andersson, D. (2012) Gas exchange and exhaust condition modeling of a diesel engine using the Engine Dynamics Library In 9th International Modelica Conference Dymola (2012) Dymola web page, www.dymola.com Elmqvist, H. (1978) A Structured Model Language for Large Continuous Systems, PhD Thesis ISRN LUTFD2/TFRT-1015--SE, Department of Automatic Control, Lund University, Sweden, May 1978 FMI (2013) FMI standard www.fmi-standard.org FMI Toolbox (2013) FMI Toolbox for MATLAB® www.modelon.com/products/fmi-toolbox-for-matlab/ FMI add-in (2013) FMI Add-in for Excel® www.modelon.com/products/fmi-add-in-for-excel/ Griffin, J., Batteh, J., Andreasson, D. (2012) Drivability using Vehicle Dynamics Library In 9th International Modelica Conference Krueger, I., Limperich, D., Schmitz, G. (2012) Energy consumption of battery cooling in hybrid electric vehicles, In International Refrigeration and Air Conditioning Conference at Purdue Modelica (2012) Modelica Association web page www.modelica.org Soejima (2000) Examples of Usage and spread of Dymola within Toyota In International Modelica Workshop, Lund, Sweden. US EPA FTP (2013) United States Environmental Protection Agency web page www.epa.gov/otaq/standards
Figure 12. Deployment scheme, including model operation from Excel using co-simulation (bottom left), export and import to and from MATLAB®/Simulink® (top right) and rFactor Pro (bottom right). Another example of when the user neither can nor wants to understand the details of a model or the simulation tool is an engineer that needs to know what effects a design change would have on the vehicle behavior. By using a stand-alone As an FMU, the model can also be simulated separately from 445
Integrated simulation of a e4WD vehicle using Modelica J. Andreasson*, D. Andersson**, J. Batteh***, J. Gohl****, J. Griffin***** I. Krueger******
*Modelon, SE-22370 Lund, Sweden (Tel: +46-462862200; e-mail: [email protected]) **Modelon, SE-22370 Lund, Sweden (e-mail: [email protected]) ***Modelon, Ann Arbor, MI 48104, USA (e-mail: [email protected]) **** Modelon, Ann Arbor, MI 48104, USA (e-mail: [email protected]) ***** Modelon, SE-22370 Lund, Sweden ([email protected]) ****** Modelon, Hamburg, Germany (e-mail: [email protected])
Abstract: In this work, a Modelica representation of an e4WD vehicle is derived and evaluated for a range of different aspects; including launch performance, lateral stability, thermal management, and driver-in-the-loop performance. The paper explains how the modeling approach supports adaption of subsystem fidelity and system focus to fit the different aspects, and different engineering domains. Keywords: hybrid vehicles, engine management, vehicle dynamics, automotive emissions
introduced too late, prediction granularity is lost. We refer to this capability as multi-fidelity. There are also different engineering perspectives to take into consideration; an electrical engineer may want detailed models of the electrical systems and a simple representation of the rest of the vehicle, whereas other engineers have different focus. We refer to this as multi-perspective.
1. INTRODUCTION The introduction of hybrid powertrains is mainly motivated by improved fuel economy and reduced environmental impact, while increased cost and complexity are the main drawbacks. It also opens up for several new potential advantages to create added value such as improved performance and driver experience. There is also potential to save cost due to down-sizing the engine. A hybrid-electric powertrain can be seen as a first step towards electric cars, so engineering know-how in this domain may also be considered to be of strategic importance in a wider scope.
Finally, the toolchain must be able to support the different engineering activities during the development process, such as virtual experimentation, concept innovation, design space exploration, robust design, performance optimization, database interaction, hardware/driver-in-the-loop evaluation. This requires that the models can be exported outside the original environment. We refer to this as deployment.
