Browse Publications Technical Papers 2004-01-3054
2004-10-25

Using Artificial Neural Networks for Representing the Air Flow Rate through a 2.4 Liter VVT Engine 2004-01-3054

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller. Comparison between a mass air flow (MAF) sensor and the ANN model during a typical dynamic maneuver shows a very good agreement and superior behavior of the network during the transient. Practical recommendations regarding the production implementation of the ANN are provided as well.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Members save up to 18% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
We also recommend:
TECHNICAL PAPER

MBT Timing Detection and its Closed-Loop Control Using In-Cylinder Ionization Signal

2004-01-2976

View Details

TECHNICAL PAPER

Using Neural Networks to Compensate Altitude Effects on the Air Flow Rate in Variable Valve Timing Engines

2005-01-0066

View Details

TECHNICAL PAPER

Nonlinear Recurrent Neural Networks for Air Fuel Ratio Control in SI Engines

2004-01-1364

View Details

X