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Strong dependency on crude oil in most areas of modern transportation needs lead into a significant consumption of petroleum resources over many decades. In order to maximize the effective use of remaining resources, various types of powertrain topologies, such as hybrid configurations among fuel cell, electric battery as well as conventional IC engine, have been proposed and tested out for number of vehicle classes including a personal commuting vehicle. In this paper the vehicle parameters are based on a typical commercial sub-compact vehicle (FIAT Panda) and energy needs are estimated on the sized powertrain. The main control approach is divided in two categories: off-line global optimization with dynamic programming (DP, not implementable in real time), and on-line Proportional and Feed-Forward with PI controllers. The proposed control approaches are developed both for charge-sustaining and charge-depleting mode and sample results are shown and compared.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

With the introduction of common rail fuel injection system enabling multiple injections per stroke and stringent pollutant emission standards, the optimization and calibration of modern compression ignition direct injection (CIDI) engines become more complex. Thus, a simple and efficient tool for CIDI combustion simulation with arbitrary fuel injection profile is required today. A crank-angle resolved combustion model was developed and validated by using a fuel injection rig and an engine dynamometer for the parameterization. With the calibrated models, accurate prediction of the in-cylinder pressure and NOx and also a virtual dynamometer mapping are possible. The results from these virtual mappings can be used to calibrate the black-box combustion models in control-oriented, dynamic Mean Value Models (MVM).