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The effects of lubricating oil on friction and engine-out emissions in a light-duty 2.2L compression ignition direct injection (CIDI) engine were investigated. A matrix of test oils varying in viscosity (SAE 5W-20 to 10W-40), friction modifier (FM) level and chemistry (MoDTC and organic FM), and basestock chemistry (mineral and synthetic) was investigated. Tests were run in an engine dynamometer according to a simulated, steady state FTP-75 procedure. Low viscosity oils and high levels of organic FM showed benefits in terms of fuel economy, but there were no significant effects observed with the oils with low MoDTC concentration on engine friction run in this program. No significant oil effects were observed on the gaseous emissions of the engine. PM emissions were analyzed for organic solubles and insolubles. The organic soluble fraction was further analyzed for the oil and fuel soluble portions.

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.