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The demands for a future diesel engine in terms of emission compliance, CO2 emissions, performance and cost effectiveness set new requirements for the development process of the combustion system. This paper focuses on the development of the next generation Volvo Cars diesel combustion system, which should comply with Euro 6d including Real Driving Emissions (RDE), with emphasis on the novel methods applied throughout the process. The foundation of a high performing combustion system is formed by first determining the requirements for the system, after which the key factors that affect system performance are selected, such as the charge motion, combustion chamber geometry and injector nozzle geometry. Based on the requirements, a robust charge motion with desired flow characteristics is defined. A new automated CFD optimization process for combustion chamber geometry and spray target is developed.

This paper is on the development process of a hydraulic hybrid passenger vehicle. A subcompact passenger vehicle is chosen for modification into a series hydraulic hybrid with the aim of achieving a fuel economy of 100 MPG (2.35 L/100km) on the Urban Dynamometer Driving Schedule (UDDS). This work develops a methodology for simultaneously designing a powertrain and power management strategy of a series hydraulic hybrid. The design process was initiated by developing a system level model validated using engine and hydraulic pump/motor testing by the US EPA at the National Vehicle and Fuel Efficiency Laboratory (NVFEL). Parametric studies were performed in order to determine the size of the pump/motors and accumulators. Several candidate engines were tested and the system models were used to determine which one could provide the best fuel economy while meeting performance constraints.

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.