From an engineering perspective, the development of hybrids requires the interaction between additional subsystems to be understood and optimized. Compared to a conventional vehicle, the additional degrees of freedom for hardware configuration, component sizing and control design pose additional challenges. For a model-based approach, this requires that the tool chain is able to handle complex systems consisting of physics from several different domains, we refer to this as multi-domain. Take the automatic engine start-stop functionality as an example. It must be swift enough to not annoy the driver, and the strategy must take into account the impact on engine temperature and after treatment system as well as the climate comfort of the passengers. This engineering problem involves the electrical system, the mechanics and the combustion process of the engine, the cooling and air conditioning cycles, and the control logic.
In this work, Modelica and FMI technologies (described briefly in Section 2) are used to address these requirements by an integrated model-based engineering approach for the hybrid adoption of the vehicle illustrated in Figure 1. The paper is laid out so that Section 3 explains the vehicle model and architecture, Section 4-6 explains how the multi-domain, -fidelity, and -perspective criteria are met by examples from engine downsizing, thermal management, and performance. Section 7 explains some deployment options and Section 8 finally summarizes the work.
A second but just as important requirement on the tool chain is the ability to support interaction of models with different fidelity levels; if you bring in a too complex model too early in the design process, you will not be able to populate the model with adequate parameters. The additional engineering work required to generate these parameters essentially means that the model-based support is introduced later in the process. If on the other hand fidelity is neglected or 978-3-902823-48-9/2013 © IFAC
Figure 1. The complete e4WD vehicle model with different subsystems indicated. 440
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2. MODELICA AND FMI The basic idea of Modelica stems from the 1970’s, Elmqvist (1978), and was to use general equations, objects, and connections to allow model developers to look at modelling from a physical perspective, instead of a mathematical one. This allows for reconfigurable, component-oriented modelling of complex multi-domain systems. The approach has been used in the automotive industry for a long time, e.g. Soejima (2000), and since the mid 1990’s, efforts have been unified under Modelica association. As a result, a common language definition is available that is being supported by an increasing number of tools, which make Modelica models and model libraries to be accessible for a larger audience. In this work Dymola is used, Dymola (2012).
Figure 2 Vehicle-centered view of the vehicle architecture used. Table 1. Subsystem fidelity levels
Engine
Mean value engine model with air path
Map based
Transmission
Composed of gears, clutches and electric
Power transfer based on gear ratio (fixed
Multi-body representation with elastic components
Longitudinal motion only
Electrical systems
Thermal-dependent losses
Constant loss factors
Thermal
Thermal dynamics and coolant loops
Constant temperature
For the transmission model, the low-fidelity representation is a power transfer that follows a desired gear ratio input. This means that the model can be used to represent both conventional and continuously variable transmissions. For hybrid configurations, the model is completed with a component that applies electric power or torque. No particular parameterization except gear ratio and losses is required which makes the model easy to deploy in early stages. The model is also suitable when no detailed resolution of shift dynamics is required. The detailed level model contains separate models for the gears, clutches and electric machines as illustrated in Figure 3. In this work, two different hybrid layouts were investigated (top and middle), the dual clutch and standard conventional transmission were used for the original vehicle without hybrid drive.
The vehicle model used in this work has been implemented using the Modelica Vehicle Dynamics Library. It is composed as a hierarchy with subsystem representation for engine, transmission, driveline, chassis, and brake system as illustrated in Figure 2. The model architecture allows for interchange of individual subsystems and components, both to change configuration and complexity level. Thus, the same model is used in the sequel, with different subsystem configurations. For each subsystem, two levels of fidelity are used as summarized in Table 1.
Low-fidelity
Chassis
The low-fidelity model comes from VDL and is defined by a map that relates throttle position and RPM to engine torque and fuel consumption. Air path dynamics is approximated by a first order filter on throttle position, and no calculation of emissions is included.
3. VEHICLE MODEL
High-fidelity
or variable)
The high-fidelity engine representation is based on a Mean Value Engine Model (MVEM) from the Modelica Engine Dynamics Library (EDL). This model allows for detailed investigation of engine and exhaust temperatures which allows for integrated control design of energy and thermal management. Dahl (2012) gives a more detailed description of the implementation and validation.
