ETAS ASCMO-DYNAMIC

Data-based modeling for dynamic behavior

ETAS ASCMO-DYNAMIC enables users to create data-based models that model the dynamic/transient behavior of complex systems. It provides a wealth of functions and options for visualizing, analyzing, and optimizing the system behavior. You can also use ETAS ASCMO-DYNAMIC for creating experimental designs based on the DoE methodology (design of experiments).

ETAS ASCMO-DYNAMIC online documentation

Image of a laptop with an ETAS ASCMO-DYNAMIC user interface on the screen showing various graphics and diagrams.

Your benefits

Easy to use

Intuitive graphical interface easy-to-use for engineers.

Accurate

Accurate modeling, analysis, and optimization of large, complex systems with less manual effort.

Time saving

Reduce testbench time with testplan design (DoE) and efficient modeling.

Features

Experiment control

Operate experiments with ease using start, stop, pause, and record functions. Initialize hardware seamlessly and trigger measurements automatically to enhance the data collection process.

Dynamic data visualization

Visualize measurement data through a variety of virtual instruments, including oscilloscopes, bar graphs, tables, bit views, maps/curves, speedometers, and LEDs. This feature allows for effective monitoring and analysis of real-time data.

Automated calibration and scenario management

Automate calibration steps and manage experimental scenarios efficiently. Define calibration procedures with adjustable repetition intervals and create dialog windows to facilitate process automation. Switch effortlessly between different experimental setups.

Voice command operation

Enhance your interaction with the system by using voice commands to conduct experiments or adjust calibration settings. Place markers during recordings and add spoken comments to increase the precision and usability of data logging.

ETAS ASCMO-DYNAMIC: key functions at a glance

ETAS ASCMO-DYNAMIC can be used to model relevant variables in electric or classic drives using latest machine learning algorithms. Taking into account their history and rate of change, you can perform a detailed, time-resolved analysis of the dynamic processes. Depending on the drive concept, precise predictions can be made for temperature, current, voltage, and battery state of charge for electric drive components, as well as for variables such as CO2 and pollutant emissions from internal combustion engines. You can then visualize dependencies and interactions on the basis of these models.

Design of experiments (DoE)

The density plot shows that the experimental space is completely covered. Relevant areas are reached more often and therefore displayed brighter.

Design of experiments (DoE)

The design of experiments (DoE) element enables you to achieve maximum model accuracy with a minimum measurement effort. The DoE experimental design module (ExpeDesDynamic) makes it easy to plan the underlying measurements, and to propose a time curve of the adjustment variables in which different final values with different gradients are approached methodically and in a space-filling way. It allows you to adjust the range of parameter variation, to add stationary points and excerpts from real driving cycles, and to consider specific aspects and peculiarities of dynamic measurements carried out on engine test benches. Graphical representations of the curves (trajectories) in all dimensions facilitate straightforward validation of your DoE plan.

The density plot shows that the experimental space is completely covered. Relevant areas are reached more often and therefore displayed brighter.

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Visualization and system analysis

ETAS ASCMO-DYNAMIC visualizes the measuring points of a system and automatically generates a model that reflects reality as accurately as possible.

Visualization and system analysis

ETAS ASCMO-DYNAMIC uses time-based scope views to display the system behavior based on the model. It also offers several other options for visualizing dependencies and influences, including the scatter plot. Extensive analysis options include statistical information such as mean, extreme values and root mean square model error (RMSE). In addition, model predictions can be compared with real measurements to evaluate curve accuracy over time. To achieve a suitable sampling rate during measurement, ETAS ASCMO-DYNAMIC allows you to reduce the amount of data by downsampling. This increases the quality of the model and reduces the time required for model training.

ETAS ASCMO-DYNAMIC visualizes the measuring points of a system and automatically generates a model that reflects reality as accurately as possible.

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Modeling

Image of a laptop with an ETAS ASCMO-DYNAMIC user interface on the screen showing various curves.

In ETAS ASCMO-DYNAMIC, models can be created (blue line), which represent the real transient behavior of the system (black line) with the demanded accuracy.

Modeling

ETAS ASCMO-DYNAMIC makes it easy for less experienced users to apply the advanced modelling methods required for accurate, robust prediction of the dynamic/transient system behavior. At the same time, it gives experts the freedom to adjust model parameters with a high degree of flexibility. ETAS ASCMO-DYNAMIC offers a wide variety of modelling approaches such as Gaussian process models, recurrent or convolutional neural networks, ensemble models, as well as autoencoder for anomaly detection. Using ETAS ASCMO-DYNAMIC automated machine-learning, the tool automatically determines the specific mathematical function that best represents the real system behavior. All models can be exported in formats such as Simulink, C code. and FMU/FMI for use outside ETAS ASCMO.

In ETAS ASCMO-DYNAMIC, models can be created (blue line), which represent the real transient behavior of the system (black line) with the demanded accuracy.

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Virtual measuring instrument / virtual test bench

Image of a laptop with an ETAS ASCMO-DYNAMIC user interface on the screen showing blue data clouds on black background

Virtual measuring instrument / virtual test bench

ETAS ASCMO-DYNAMIC allows you to use the models as a virtual measuring instrument. An example of this is the prediction of emissions over a complete driving cycle based on time series of input variables. For all desired parameter combinations of input variables, the model then calculates the corresponding output variables. Just like real measurements (for example from the engine test bench), this artificial data can be used, analyzed graphically, and exported as a CSV file for further processing in Excel.

Compatibility

ETAS ASCMO-DYNAMIC supports all relevant data formats, exports models in various formats, and integrates with MATLAB®, i.e. for custom functions and scripting.

ETAS ASCMO-DYNAMIC add-ons

The functionalities of ETAS ASCMO-DYNAMIC can be expanded for specific tasks using the add-ons listed below.

ETAS ASCMO-ME (model export)

ETAS ASCMO-ME enables models generated with ETAS ASCMO to be exported in the following formats: C code, MATLAB®/Simulink®, Python, ETAS INCA / MDA, Excel VBA, GT‑SUITE, or FMI/FMU. These models can be used outside the ETAS ASCMO environment without the need for license verification.

ETAS ASCMO-SDK (software development kit)

The ETAS ASCMO-SDK provides a MATLAB® interface to ETAS ASCMO. It allows you to remotely control ETAS ASCMO via the command line and via scripts. It also allows user-defined functionality to be integrated, for example any kind of visualization. Moreover, you can use it to connect to test bench automations.

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