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A detailed study is undertaken to examine how 2010+ diesel engine exhaust emissions change when a soybean-derived B20 biodiesel fuel is used instead of a conventional ultra-low sulfur diesel fuel and to investigate how these changes impact the aftertreatment system. Particulate matter (PM) emissions for each fuel are characterized in terms of mass emissions, size distributions, organic versus soot fraction, metals content, and particle morphology. PM mass recorded by Dekati Mass Monitor, thermal analysis of quartz filters, and calculated from particle size distributions consistently shows a 2 - 3 fold decrease in engine-out soot emissions over a wide mid-load range when changing from ULSD to B20 fuel. This is partly due to a decrease in particle number and partly to a decrease in average size. HC and NO emissions, in contrast, exhibit little change with fuel type.

New European emissions legislation (Euro5) specifies a limit for Particle Number (PN) emissions and therefore drives measurement of PN during vehicle development and homologation. Concurrently, the use of biofuel is increasing in the marketplace, and Euro5 specifies that reference fuel must contain a bio-derived portion. Work was carried out to test the effect of fuels containing different levels of Fatty Acid Methyl Ester (FAME) on particle number, size, mass and composition. Measurements were conducted with a Cambustion Differential Mobility Spectrometer (DMS) to time-resolve sub-micron particles (5-1000nm), and a Horiba Solid Particle Counting System (SPCS) providing PN data from a Euro5-compliant measurement system. To ensure the findings are relevant to the modern automotive business, testing was carried out on a Euro4 compliant passenger car fitted with a high-pressure common-rail diesel engine and using standard homologation procedures.

In this study an attempt to understand and demonstrate the effects of various washcoat technologies under active and passive regeneration conditions was performed. Six different formulations, on 1.0" D. x 3.0" L. SiC wall flow filters at the laboratory level were used at various test conditions, including variable NO₂/NO ratios and O₂ concentrations. Samples were regenerated using active and passive conditions to evaluate regeneration rates and the potential impact of regeneration at the vehicle level. Results were applied to vehicle operating conditions to determine passive functionality and potential benefits. Active regenerations at 2% O₂ and 5% O₂ showed no significant difference in time to complete regeneration and soot burn rates. Active regenerations performed at 1% O₂ and 5% O₂ concentration showed that the regeneration temperature was shifted by approximately 50°C.

In this study, the particle emission characteristics of 10% alternative diesel fuel blends (Rapeseed Methyl Ester and Gas-to-Liquid) were investigated through the tests carried out on a light duty common-rail Euro 4 diesel engine. Under steady engine conditions, the study focused on particle number concentration and size distribution, to comply with the particle metrics of the European Emission Regulations (Regulation NO 715/2007, amended by 692/2008 and 595/2009). The non-volatile particle characteristics during the engine warming up were also investigated. They indicated that without any modification to the engine, adding selected alternative fuels, even at a low percentage, can result in a noticeable reduction of the total particle numbers; however, the number of nucleation mode particles can increase in certain cases.

Vehicle restraint system design is a difficult optimization problem to solve because (1) the nature of the problem is highly nonlinear, non-convex, noisy, and discontinuous; (2) there are large numbers of discrete and continuous design variables; (3) a design has to meet safety performance requirements for multiple crash modes simultaneously, hence there are a large number of design constraints. Based on the above knowledge of the problem, it is understandable why design of experiment (DOE) does not produce a high-percentage of feasible solutions, and it is difficult for response surface methods (RSM) to capture the true landscape of the problem. Furthermore, in order to keep the restraint system more robust, the complexity of restraint system content needs to be minimized in addition to minimizing the relative risk score to achieve New Car Assessment Program (NCAP) 5-star rating.

Pareto optimal map concept has been applied to the optimization of the vehicle system control (VSC) strategy for a power-split hybrid electric vehicle (HEV) system. The methodology relies on an inner-loop optimization process to define Pareto maps of the best engine and electric motor/generator operating points given wheel power demand, vehicle speed, and battery power. Selected levels of model fidelity, from simple to very detailed, can be used to generate the Pareto maps. Optimal control is achieved by applying Pontryagin's minimum principle which is based on minimization of the Hamiltonian comprised of the rate of fuel consumption and a co-state variable multiplied by the rate of change of battery SOC. The approach delivers optimal control for lowest fuel consumption over a drive cycle while accounting for all critical vehicle operating constraints, e.g. battery charge balance and power limits, and engine speed and torque limits.

In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affect the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. This paper further investigates the Bayesian inference based model extrapolation method which is previously proposed by the authors, and provides a systematic and integrated stochastic bias corrected model extrapolation and robustness design process under uncertainty. A real world vehicle design example is used to demonstrate the validity of the proposed method.

