Model Based Calibration

Prerequisites

Background in powertrain calibration required; basic knowledge of MATLAB recommended.

View details

Day 1 of 2

Problem Definition


Objective:
Get an overview of the general effects of the engine actuators used in the course and review a block-diagram problem statement for the calibration problem to be solved over the two-day course.

Definition of engine type and actuator technologies
Definition of dynamometer test-setup
Definition of actuator control ranges
Qualitative review of engine actuator effects on emissions, performance, and fuel economy
Preview of optimal calibration table solutions to be achieved by the end of the course

Design of Experiments


Objective:
Develop an efficient Design of Experiments (DoE) test plan for the calibration problem definition using the minimal amount of expensive dynamometer test points.

Set up test plan in MBCMODEL interface
Enter engine actuator variable ranges
Design DoE
Apply design constraints
Export DoE to CSV or MATLAB for dynamometer testing

Test Data Import, Filtering, and Meta Variable Setup

Objective: Import data into Model-Based Calibration Toolbox™ and filter it for bad data. Using the directly measured data, calculate meta variables that may not be directly measured (e.g., mechanical power, brake specific fuel consumption).

Putting measured data into simple MBC-compatible Microsoft® Excel® format
Importing data to the MBC Data Editor
Filtering bad data by test and record
Adding meta variables

Statistical Modeling of Measured Engine Data

Objective: Develop accurate statistical engine models required for calibration development. Export those models to MATLAB and Simulink so that others can reuse them in HIL and powertrain simulation models.

Associating the engine data set with the engine test plan
Generating statistical response models
Removing outlier data
Refining response models
Visually reviewing response models
Exporting response models to MATLAB and Simulink

Day 2 of 2

Set Up and Run Calibration Optimization Problem in CAGE Tool

Objective: Import models into the Calibration Generator tool (CAGE) in Model-Based Calibration Toolbox, set up empty lookup table structures, and enter the optimization problem definition.

Import engine models into the CAGE tool
Set up calibration lookup tables
Set up calibration optimizer objective function and constraints for operating-point optimization and operating mode optimization
Add table-gradient and sum constraints to operating-point optimization for drive-cycle-based optimization
Run optimizations for point and sum

Fill Calibration Tables, Judge Results, and Export for ECU Implementation

Objective: Fill the optimal calibration tables, judge the sensitivity of the final calibrations and their validity relative to a validation data set, and export calibration tables for ECU implementation.

Use the Fill Tables button to fill the empty calibration tables with optimal results
Use the Tradeoff tool to judge the sensitivity of the final calibration tables and determine if additional constraints are needed in the optimization process
Use the Dataset tool to import test points measured at optimal settings and compare to predicted engine emissions, performance, and fuel economy to validate the calibration tables
Export calibration tables to ATI Vision, INCA .DCM, and MATLAB formats for ECU implementation

Student Walk-In Example Application Support

Objective: Work with the instructor to solve specific calibration problems using the skills learned in the formal course.