The FMI standard, FMI (2013), defines a standardized interface to simulation models, where a model defined according to the standard are referred to as an FMU. The standard was initiated by Daimler in order to be able to combine models from different simulation tools to perform integrated simulations. The standard supports both FMUs with built-in integrators for co-simulation and/or stand-alone application, and FMUs that can be simulated with external integrators for integration in other simulation environments, referred to as model exchange FMU.
Subsystem
machines
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of this system is the heat exchanger stack which exchanges the heat generated by the vehicle components (engine, transmission, battery, etc.) with the ambient air. The model also includes a parameterized model of the air flow through the stack that is based on multiple boundary conditions. In this model these boundary conditions include the vehicle speed and fan speed. The heat from the vehicle components is carried to the heat exchangers by fluid media that includes the fluid flow dynamics due to the fluid circuit. It also includes the fluid pumps, restrictions, thermostat, etc. This is shown in the Figure 5. Additional heat transfer occurs between the components and ambient through normal underhood and under-body conduction/convection. Finally active controls for cooling system components like the fan and grill shutters are also included. This allows the controls engineer to develop the control systems using a full model-based design approach.
Figure 3. High-fidelity transmission models: Prius topology (top), Alison topology (middle), and dual clutch transmission (bottom). The driveline of the original vehicle is a traditional rear axle drive layout. An additional e4WD variant was completed with an electric motor that drives the front wheel via a differential and two shafts, Figure 4.
Figure 4. Driveline layout with front electric drive and rear conventional drive. The high-fidelity chassis model is the same as for the original vehicle, with full geometric and compliance characteristics. This model is able to capture the chassis and tire contribution to the handling and drivability simulations. Since the parameterization was already available the choice was easy. In the case of a new development without detailed suspension data available, behavioral representations of the suspensions may be a better choice. For the low fidelity chassis model, only the longitudinal degree of freedom is used and suspension and tire slip dynamics are neglected.
Figure 5. Thermal centered layout of the vehicle, here everything from Figure 2 is lumped into one component, which gives a clearer overview from the thermal management perspective 4. ENGINE DOWNSIZING The primary driver for the introduction of hybrid technology in vehicles is increased fuel efficiency; by means of energy recovery, ability to have the engine operate under better conditions and allowing for the engine to be dimensioned after mean instead of peak power, so called downsizing. The size matching of engine, electric machines and battery is a decision that has significant impact on the over-all vehicle design and can range from an integrated starter-generator to ability to drive in zero-emissions mode. However, already the decision to select hybrid-electric technology is supported by engineering work; other alternatives are for example supercharging and flywheel.
The low-fidelity electrical system uses a generic buffer with constant efficiency for charge/discharge and electrical machines with behavioural parameterization such as rated torque and power. This allows for straightforward parameterization allowing elaboration on dimensioning without having to consider and/or understand electrical systems in more detail. The high fidelity model includes a model of the battery stack, allowing for studies on how usage and temperature affects efficiency and wear. The low-fidelity thermal system provides constant temperature to all temperature dependent components, and as such it represents an ideal cooling circuit. The high-fidelity model is depicted in Figure 5 and contains a system representation of the thermal system of the full vehicle, including cooling of the engine, transmission and electrical system.
To compare these three approaches, a mean-value engine model (MVEM) from the Engine Dynamics Library is used as a base, and it is configured with 1) turbo charger with electrical support, so called e-boost, 2) an integrated flywheel and 3) a motor/generator with battery.
The thermal system itself includes all the main components of a Vehicle Thermal Management (VTM) system. The core 442
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Figure 7. Accumulated fuel consumption (left) and state of charge (right) for configurations 1 (thick blue), 2 (dashed red) and 3 (green). 5. THERMAL MANAGEMENT As shown in section 3, a multi-perspective approach is possible for the models. As illustrated by the thermal management layout, the complete vehicle and thermal dynamics can be modelled together. This allows closed-loop simulations of the vehicle dynamics together with the thermal system dynamics. The model however is structured with the particular domain of interest at the top level (multiperspective). In this case, since the focus of the thermal system engineer is on the thermal system components, these reside at the top level of the model. The rest of the vehicle systems (including a driver model) exist but remain at lower levels of the model. A portion of this hierarchy is shown in the Figure 8.
