This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Model-Based Approach for Engine Performance Optimization
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 30, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
State-of-the-art motorcycle engines consist of numerous variable components and require a powerful motor management to meet the growing customer expectations and the legislative requirements (e.g. exhaust and noise emissions, fuel consumption) at the same time. These demands are often competing and raise the level of complexity in calibration. In the racing domain, the optimization requirements are usually higher and test efficiency is crucial. Whilst the number of variables to control is growing, the time to perform an engine optimization remains the same or is even shortened. Therefore, simulation is becoming an essential part of the engine calibration optimization. Considering the special circumstances in racing, involving valuable hardware, as well as extremely short development and calibration iteration loops, only transient testing is possible.
By utilizing model-based testing and optimization, Ducati Corse, the racing team division of the well-known motorcycle manufacturer Ducati, improved the ability to optimize their race engines efficiently. By using an engine model it is possible to make extremely quick calibration adaptations. All parameters can easily be optimized with respect to potential constraints without running the engine on a testbed. Moreover, the re-use of the engine model for co-simulations is applicable and sharing it with other departments in the company is possible to increase efficiency even more.
AVL CAMEO™ - the intelligent automated calibration environment - supports all engine optimization requirements with a consistent workflow from the task definition to the verification. For this specific racing use case, the software solution was implemented for the test planning using DoE (Design of Experiment), the data plausibility check and the empirical engine modeling. In addition, AVL CAMEO™ was the tool for realizing the model-based optimization and map creation.
With the implementation of the model-based approach, the motorcycle manufacturer has successfully improved the engine performance optimization. An exact model of the engine is now available which supports a deep understanding of the engine behavior. Through realizing this calibration approach, quick office and race track adaptations are possible and alternative optimizations for different tracks or conditions are easy to execute.
|Technical Paper||Increased 2-Wheeler Development Efficiency by Using a New Dedicated Test System Solution|
|Journal Article||Reed Valve CFD Simulation of a 2-Stroke Engine Using a 2D Model Including the Complete Engine Geometry|
CitationBartoccini, D., Niedermaier, P., and Grassberger, H., "Model-Based Approach for Engine Performance Optimization," SAE Technical Paper 2018-32-0082, 2018, https://doi.org/10.4271/2018-32-0082.
Data Sets - Support Documents
|Unnamed Dataset 1|
|Unnamed Dataset 2|
|Unnamed Dataset 3|
- Zerbini , G. , Cugnetto , G. , and Ivarson , M. Model-Based Optimization of the Ducati Multistrada MY15 with Desmodromic Variable Valve Timing (DVT) 6th International Symposium on Development Methodology Wiesbaden 2016
- Scheidel , S. and Gande , M. DOE-Based Transient Maneuver 7th International Symposium on Development Methodology Wiesbaden 2017
- Varsha , A. , Rainer , A. , Santiago , P. , and Umale , R. Global COR iDOE Methodology: An Efficient Way to Calibrate Medium & Heavy Commercial Vehicle Engine Emission and Fuel Consumption Calibration SAE Technical Paper 2017-26-0032 2017 10.4271/2017-26-0032
- Büchel , M. and Thomas , M. Rollout of a Fast Calibration Approach for Engine Base Calibration 3rd International Symposium on Development Methodology Wiesbaden 2009
- Hametner , C. , Stadlbauer , M. , Deregnaucourt , M. , and Jakubek , S. Incremental Optimal Process Excitation for Online System Identification Based on Evolving Local Model Networks Mathematical and Computer Modelling of Dynamical Systems 19 6 505 525 2013