Model-Based Calibration Toolbox
Model and calibrate complex powertrain systems
Model-Based Calibration Toolbox™ provides apps and design tools for modeling and calibrating complex nonlinear systems. It can be used in a wide range of applications, including powertrain systems such as engines, electric machines, pumps, and fans, as well as nonautomotive systems such as jet engines, marine hydrofoils, and drilling equipment. You can define optimal test plans, automatically fit statistical models, and generate calibrations and lookup tables for high-degree-of-freedom systems that would require exhaustive testing using traditional methods. Using the toolbox apps or MATLAB® functions, you can automate the model fitting and calibration process.
Models created with Model-Based Calibration Toolbox can be exported to Simulink® to support control design, sensitivity analysis, hardware-in-the-loop testing, and other simulation activities. Calibration tables can be exported to ETAS INCA and ATI VISION.
Get Started:
Design of Experiments
Model-Based Calibration Toolbox™ enables you to design a test plan based on Design of Experiments, a methodology that saves test time by letting you perform only those tests that are needed to determine the shape of your system’s response. The toolbox offers a full range of proven experimental designs including Space-filling designs, Optimal designs, and Classical designs.
Define a space-filling design and investigate the properties of the design using the Design Editor.
Test Strategies
Model-Based Calibration Toolbox integrates experimental design with three widely used test strategies: one-stage, two-stage, and point-by-point. Each test strategy has an appropriate test plan and model type.
Assign your local / global variables for the chosen model type.
Modeling the System Envelope
Acquiring data and modeling the engine must account for the operating regions of the system that can be physically tested. Model-Based Calibration Toolbox lets you add constraints to your experimental designs and create boundary models that describe the feasible region for testing and simulation. Supported boundary model types include convex hulls, which provide the minimal convex set containing all the data points.
Using the Boundary Editor to define and visualize the feasible testing region and associated test conditions.
Preprocessing Data
Model-Based Calibration Toolbox provides tools to analyze data and transform it into a form that is suitable for modeling. With the Data Editor you can perform a variety of preprocessing operations, including filtering to remove unwanted data, adding test notes to document findings, transforming or scaling raw data, grouping test data, and matching test data to experimental designs.
Using the Data Editor to select a subset of tests and view the data in different formats: a 2D plot, a 3D plot, and a table.
Fit Model to Data
The MBC Model Fitting app provides interactive tools for fitting and validating system models. Many types of models are available, enabling you to create statistical models that accurately represent your data. You can choose from Gaussian Process models, radial basis functions, polynomials, splines, and user-defined nonlinear models. The app makes it simple to compare multiple different models, so you can gain confidence in the resulting model fit.
Using the MBC Model Fitting app to fit and evaluate different model types for a spark ignition engine.
Optimizing Engine Performance
The MBC Optimization app in Model-Based Calibration Toolbox lets you generate optimal calibrations for lookup tables that control engine functions, such as spark ignition, fuel injection, and inlet and exhaust valve timing. Calibration of these features typically involves tradeoffs between engine performance, economy, reliability, and emissions. You can:
- Make tradeoffs between competing design objectives
- Perform multi-objective, constrained optimizations
- Perform weighted optimizations based on typical drive cycles
- Export calibrations to ETAS INCA and ATI VISION
Mazda’s SKYACTIVE-D engine.
Optimizing Systems with Multiple Operating Modes
Complex calibration problems can require different optimizations for varying regions of a table. The table-filling wizard enables you to incrementally fill tables from the results of multiple optimizations, providing smooth interpolation through existing table values. You can also combine a number of models that represent the system responses under different operating modes, where the goal is to fill a single table for all modes or to fill a table for each mode.
Using the MBC Optimization app to produce optimal calibrations for engines with multiple operating modes
Calibrating Estimator Features
Controller software often includes features for estimating states that are too difficult or costly to measure in production, such as engine torque or aircharge. Using the MBC Optimization app, you can describe estimator features graphically with Simulink® block diagrams, fill the lookup tables for these features, and then compare the estimators with empirical models made from measured data.
Process for calibrating, filling, and validating feature tables for a subsystem that estimates engine torque.
Plant Modeling and Optimization
Use statistical models developed in the toolbox to capture real-world complex physical phenomena that are difficult to model using traditional mathematical and physical modeling. For example, you can export models for torque, fuel consumption, and engine-out emissions to Simulink and perform powertrain-matching, fuel economy, performance, and emission simulations. The statistical surrogate can then replace the long-running subsystems in Simulink to speed up simulation time.
Reference application from Powertrain Blockset.
Hardware-in-the-Loop Testing
Model-Based Calibration Toolbox models exported to Simulink can be used in real-time simulations with hardware to provide fast, accurate plant model emulation to the sensor and actuator harnesses. Since developing models in the toolbox takes advantage of a methodical process, you can reduce bottlenecks related to the current art of HIL plant model development, resulting in earlier validation of algorithm designs.
Complete Speedgoat rack setup. This setup was used to automate the testing of tractor controllers with hardware-in-the-loop test benches.
Lookup Tables
Simplified workflows for filling breakpoints, smoothing data, and accessing common tasks
Regression Models
Use CAGE to import and optimize Statistics and Machine Learning Toolbox regression models
Design of Experiments
Add points for engine calibrations at operating boundary
Examples
Live Scripts for engine calibration, design of experiment, and engine modeling
See release notes for details on any of these features and corresponding functions.