Figure 8. Vehicle Thermal Management model hierarchy for the e4WD
Figure 6. Engine model with three different configurations for engine downsizing comparison; turbo with e-boost (top), flywheel (middle) and electric machine integrated at the crank-shaft (bottom).
As an illustration of the capability of this type of model, closed loop drive cycle simulations were performed. Drive cycles are often used for fuel economy studies but are also suited for thermal management design. In these studies the driver model follows a vehicle longitudinal speed trace which generates heat within the vehicle component models. This heat must be managed by the cooling system.
Figure 7 shows simulation results of repeated EUDC cycles. Here, since configurations 2 and 3 have the ability to regenerate brake energy, fuel consumption is better as expected. Configuration 2 performs better than 3 since the losses of the flywheel are lower than the electric machinebattery combination. The battery adds a significant contribution to the losses as the power output enforces the high internal currents in the battery. This suggest to either increase the battery or to complement with a more dynamic buffer, e.g. a super capacitor.
Since the focus of the simulations is the thermal system performance, the high-fidelity thermal system model is used. Also, since high frequency vehicle dynamics do not contribute appreciably to heat generation (note that the suspension dampers are not connected to the cooling system in this version of the thermal system) the low-fidelity chassis model is adequate for these tests. Recall that the low fidelity chassis model includes only longitudinal vehicle dynamics, as described in section 3. The powertrain in these simulations is the conventional rear wheel drive with front wheel electric assist (e4WD). The tests compare alternate configurations of the thermal management system. In the first case the high voltage 443
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electric battery is assumed to be passively cooled through convective heat transfer with the vehicle under-body. In the second case, the high voltage battery is isolated from the under-body but includes an active fluid coolant loop in the heat exchanger stack. This slight difference can create some unexpected results, illustrating the benefit of model-based design.
the increase in battery temperature in addition to its heat generation. Second, this had an unexpected result of affecting the transmission oil temperature since the transmission is also connected to the under-body. Third, the battery SOC benefitted from the reduced temperature for the actively cooled system and remained higher through the simulation. 6. DRIVING PERFORMANCE
Figure 9 show some of the results of these simulations using the US EPA FTP (2013) drive cycle. This drive cycle was chosen because it includes moderate speeds and frequent start/stop. In these figures the blue trajectories are the passively cooled version and the red trajectories are the actively cooled version as described above.
By the introduction of partial electric propulsion, new possibilities to improve the driving experience open up. For the vehicle in this work, where the additional electric four wheel drive is considered, acceleration should consider traction blending of combustion engine and electric machines, as well as front/rear torque distribution. Correspondingly for a deceleration event, there is a split between mechanical friction brakes and electric machines. Common for both cases is that the blending must fit with the over-all energy management strategy and fulfil requirements with respect to drivability, performance, and safety. In this Section, launch and acceleration analysis is used to illustrate the evaluation; other examples can be found in Griffin (2012). The experiment is performed on a skid-pad with 50m radius and the friction coefficient lowered to 70% of nominal. The vehicle launch event is set up with a driver that starts from stand still with applied brakes, by releasing the brakes and then pressing the accelerator pedal. In this case, the engine is in idle mode but also start-stop functionality can be evaluated using the same set-up, see e.g. Griffin (2012). Figure 10 shows a screen shot from the event. Here, acceleration and velocity profile constitutes the key performance indicators, while jerk (acceleration change rate), response time and peak acceleration are key indicators for drivability and comfort. Figure 11 compares the original rear wheel drive vehicle with the e4WD version. As seen from the acceleration and velocity profile, the e4WD outperforms the original vehicle, mainly due to improved grip, and significantly more available torque from stand-still. Additionally, the feel of the e4WD will be much better, as it provides faster response time, smoother acceleration with lower peak, and thereby also a lower jerk.
