Cam-Phasing Optimization Using Artificial Neural Networks as Surrogate Models-Maximizing Torque Output The University of Michigan

Format:

Conference/Event

Author/Creator:

Wu, Bin, author.

Conference Name:

Powertrain & Fluid Systems Conference & Exhibition (2005-10-24 : San Antonio, Texas, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2005

Summary:

Variable Valve Actuation (VVA) technology provides high potential in achieving high performance, low fuel consumption and pollutant reduction. However, more degrees of freedom impose a big challenge for engine characterization and calibration. In this study, a simulation based approach and optimization framework is proposed to optimize the setpoints of multiple independent control variables. Since solving an optimization problem typically requires hundreds of function evaluations, a direct use of the high-fidelity simulation tool leads to the unbearably long computational time. Hence, the Artificial Neural Networks (ANN) are trained with high-fidelity simulation results and used as surrogate models, representing engine's response to different control variable combinations with greatly reduced computational time. To demonstrate the proposed methodology, the cam-phasing strategy at Wide Open Throttle (WOT) is optimized for a dual-independent Variable Valve Timing (VVT) engine. The optimality of the cam-phasing strategy is validated with engine dynamometer tests

Notes:

Vendor supplied data

Publisher Number:

2005-01-3757

Access Restriction:

Restricted for use by site license