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Cam-Phasing Optimization Using Artificial Neural Networks as Surrogate Models-Maximizing Torque Output
Technical Paper
2005-01-3757
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
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.
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Wu, B., Prucka, R., Filipi, Z., Kramer, D. et al., "Cam-Phasing Optimization Using Artificial Neural Networks as Surrogate Models-Maximizing Torque Output," SAE Technical Paper 2005-01-3757, 2005, https://doi.org/10.4271/2005-01-3757.Also In
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