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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.

Cam-phasing is increasingly considered as a feasible Variable Valve Timing (VVT) technology for production engines. Additional independent control variables in a dual-independent VVT engine increase the complexity of the system, and achieving its full benefit depends critically on devising an optimum control strategy. A traditional approach relying on hardware experiments to generate set-point maps for all independent control variables leads to an exponential increase in the number of required tests and prohibitive cost. Instead, this work formulates the task of defining actuator set-points as an optimization problem. In our previous study, an optimization framework was developed and demonstrated with the objective of maximizing torque at full load. This study extends the technique and uses the optimization framework to minimize fuel consumption of a VVT engine at part load.

Tire stiffness can have a significant effect on the spindle and component loads. While its’ effect on the component loads may show a different trend. This paper deals with data acquisition loads using Wheel Force Transducer (WFT) with 17 inch, 18 inch and 20 inch tires and shows how the spindle loads changed for different tire. These loads are applied on the analytical suspension model to generate both component and the body attachment loads. Some of the measured channels are correlated for all the wheel sizes for multiple events to ensure the confidence in the model. It is found that even if spindle loads are increased with tire stiffness, the component loads do not necessarily show a similar trend. This paper studies why higher spindle forces do not always give higher component loads and what are the possible alternatives one may look into to shortlist or select one set of loads over the other.

Since being commissioned in 2001, the aero-acoustic wind tunnel at DTF, Wind Tunnel 8 (WT8) has been used to conduct a wide variety of tests. In 2003 alone, over 5250 hours of aerodynamic and aero-acoustic testing were run on over 2000 test articles, including commercial cars, trucks and racing vehicles. Additionally, more unique test articles such as solar cars, motorcycles, Olympic sleds, and others have also been recently tested. The demand for WT8 is driven by the fact that it is among the quietest wind tunnels in the world and one of a very small number of facilities that combines aerodynamic, aero-acoustic, and climatic capabilities in one facility. To enhance WT8's ability to meet the ever-increasing demands of the testing community, and the Motorsports community specifically, an effort was recently initiated to optimize and document the repeatability of aerodynamic force measurements in this tunnel.

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.

An accurate air flow rate model is critical for high-quality air-fuel ratio control in Spark-Ignition engines using a Three-Way-Catalyst. Emerging Variable Valve Timing technology complicates cylinder air charge estimation by increasing the number of independent variables. In our previous study (SAE 2004-01-3054), an Artificial Neural Network (ANN) has been used successfully to represent the air flow rate as a function of four independent variables: intake camshaft position, exhaust camshaft position, engine speed and intake manifold pressure. However, in more general terms the air flow rate also depends on ambient temperature and pressure, the latter being largely a function of altitude. With arbitrary cam phasing combinations, the ambient pressure effects in particular can be very complex. In this study, we propose using a separate neural network to compensate the effects of altitude on the air flow rate.