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In passenger car development, extreme ICE downsizing trends have been observed over the past decade. While this comes with fuel economy benefits, they are often obtained at the expense of Brake Mean Effective Pressure (BMEP) rise time in transient engine response. Through advanced control strategies, the use of Fully Variable Valvetrain (FVVT) technologies has the potential to completely mitigate the associated drivability-penalizing constraints. Adopting a statistical approach, key part load performance engine parameters are analyzed. Design-of-Experiment data is generated using a validated GT-Power model for a Freevalve-converted turbocharged Ultraboost engine. Subsequently, MathWorks' Model Based Calibration (MBC) toolbox is utilized to interpret the data through model fitments using neural network models of optimized architectures.

Throughout its history, the internal combustion engine has been continuously scrutinized to achieve strict legislative emission targets. With the dawn of renewable fuels fast approaching, most Internal Combustion Engine (ICE) equipped hybrid electric vehicles (HEVs) face difficulty in adjusting their precise control strategies to new fuels. This is partly due to constrained limitations associated with camshaft-induced design-point air induction limitations. Freevalve is a fully variable valvetrain technology enabling independent control of valve lifts, durations, and timings. Additionally, the added degrees-of-freedom enable the capability to shut-off individual engine valves, optimizing combustion performance and stability through specific speed ranges. By design, it minimizes the existing breathing-related constraints that are currently hindering the extraction of the higher efficiency potential of ICEs.

With ever stricter legislative requirements for CO2 and other exhaust emissions, significant efforts by OEMs have launched a number of different technological strategies to meet these challenges such as Battery Electric Vehicles (BEVs). However, a multiple technology approach is needed to deliver a broad portfolio of products as battery costs and supply constraints are considerable concerns hindering mass uptake of BEVs. Therefore, further investment in Internal Combustion (IC) engine technologies to meet these targets are being considered, such as lean burn gasoline technologies alongside other high efficiency concepts such as dedicated hybrid engines. Hence, it becomes of sound reason to further embrace diversity and develop complementary technologies to assist in the transition to the next generation hybrid powertrain. One such approach is to provide increased valvetrain flexibility to afford new degrees of freedom in engine operating strategies.

Nitrogen oxides emissions are an important aspect of engine design and calibration due to increasingly strict legislation. As a consequence, accurate modeling of nitrogen oxides emissions from Diesel engines could play a crucial role during the design and development phases of vehicle powertrain systems. A key step in future engine calibration will be the need to capture the nonlinear behavior of the engine with respect to nitrogen oxides emissions within a rapid-calculating mathematical model. These models will then be used in optimization routines or on-board control features. In this paper, an artificial neural network structure incorporating a number of engine variables as inputs including torque, speed, oil temperature and variables related to fuel injection is developed as a method of predicting the production of nitrogen oxides based on measured test data. A multi-layer perceptron model is identified and validated using data from dynamometry tests.

The series sequential turbocharging technology is recently gaining attention as the new round of engine downsizing and emission control becomes imperative for the engine manufacturers. The technology is able to provide combined benefits of transient performance, engine downsizing, fuel efficiency and emissions reduction with foreseeable problems of control, packaging and cost. The matching and characterization of the two interactive turbochargers is a challenging exercise. Two important questions are, how should the two machines be sized and what is the best strategy for the turbochargers across the speed range of the engine at full load. This paper addresses these two questions by comparing a variety of matching sizes and presenting an attempt to identify an optimal valve operating schedule in order to achieve the target limiting torque curve.