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The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

With their high specific strength and stiffness, composites have the potential to significantly lower the vehicle weight, which can have a dramatic effect on improving fuel efficiency and reducing greenhouse gas emissions. For the past decade or so, composites have been experiencing several transitions, including the transition from micro-scale reinforcement fillers to nano-scale reinforcement fillers, resulting in the nanocomposite. The effectiveness of the nano-sized fillers in composites can be explained by one of their unique geometric properties: the length-to-thickness aspect ratio. Therefore, nano-sized fillers have exceptionally higher reinforcing efficiency than the conventional, large fillers. The effectiveness of the nano-sized fillers in composites is also due to their large surface area and surface energy.

Track testing of race cars is expensive and racing series typically limit the amount of testing that can be done on circuit tracks. Because of this, we saw the need to develop a computer model that could simulate a car on a track with any specified surface roughness and with aerodynamic loading acting on the vehicle. This model allows an analysis of the effect of aerodynamic loading on the vertical dynamic response of the vehicle. Vehicle parameters specific to an IMSA GTP car including aerodynamic data from wind tunnel testing and nonlinear shock characteristics were used in this study. Simulations were run for various speeds and ride height configurations and it was found that very small changes in the static settings of the front and rear ride heights can lead to large differences in the resulting ride heights at speed. This can be attributed to the variations in the nonlinear aerodynamic loading as the ride height and speed of the vehicle change.

Due to titanium's excellent strength-to-weight ratio and high corrosion resistance, titanium and its alloys have great potential to reduce energy usage in vehicles through a reduction in vehicle mass. The mass of a road vehicle is directly related to its energy consumption through inertial requirements and tire rolling resistance losses. However, when considering the manufacture of titanium automotive components, the machinability is poor, thus increasing processing cost through a trade-off between extended cycle time (labor cost) or increased tool wear (tooling cost). This fact has classified titanium as a “difficult-to-machine” material and consequently, titanium has been traditionally used for application areas having a comparatively higher end product cost such as in aerospace applications, the automotive racing segment, etc., as opposed to the consumer automotive segment.

It is well known that the ride quality of trucks is much harsher than that of automobiles. Additionally, truck drivers typically drive trucks for much longer duration than automobile drivers. These two factors contribute to the fatigue that a truck driver typically experiences during long haul deliveries. Fatigue reduces driver alertness and increases reaction times, increasing the possibility of an accident. One may conclude that better ride quality contributes to safer operation. The secondary suspensions of a tractor have been an area of particular interest because of the considerable ride comfort improvements they provide. A gap exists in the current engineering domain of an easily configurable high fidelity low computational cost simulation tool to analyze the ride of a tractor semi-trailer. For a preliminary design study, a 15 d.o.f. model of the tractor semi-trailer was developed to simulate in the Matlab/Simulink environment.

Design of Automotive Composites reports that successful designs of automotive composites occurred recently in this arena. The chapters consist of eleven technical papers selected from the Automotive Composites and other relevant sessions that the editors have been organizing for the SAE International World Congress over the past five years. The book is divided into four sections: o Body Structures o Powertrain Components o Suspension Components o Electrical and Alternative Vehicle Components The composite design examples presented in Design of Automotive Composites come from the major OEMs and top-tier suppliers and are most relevant to the automotive materials challenges currently faced by the industry. Many of the innovative ideas have already been implemented on existing or new model vehicles, although a great deal of innovation is still in the works.

The automotive sector has taken a keen interest in lightweighting as new required performance standards for fuel economy come into place. This strategy includes parts consolidation, design optimization, and material substitution, with sustainable polymers playing a major role in reducing a vehicle’s weight. Sustainable polymers are largely biodegradable, biocompatible, and sourced from renewable plant and agricultural stocks. A facile way to enhance their properties, so they can indeed replace the ones made from fossil fuels, is by reinforcing them with fibers to make composites. Natural fibers are gaining more acceptance in the industry due to their renewable nature, low cost, low density, low energy consumption, high specific strength and stiffness, CO2 sequestration potential, biodegradability, and less wear imposed on machinery. Biocomposites then become a very feasible way to help address the fuel consumption challenge ahead of us.

A computational methodology has been developed for loss prediction in intake regions of internal combustion engines. The methodology consists of a hierarchy of four major tasks: (1) proper computational modeling of flow physics; (2) exact geometry and high quality and generation; (3) discretization schemes for low numerical viscosity; and (4) higher order turbulence modeling. Only when these four tasks are dealt with properly will a computational simulation yield consistently accurate results. This methodology, which is has been successfully tested and validated against benchmark quality data for a wide variety of complex 2-D and 3-D laminar and turbulent flow situations, is applied here to a loss prediction problem from industry. Total pressure losses in the intake region (inlet duct, manifold, plenum, ports, valves, and cylinder) of a Caterpillar diesel engine are predicted computationally and compared to experimental data.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).