Engine base calibration maps are traditionally developed in the laboratory well after an engine hardware design is established using CAE models. However, base map calibration significantly affects engine performance. As such, today’s engine designers cannot objectively judge the vehicle drive-cycle performance impact of hardware design changes during the engine hardware design phase prior to prototype hardware build. MathWorks developed a Virtual Engine Calibration Optimization (VECO) process to enable spark ignition (SI) engine designers to automatically generate optimal base engine calibration maps composed of 1350 variables directly from their CAE 1D engine models with no human labor in less than 2 hours using Amazon EC2 Cloud Computing. The base calibration maps produced by the VECO process also provide feed-forward control data needed in model predictive control (MPC) development, as well as a starting point for production engine calibration map development in the engine dynamometer laboratory after prototype engine hardware build.

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