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 engine response.
The toolbox offers a full range of proven experimental designs:
You can use the experimental design to define the test points to be run in an engine dynamometer. You then bring the test data into Model-Based Calibration Toolbox to develop engine models.
Using the Design Editor in the toolbox, you can generate, augment, and visually compare designs without needing to know the detailed mathematics of Design of Experiments.
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.
One-stage, or global modeling, enables you to fit a model to all the data in one process. Model inputs do not have a hierarchical structure.
Two-stage modeling enables you to fit a model to data with a hierarchical structure; one input is varied while all other inputs are fixed. For example, data collected in the form of spark sweeps is suited to a two-stage model. Each test sweeps a range of spark angles while keeping fixed engine speed, load, intake, and exhaust settings.
Point-by-point, or local modeling, enables you to fit a model at each operating point of an engine with the accuracy to produce an optimal calibration. Point-by-point models are often required for multiple injection diesel engines and gasoline direct-injection engines.
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.
Two-stage and point-by-point models provide additional boundary models for these types of test plans.
Model-Based Calibration Toolbox uses MATLAB® functions for data analysis and visualization, statistics, and optimization to fit the models and view a graphical representation of an engine's behavior. The toolbox provides the MBC Model Fitting app to help you ensure that test points taken in the laboratory match the original experimental design. Using the app, you can fit different model types to the collected data.
The Data Editor enables you to analyze engine 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.
The MBC Model Fitting app provides interactive tools for fitting and validating engine models.
Many types of models are available, enabling you to create statistical models that accurately represent engine data. Gaussian Process models provide good default fits, and are suitable to a wide range of problems. They also have fewer parameters to tune, making them an easy-to-use starting point. Other model types, such as radial basis functions, polynomials, splines, and user-defined nonlinear models are available for further refinement.
The app makes it simple to compare multiple different models, so you can gain confidence in the model fit. Model trends and summary statistics are readily available in plots and tables, enabling you to assess the goodness of fit from both an engineering and statistical viewpoint. You can also refine model fits by removing outliers or exploring different model types.
The app provides appropriate tools and workflows for working with models for one-stage, two-stage, or point-by-point test strategies.
The MBC Optimization app in Model-Based Calibration Toolbox lets you calibrate lookup tables for your engine control unit (ECU). With the app, you can fill and optimize lookup tables in ECU software using models created with the MBC Model Fitting app. You can:
The MBC Optimization app 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:
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. The MBC Optimization app extrapolates the optimization results to pass smoothly through table masks and locked cells (fixed table values). Use these features when you want to use separate optimizations to fill different regions of a lookup table.
The app also provides gradient constraints for controlling table smoothness in optimization-based and feature-based table filling.
Model-Based Calibration Toolbox enables you to produce optimal calibrations for engines with multiple operating modes. You can use the composite model type to combine a number of models that represent engine responses under different operating modes. Using the composite model in the MBC Optimization app produces optimal calibrations for engines with multiple operating modes, where the goal is to fill a single table for all modes or to fill a table for each mode.
ECU software often includes features for estimating states that are too difficult or costly to measure in production vehicles, such as torque and 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 engine models made from measured engine data.
You can export statistical models developed in Model-Based Calibration Toolbox to Simulink or use them for hardware-in-the-loop (HIL) testing.
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 emissions (such as engine-out HC, CO, NOx, and CO2) to Simulink and perform powertrain-matching, fuel economy, performance, and emission simulations to improve powertrain component selections, drivability-related controls, and emission-related controls. Since the key physical components of your model have been derived from measured engine performance data, your models yield more accurate results than detailed physical models from theory that do not capture the complete physical phenomenon of the real-world system.
You can also reduce long-running or computationally intensive simulations by creating an accurate statistical surrogate model of an existing detailed high-fidelity engine model. For example, you can use the toolbox to generate accurate, fast-running models from complex Simulink models or subsystems over the design space of interest. 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 ECU 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 ECU algorithm designs.