This hands-on, two-day course focuses on tools and techniques for using Design of Experiments, statistical modeling, and optimization to calibrate modern powertrain systems in MATLAB® and Simulink®. The course is designed for engineers who intend to calibrate and test engines, develop control algorithms, and simulate powertrain behavior. By the end of the course, attendees will be able to produce a set of optimal base calibration tables for a typical modern gasoline or diesel engine. Two subject variants, based on gasoline or diesel materials, are available. Topics include:
Working with Design of Experiments
-Setting up test plans
-Designing for constraints
-Applying classical, space-filling, and optimal methods
Data loading and handling
-Loading, visualizing, filtering, and augmenting measured data
Response surface modeling
-Creating one-stage and two-stage response-surface models using radial basis functions, polynomials, splines, and neural nets
-Setting up optimization problems
-Reviewing, executing, filling, and exporting tables
-Applying application-specific, multiobjective, and drive-cycle optimization
Background in powertrain calibration required; basic knowledge of MATLAB recommended.
|Day 1 of 2|
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.
|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.
|Test Data Import, Filtering, and Meta Variable Setup||
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).
|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.
|Day 2 of 2|
|Set Up and Run Calibration Optimization Problem in CAGE Tool||
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.
|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.
|Student Walk-In Example Application Support||Work with the instructor to solve specific calibration problems using the skills learned in the formal course.|
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