There is change throughout the automotive industry, from advancing technologies and powertrain options to the increased number of companies producing vehicles. But some approaches are still applicable and can be used amongst all this change. For example, ETAS’ ASCMO has a proven track record with internal combustion engines, but it’s also ideal for calibrating e-motors, specifically a permanent magnet synchronous motor (PMSM).
Defining ASCMO
Let’s take a step back and explain what ASCMO is – the ETAS ASCMO (Advanced Simulation for Calibration, Modeling and Optimization) product family offers a wide range of solutions for data-based system modeling and optimization. It is a powerful statistical tool that has a wide range of applications for nonlinear systems.
ASCMO-MOCA (MOdel CAlibration) optimizes parameters in physics-based models, including those used in ECU and simulation environments. Various plant models and controller models can be loaded, connected, or modeled. You can also load measurement data, import and export model parameters, and define optimization tasks. ASCMO-MOCA provides a variety of functions and options for visualizing and analyzing data and models used.
Then there’s ASCMO-STATIC, which enables users to create data-based models that model the stationary behavior of complex systems. It provides a wealth of functions and options for visualizing, analyzing and optimizing the system behavior and can be used for creating experimental designs based on the DoE (design of experiments) methodology.
A sample situation
Your shipping department just received a crate of new PMSMs. Each motor has a unique air gap design to increase efficiency and torque density while reducing external excitation. And thanks to shipping delays, you are now pressed for time to determine which unique design is best. Oh, and of course you have limited time and resources. So how are you going to run a complete extensive characterization of each motor and calibrate the traction inverter lookup tables in time?
With a little skill in the art of model-based calibration, you enter your variables and ASCMO-STATIC will generate a a limited number of DoE points to obtain all the necessary information and dependencies from the engine. With some additional knowledge you can further reduce the number of points before any testing begins.
You then run your DoE in-vehicle or on the test bed and ASCMO-STATIC quickly generates response models characterizing each motor.
Sample of ASCMO characterization of motor, response models.
This is where the optimizers of ASCMO-STATIC and ASCMO-MOCA enter, optimizing your new models to maximize the PMSM’s efficiency, while reaching the required torque. ASCMO-MOCA can be linked directly to even complex control logics (e.g., Simulink® or as FMU) to calibrate the traction inverter field weakening lookup tables for you. Now you have complete field oriented control (FOC) calibrations for each of your new motors. From there, further analysis can be completed to determine the best stator/rotor air gap combo.
Depiction of potential FOC calibration table
And there you have it – you have an answer by your deadline. Now on to the next challenge.
Not just for internal combustion engines
ETAS ASCMO is commonly used to optimize the prediction quality of ECU models (i.e., virtual sensors) so the deviation of the model prediction from a real measurement on the engine test bench or in the vehicle is minimized for all measuring points. It can also be used to optimize emissions and fuel consumption for complex internal combustion engines in dynamic/transient driving cycles.
But the methodology used in ASCMO is not tied to the internal combustion engine, making it applicable in other areas, including electric mobility (e.g., charging strategy) and component development.
For information on how ETAS ASCMO-MOCA, ASCMO-STATIC, or any of our ETAS ASCMO products can help you, please contact us.
