In response to new emission regulations, learn how you can reduce calibration time and achieve an optimal tradeoff among emission, fuel economy, and performance with model-based calibration methods.  

In this webinar, Pete Maloney from MathWorks discusses how design-of-experiment (DOE), statistical and optimization methods are integrated into a model-based engine calibration process with the objective of achieving optimized calibration values through a consistent process.  Pete also demonstrates the generation of a real-time engine model for in-the-loop testing of the engine control system as part of this calibration process, without additional modeling and model validation effort.   

About the presenter:  Pete Maloney is a principal consulting engineer for MathWorks. His main areas of focus are powertrain calibration tool development and application, large-scale control modeling, and physical system modeling for automotive customers. Before joining MathWorks in 2000, he designed and developed electronic engine control algorithms for Ford Motor Company and Delphi Automotive Systems over a 10-year period, resulting in 15 related patents. Pete has a B.S.M.E. from Texas Tech University and an S.M.M.E. from the Massachusetts Institute of Technology.