Figure 9. Collection of various results from FTP drive cycle simulation for the e4WD Vehicle Thermal Management model There are a few interesting results to note. First, since the battery was connected to the under-body for the passively cooled version, heat (which can flow in either direction) flowed to the battery during the simulation. This contributed
Figure 10. Animation snap shot from the driving performance event; vectors indicate tire forces.
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any simulation tool. In this case it is run from Excel using the FMI add-in for Excel, FMI Add-in (2013). This allows a component designer to evaluate the performance of different component sizes without the need of the full Modelica tool. The last example considers the import of a full vehicle FMU to a driver-in-the-loop-environment using FMI Library, FMI Library (2013). The vehicle model is compiled with a fixedstep integrator that is in-lined with the code for maximum efficiency. As a result, complex models with high-fidelity can be run in real-time on the target platform.
Figure 11. Comparison of launch and acceleration performance in a curve with radius 50m and 70% road friction for the original vehicle (thin lines) and the e4WD vehicle (thick lines). Left: velocity [m/s], right: acceleration [m/s2] (solid) and jerk [m/s3] (dashed).
8. SUMMARY This paper has illustrated how Modelica and FMI technologies is and can be used the automotive industry to allow for an integrated view of many different aspects of vehicle development, illustrated by the addition of electric front axle drive to a conventional rear axle drive vehicle. Here, the following capabilities of the technologies key enablers:
7. DEPLOYMENT Since Modelica tool technology includes features like symbolic manipulation and index reduction, the original DAE model can be presented as an index-1 ODE to solvers. Thus, the generated models are suitable for integration with other models, both using model export and co-simulation. The ability to export models outside the original tool also opens up for a wider deployment of the models within an organization.
In this work, deployment was addressed using FMI compliant tools. Figure 12 shows an overview of the deployment schemes used in this work. For the extended control design of the motor management, the vehicle model with driver is imported as a plant model in Simulink® using the FMI Toolbox for MATLAB®, FMI Toolbox (2013). This allows for control designers to access a high-fidelity plant model to test and evaluate their designs while remaining in the environment familiar to them, and without having to learn or understand an additional tool. Furthermore, the controller from Simulink® can be exported as an FMU for import in the Modelica tool using the same tool.
To cover several physical domains and their interaction in the same system model, To create plug and play compatible component and system models, To design system architectures, To perform component based validation, To work with different fidelity levels within the same framework, and To export models to other tools and applications. REFERENCES
Dahl, J., Andersson, D. (2012) Gas exchange and exhaust condition modeling of a diesel engine using the Engine Dynamics Library In 9th International Modelica Conference Dymola (2012) Dymola web page, www.dymola.com Elmqvist, H. (1978) A Structured Model Language for Large Continuous Systems, PhD Thesis ISRN LUTFD2/TFRT-1015--SE, Department of Automatic Control, Lund University, Sweden, May 1978 FMI (2013) FMI standard www.fmi-standard.org FMI Toolbox (2013) FMI Toolbox for MATLAB® www.modelon.com/products/fmi-toolbox-for-matlab/ FMI add-in (2013) FMI Add-in for Excel® www.modelon.com/products/fmi-add-in-for-excel/ Griffin, J., Batteh, J., Andreasson, D. (2012) Drivability using Vehicle Dynamics Library In 9th International Modelica Conference Krueger, I., Limperich, D., Schmitz, G. (2012) Energy consumption of battery cooling in hybrid electric vehicles, In International Refrigeration and Air Conditioning Conference at Purdue Modelica (2012) Modelica Association web page www.modelica.org Soejima (2000) Examples of Usage and spread of Dymola within Toyota In International Modelica Workshop, Lund, Sweden. US EPA FTP (2013) United States Environmental Protection Agency web page www.epa.gov/otaq/standards
Figure 12. Deployment scheme, including model operation from Excel using co-simulation (bottom left), export and import to and from MATLAB®/Simulink® (top right) and rFactor Pro (bottom right). Another example of when the user neither can nor wants to understand the details of a model or the simulation tool is an engineer that needs to know what effects a design change would have on the vehicle behavior. By using a stand-alone As an FMU, the model can also be simulated separately from 445