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.
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.
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.
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.
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.
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
Optimizing Traction E-Motor Performance
Traction e-motors play a central role in vehicle electrification. When applied to e-motor control calibration, MBC helps motor control engineers to achieve optimal torque and field-weakening control and to maximize e-motor efficiency across the entire torque and speed range. You can:
- Fit flux linkage surfaces at different torque and speed operating points
- Fit e-motor core loss models based on id/iq currents and speed
- Generate torque-speed envelope using DC bus voltage and flux tables
- Generate id/iq field-weakening control lookup tables that maximize e-motor efficiency.
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.
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.
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.
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.