Introduction
Powertrain Blockset™ provides fully assembled reference application models of automotive powertrains, including gasoline, diesel, hybrid, and electric systems. It includes a component library for simulating engine subsystems, transmission assemblies, traction motors, battery packs, and controller models. Powertrain Blockset also includes a dynamometer model for virtual testing.
Powertrain Blockset provides a standard model architecture that can be reused throughout the development process. You can use it for design tradeoff analysis and component sizing, control parameter optimization, and hardware-in-the-loop testing. You can customize models by parameterizing components in a reference application with your own data or by replacing a subsystem with your own model.
Key Features :
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Fully assembled models for gasoline, diesel, hybrid, and electric powertrains
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Libraries of engine, transmission, traction motor, and battery components
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Basic controllers for powertrain subsystems
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Standard drive cycle data, including FTP75, NEDC, and JC08
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Engine dynamometer model for virtual calibration and testing
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MDF file support for calibration data import
Case 1 : Mapped and Dynamic model of engine, motor and generator
Mapped model : It consists of data points which is assigned accordingly in the form of interpolation tables or Look up tables(Maps). This refers to kind of static simulation which take very less time but the accuracy of the results are not that good. It can be considered as macro level solver and does not elevate for real time solver.
Dynamic model : It is a micro level solver, and always evaluates outputs from various from various subsystems and combines together to form a dynamic effect. It considers Real time scenarios. It has good accuracy.
Mapped & Dynamic Engine
Mapped Engine represents macro engine behaviour as set of interpolation tables inclues brake torque, fuel flow, air mass flow, exhaust temperature, efficiency & emissions as function of commanded load and measured engine speed. It can be simply refered as static system. e.g. In Engine & transmission power train, matching analysis for fuel economy, emissions & performance tade-offs.
Fig.1.1. Mapped engine model
Dynamic engine decomposes engine behaviour into individual component models that account for engine dynamics, i.e., intake airflow and turbocharger dynamics. It is suitable for designing control, estimator, and diagnostic algorithms that depend of dynamic subsystems,e.g, in closed loop AFR control algorithm development.
Fig.1.2. Dynamic engine model
Mapped & Dynamic motor
Here in mapped motor model represents macro engine behaviour as set of interpolation tables Torque, motor speed, input voltage etc,.
Fig.1.3 Mapped motor model
Dynamic motor model is a micro level solver, and always evaluates outputs from various from various subsystems and combines together to form a dynamic effect.
Fig.1.4. Dynamic motor model
Mapped & Dynamic Generator
Fig.1.5. Mapped Generator Model
Fig.1.6. Dynamic Generator model
To convert from one model to another
For eg. Lets consider the motor model which is by default in motor dynamic model type which has to be changed to mapped motor model
Here we have to select any dynamic/ selected model and should go to View > Model explorer as per fig.1.7.
Fig.1.7.
Then choose the type of model required as shown in Fig.1.8.,
Fig.1.8.
The model type is changed from dynamic to mapped type as shown in fig.1.9.
Fig.1.9.
The same procedure can be followed for engine & generator.
Case 2 : How does the model calculate miles per gallon? Which factors are considered to model fuel flow?
When you find the performance calculation block inside the visualization block we will find different inputs like vehicle speed, fuel flow & battery power. The miles per gallon is found by following the steps shown in fig.2.0.
Fig.2.0.
1) In the block 1, vehicle speed is root mean squared and is integrated over the span of time to get average distance travelled, then converted to m to km by dividing it by 1000, and further the same block is divided by 100 for calculating per 100 km distance.
2) Fuel flow rate in block 2 is integrated over time span to get volume of fuel rate then add 1000 to convert to litres.
3) Battery power is converted to kw by dividing by 1000. As one U.S. gallon contains 33.7 kwh, which will convert kw unit into gallon by dividing it further by 3600 as per hr conversion and then get gain of 0.00378541 m^3 to get the equivalent battery power in rate of fuel rate.
Hence, Total Fuel flow rate = Fuel flow rate from engine + Battery energy equivalent flow rate.
If we divide total distance per 100 km by ltrs of fuel flow, then we get fuel economy as L/100 km
If we require the output of fuel economy in U.S gallons i.e. m^3 to gallons, then convert distance to miles
Case 3 : Run the HEV Reference Application with WOT drive cycle. Change the grade and wind velocity in the environment block. Comment on the results.
Open the power train block found in the help section and open the Hybrid Electric multimode model in the example section, A window will open as per fig.2.1.
Fig.2.1.
Change the parameters as per the fig.2.2.,
Fig.2.2.
Run the simulation now and you get the outputs as per fig.2.3.
Fig.2.3. Case 1 : Output of HEV model without change in Grade and wind velocity
Now, change the Grade and wind velocity inside the Environment block as shown in fig.2.4.,
Fig.2.4.
The outputs are show as per fig.2.5.
Fig.2.5. Case 2 : Output of HEV model with change in Grade and wind velocity
Conclusion
1) In the case 1 where the grade and velocity not added, the nominal speed of the vehicle is reached nearly in 20 seconds, whereas in case 2, with addition of grade and velocity factors the time taken to reach the nominal speed is nearly 25 seconds. Hence more time is required to achive the nominal speed with increase in grade and velocity of wind.
2) Battery SOC in case 1 is 80 % to 71 % wheras in case 2 it is 80 % to 68 %
3) Fuel economy in case 1 is nearly 10- 11 Mpge & in case 2 it is 7-8 Mpge
Case 4 : Keeping all other parameters same, compare the simulated results of hybrid and pure electric powertrains.
Similarly open the pure EV model, in the example section of block train in powertrain section.
Fig.2.6.
Same steps which was followed for Hybrid vehicle is followed for Ev model.
Fig.2.7. Case 1 : Output of Pure EV model without change in Grade and wind velocity
Fig.2.8. Case 2: Output of Pure EV model with change in Grade and wind velocity
Conclusion (Comparing Hybrid and Ev outputs for altered grade and velocity )
1) In the case where the grade and velocity are changed the nominal speed of the vehicle is reached nearly in 25 seconds, whereas in case of EV with altered values of grade and velocity time taken to reach the nominal speed is nearly 40 seconds. Hence an EV takes more time achive the nominal speed when compared with HEV with increase in grade and velocity of wind , But
2) For the same time interval the Battery SOC in case of HEV is 80 % to 68 % wheras in case of EV it is 100 % to 98 %, This is also because the HEV uses lead acid battery.
3) Fuel economy in case of HEV is nearly 7-8 Mpge & in case of EV it is 38 Mpge