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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.

This year, in the third year of FutureTruck competition, the Ohio State University team has taken the challenge to convert a 2002 Ford Explorer into a more fuel efficient and environmentally friendly SUV. This goal was achieved by use of a post-transmission, charge sustaining, parallel hybrid diesel-electric drivetrain. The main power source is a 2.5-liter, 103 kW advanced CIDI engine manufactured by VM Motori. A 55 kW Ecostar AC induction electric motor provides the supplemental power. The powertrain is managed by a state of the art supervisory control system which optimizes powertrain characteristics using advanced energy management and emission control algorithms. A unique driver interface implementing advanced telematics, and an interior designed specifically to reduce weight and be more environmentally friendly add to the utility of the vehicle as well as the consumer appeal.

The aim of this paper is to describe a unified approach to modeling the energy efficiency and power flow characteristics of energy storage and energy conversion elements used in hybrid vehicles. Hybrid vehicle analysis and design is concerned with the storage of energy in three domains - chemical, mechanical, and electrical - and on energy conversions between these domains. The paper presents the physical and mathematical basis of this modeling approach, as well as a modular simulator that embodies the same basic principles. The use of the simulator as an analysis tool is demonstrated through the conceptual design of a sport-utility hybrid drivetrain.

The evolution of the flow field inside an IC engine during the intake stroke was studied using 2 different experimental techniques, namely the 2–D Particle Image Velocimetry (2–D PIV) and 3–D Particle Tracking Velocimetry (3–D PTV) techniques. Both studies were conducted using a water analog engine simulation rig. The head tested was a typical pent–roof head geometry with two intake valves and one exhaust valve, and the simulated engine operating point corresponded to an idle condition. For both the 2–D PIV and 3–D PTV experiments, high–speed CCD cameras were used to record the motion of the flow tracer particles. The camera frame rate was adjusted to correspond to 1/4° of crank angle (CA), hence ensuring excellent temporal resolution for velocity calculations. For the 2–D PIV experiment, the flow field was illuminated by an Argon–ion laser with laser–sheet forming optics and this laser sheet was introduced through a transparent piston crown to illuminate the center tumble plane.

Over the last decade, many methods have been proposed for estimating the in-cylinder combustion pressure or the torque from instantaneous crankshaft speed measurements. However, such approaches are typically computationally expensive. In this paper, an entirely different approach is presented to allow the real-time estimation of the in-cylinder pressures based on crankshaft speed measurements. The technical implementation of the method will be presented, as well as extensive results obtained for a V-6 S.I. engine while varying spark timing, engine speed, engine load and EGR. The method allows to estimate the in-cylinder pressure with an average estimation error of the order of 1 to 2% of the peak pressure. It is very general in its formulation, is statistically robust in the presence of noise, and computationally inexpensive.

Typically, the combustion analysis for S.I. engines is limited to the determination of the apparent heat release from in-cylinder pressure measurements, effectively using a single zone approach with constant properties determined at some average temperature. In this paper, we follow an approach consistent with the engine modeling approach (i.e., reverse modeling) to extract heat release rate from combustion pressure data. The experimental data used here solely consists of quantities measured in a typical engine dynamometer tests, namely the crank-angle resolved cylinder pressure, as well as global measurements of the A/F ratio, engine speed, load, EGR, air mass flow rate and temperature and exhaust emissions. We then perform a two-zone, crank-angle resolved analysis of the pressure data using variable composition and properties.