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Selecting the right transportation platform is challenging, whether it is at a personal level or at an organizational level. In settings where predominantly the functional aspects rule the decision making process, defining the mobility of a vehicle is critical for comparing different offerings and making acquisition decisions. With the advent of intelligent vehicles, exhibiting partial to full autonomy, this challenge is exacerbated. The same vehicle may traverse independently and with greater tolerance for acceleration than human occupied vehicles, while, at the same time struggle with obstacle avoidance. The problem presents itself at the individual vehicle sensing level and also at the vehicle/fleet level. At the sensing and information level, one can be looking at issues of latency, bandwidth and optimal information fusion from multiple sources including privileged sensing. At the overall vehicle level, one focuses more on the ability to complete missions.

An application of variation simulation for predicting vehicle interior driveline vibration is presented. The model, based on a “Monte Carlo”-style approach, predicts the noise, vibration and harshness (NVH) response of the vehicle driveline based on distributions of imbalance and runout derived from manufacturing production variation (the forcing function) and the vehicle's sensitivity to the forcing function. The model is used to illustrate the change in vehicle interior vibration that results when changes are made to production variation for runout and imbalance of driveline components, and how those same changes result in different responses based on vehicle sensitivity.

The need for transient thermal simulations in vehicle packaging studies has grown rapidly in recent years. To date, the computational costs associated with the transient simulation of 3D conjugate heat transfer phenomena has prohibited the widespread use of full vehicle transient simulations. This paper presents results from a recent study that explored a method to circumvent the computational costs associated with long transient conjugate heat transfer simulations. The proposed method first segregates the thermal structural and fluid physics domains to take advantage of time scale differences. The two domains are then re-coupled to calculate a series of steady state conjugate heat transfer simulations at various vehicle speeds. The local convection terms are then used to construct a set of surrogate models dependent on vehicle speed, that predict the local heat transfer coefficients and the local near wall fluid temperatures.

Durability of automotive structures is a primary engineering consideration that is evaluated during a vehicle's design and development. In addition, it is a basic expectation of consumers, who demand ever-increasing levels of quality and dependability. Automakers have developed corporate requirements for vehicle system durability which must be met before a products is delivered to the customer. To provide early predictions of vehicle durability, prior to the construction and testing of prototypes, it is necessary to predict the forces generated in the vehicle structure due to road inputs. This paper describes an application of the “virtual proving ground” approach for vehicle durability load prediction for a vehicle on proving ground road surfaces. Correlation of the results of such a series of simulations will be described, and the modeling and simulation requirements to provide accurate simulations will be presented.

The Ethanol-Boosted Direct Injection (EBDI) demonstrator engine is a collaborative project led by Ricardo targeted at reducing the fuel consumption of a spark-ignited engine. This paper describes the design challenges to upgrade an existing engine architecture and the synergistic use of a combination of technologies that allows a significant reduction in fuel consumption and CO₂ emissions. Features include an extremely reduced displacement for the target vehicle, 180 bar cylinder pressure capability, cooled exhaust gas recirculation, advanced boosting concepts and direct injection. Precise harmonization of these individual technologies and control algorithms provide optimized operation on gasoline of varying octane and ethanol content.

Motivated by experiences in the Formula SAE® competition, an engine control unit (ECU) was designed, developed and tested at Oakland University. A systems approach was taken in which the designs of the electronic architecture and software were driven by the mechanical requirements and operational needs of the engine, and by the need for dynamometer testing and tuning functions. An ECU, powered by a 68HC12 microcontroller was developed, including a four-layer circuit board designed for EMC. A GUI was written with Visual C++® for communication with a personal computer (PC). The ECU was systematically tested with an engine simulator, a 2L Ford engine and a 600cc Honda engine, and finally in Oakland's 2004 FSAE vehicle.

Springback is a common phenomenon in automotive manufacturing processes, caused by the elastic recovery of the internal stresses during unloading. A thorough understanding of springback is essential for the design of tools used in sheet metal forming operations. A DBS (Draw-bead Simulator) has been used to simulate the forming process for two different sheet metals: aluminum and steel. Two levels of pulling force and two die radii have been enforced to the experimental process to get different springback. Also, the Digital Image Correlation (DIC) system has been adopted to capture the sheet contour and measure the amount of side-wall-curl (sheet springback) after deformation. This paper presents the influence of the material properties, force, and die radius on the deformation and springback after forming. A thorough understanding of this phenomenon is essential, seeing that any curvature in the part wall can affect quality and sustainability.

The coefficient of friction between surfaces is an important criterion for predicting metal behavior during sheet metal stamping processes. This research introduces an innovative technique to find the coefficient of friction on a lubricated aluminum sheet metal surface by simulating the industrial manufacturing stamping process while using 3D digital image correlation (3D-DIC) to track the deformation. During testing, a 5000 series aluminum specimen is placed inside a Stretch-Bend-Draw Simulator (SBDS), which operates with a tensile machine to create a stretch and bend effect. The friction coefficient at the contact point between an alloy sheet metal and a punch tool is calculated using an empirical equation previously developed. In order to solve for the unknown friction coefficient, the load force and the drawback force are both required. The tensile machine software only provides the load force applied on the specimen by the load cell.

Mechanical properties of as-rolled steels used in a vehicle vary with many parameters including gages, steel suppliers and manufacturing processes. The residual forming and strain rate effects of automotive components have been generally neglected in full vehicle crashworthiness analyses. Not having the above information has been considered as one of the reasons for the discrepancy between the results from computer simulation models and actual vehicle tests. The objective of this study is to choose the right material property for as-rolled steels for stamping and crash computer simulation, and investigate the effect of forming and strain rate on the results of full vehicle impact analyses. Major Body-in-White components which were in the crash load paths and whose material property would change in the forming process were selected in this study. The post-formed thickness and yield stress distributions on the components were estimated using One Step forming analyses.

It is becoming increasingly difficult to obtain good repeatability from running lab vehicle correlation testing, since vehicle variability is so significant at the Low ULEV and SULEV emissions levels. These new emission standards are becoming so stringent that it makes it very difficult to distinguish whether a problem is a result of vehicle variability, test cell sampling or the analytical system. A vehicle exhaust emission simulator (VEES) developed by Horiba, can simulate emissions from low emitting gasoline vehicles by producing tailpipe flow rates containing emissions constituents ( HC, CH4, CO, NOx, CO2 ) injected at the tailpipe flow stream via mass flow controllers.

As the standards for exhaust emissions have become more stringent, the quality control tools used to evaluate the performance of low level samplers and analyzers has become more important. The Vehicle Exhaust Emissions Simulator (VEES) was developed to evaluate the performance of vehicle or engine exhaust emissions sampling and analytical systems. The simulator emulates emissions from low-emitting gasoline vehicles by producing a simulated exhaust stream containing emission constituents (HC, CO, CO2, and NOx) injected via Mass Flow Controllers (MFCs). This paper discusses various applications of the VEES as a quality control tool for ULEV and SULEV testing. A comparison is made between the injected amount of exhaust species by the VEES and the amounts recovered by the different sampling systems. Different root cause scenarios are discussed as to the source of discrepancies between the results on the CVS and BMD for different driving cycles.