Model-Based Calibration Toolbox

Model-Based Calibration Workflow diagram

Designing and Managing Tests

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, including:

  • Classical: Box-Behnken, Central-Composite, and Full Factorial
  • Space-filling: Latin Hypercube and Lattice
  • Optimal: V, D, and A optimality criteria

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.

Defining a space-filling design in the Design Editor and investigating the properties of the design using the MBC Model Fitting app.

Defining a space-filling design in the Design Editor (left) and investigating the properties of the design using the MBC Model Fitting app (right).

Model-Based Calibration Toolbox integrates experimental design with three widely used test strategies:

  • One-stage
  • Two-stage
  • Point-by-point

One-Stage Test Strategies

One-stage test strategies introduce a single source of variation, between tests, and are used for performing variable screening and design-space mapping. The Design of Experiments methodology is typically used to generate test plans that vary all variables simultaneously in this type of approach.

In Model-Based Calibration Toolbox, you can use the one-stage test strategy to identify and model the relationships among the variables in complex systems with multiple variables. For example, you can test an engine at different operating points and control actuator settings defined by a space-filling design for engine speed, load, and air-fuel ratio to develop a performance characterization of the engine using a response surface model.

Two-Stage Test Strategies

Two-stage test strategies introduce two sources of variation: local and global. They are used for tasks that involve sweeping a single control variable while holding other variables constant, as in collecting engine data by sweeping spark at a given engine speed, load, variable valvetrain settings, and air-fuel ratio. In this example the local variation occurs within the test when the spark angle is changed, and the global variation occurs between tests when the engine speed, load, variable valvetrain settings, and air-fuel ratio are changed.

Model-Based Calibration Toolbox lets you estimate local and global variations separately by fitting local and global models in two stages. You can use two-stage modeling to map the complex relationships among all the variables that control the behavior of the engine.

Collecting engine data for each test by sweeping spark while keeping speed, load, variable valvetrain settings, and air-fuel ratio constant.

Collecting engine data for each test by sweeping spark while keeping speed, load, variable valvetrain settings, and air-fuel ratio constant.

Conducting a series of tests, each at a different value of speed, load, variable valvetrain settings, and air-fuel ratio. A model is then fitted to each test (local fitting).

Conducting a series of tests, each at a different value of speed, load, variable valvetrain settings, and air-fuel ratio. A model is then fitted to each test (local fitting).

Using the local models to calculate global models of the engine’s behavior as speed, load, variable valvetrain settings, and air-fuel ratio vary (global fitting).

Using the local models to calculate global models of the engine’s behavior as speed, load, variable valvetrain settings, and air-fuel ratio vary (global fitting).

Performing global fitting for several aspects of engine behavior. (Images courtesy of Ford Motor Company.)

Performing global fitting for several aspects of engine behavior. (Images courtesy of Ford Motor Company.)

Point-by-Point Test Strategies

Point-by-point test strategies enable you to develop statistical models at each operating point of an engine with the necessary accuracy to produce optimal engine calibrations when two-stage test strategies can no longer model engine performance responses accurately enough. Using a point-by-point test strategy in Model-Based Calibration Toolbox, you can accurately model and calibrate modern multiple-injection diesel engines and gasoline direct-injection engines.

Next: Modeling the Engine Envelope

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