Model Based Calibration
Prerequisites
Background in powertrain calibration required; basic knowledge of MATLAB recommended.
Day 1 of 2 
Problem Definition 
Get an overview of the general effects of the engine actuators used in the course and review a blockdiagram problem statement for the calibration problem to be solved over the twoday course.
 Definition of engine type and actuator technologies
 Definition of dynamometer testsetup
 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 
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 
Import data into ModelBased 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 MBCcompatible 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 
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 
Import models into the Calibration Generator tool (CAGE) in ModelBased 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 operatingpoint optimization and operating mode optimization
 Add tablegradient and sum constraints to operatingpoint optimization for drivecyclebased optimization
 Run optimizations for point and sum

Fill Calibration Tables, Judge Results, and Export for ECU Implementation 
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 WalkIn Example Application Support 
Work with the instructor to solve specific calibration problems using the skills learned in the formal course. 