The objective of this paper focused on the modeling of an adaptive energy absorbing steering column which is the first phase of a study to develop a modeling methodology for an advanced steering wheel and column assembly. Early steering column designs often consisted of a simple long steel rod connecting the steering wheel to the steering gear box. In frontal collisions, a single-piece design steering column would often be displaced toward the driver as a result of front-end crush. Over time, engineers recognized the need to reduce the chance that a steering column would be displaced toward the driver in a frontal crash. As a result, collapsible, detachable, and other energy absorbing steering columns emerged as safer steering column designs. The safety-enhanced construction of the steering columns, whether collapsible, detachable, or other types, absorb rather than transfer frontal impact energy.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

Design optimization techniques are widely used to drive designs toward a global or a near global optimal solution. However, the achieved optimal solution often appears to be the only choice that an engineer/designer can select as the final design. This is caused by either problem topology or by the nature of optimization algorithms to converge quickly in local/global optimal or both. Problem topology can be unimodal or multimodal with many local and/or global optimal solutions. For multimodal problems, most global algorithms tend to exploit the global optimal solution quickly but at the same time leaving the engineer with only one choice of design. The paper explores the application of genetic algorithms (GA), simulated annealing (SA), and mixed integer problem sequential quadratic programming (MIPSQP) to find multiple local and global solutions using single objective optimization formulation.

Hybrid electric vehicle (HEV) systems offer significant improvements in vehicle fuel economy and reductions in vehicle generated greenhouse gas emissions. The widely accepted power-split HEV system configuration couples together an internal combustion engine with two electric machines (a motor and a generator) through a planetary gear set. This paper describes a methodology for analysis and optimization of alternative HEV power-split configurations defined by alternative connections between power sources and transaxle. The alternative configurations are identified by a matrix of kinematic equations for connected power sources. Based on the universal kinematic matrix, a generic method for automatically formulating dynamic models is developed. Screening and optimization of alternative configurations involves verification of a set of design requirements which reflect: vehicle continuous operation, e.g. grade test; and vehicle dynamic operation such as acceleration and drivability.

Sampling-based methods are general but time consuming for solving a Reliability-Based Design Optimization (RBDO) problem. In order to alleviate the computation burden, score function together with the Monte Carlo method was used to compute the stochastic sensitivities of reliability functions. In literature, re-weighting schemes were shown to converge faster than the regular Monte Carlo method. In this paper, a reweighting scheme together with score function is employed to perform sampling-based stochastic sensitivity analysis to improve the computational efficiency and accuracy. An analytical example is used to show the advantages of the proposed method. Comparisons to the conventional methods are made and discussed. Two RBDO problems are solved to demonstrate the use of the proposed method.

New emissions certification requirements for medium duty vehicles (MDV) meeting chassis dynamometer regulations in the 8,500 lb to 14,000 lb weight classes as well as heavy duty (HD) engine dynamometer certified applications in both the under 14,000 lb and over 14,000 lb weight classes employing large diameter exhaust pipes (up to 4″) have created new exhaust stream sampling concerns. Current On-Board-Diagnostic (OBD) dyno certified particulate matter (PM) requirements were/are 7x the standard for 2010-2012 applications with a planned phase in down to 3x the standard by 2017. Chassis certified applications undergo a similar reduction down to 1.75x the standard for 2017 model year (MY) applications. Failure detection of a Diesel Particulate Filter (DPF) at these low detection limits facilitates the need for a particulate matter sensor.

The Leaner Lifted-Flame Combustion (LLFC) strategy offers a possible alternative to low temperature combustion or other globally lean, premixed operation strategies to reduce soot directly in the flame, while maintaining mixing-controlled combustion. Adjustments to fuel properties, especially fuel oxygenation, have been reported to have potentially beneficial effects for LLFC applications. Six fuels were selected or blended based on cetane number, oxygen content, molecular structure, and the presence of an aromatic hydrocarbon. The experiments compared different fuel blends made of n-hexadecane, n-dodecane, methyl decanoate, tri-propylene glycol monomethyl ether (TPGME), as well as m-xylene. Several optical diagnostics have been used simultaneously to monitor the ignition, combustion and soot formation/oxidation processes from spray flames in a constant-volume combustion vessel.

A single-cylinder Direct Injection Spark Ignition (DISI) engine with optical access was used to investigate the effects of ethanol/gasoline blends on in-cylinder formation of particulate matter (PM) and fuel spray characteristics. Indolene was used as a baseline fuel and two blends of 50% and 85% ethanol (by volume, balance indolene) were investigated. Time resolved thermal radiation (incandescence/natural luminosity) of soot particles and fuel spray characteristics were recorded using a high speed camera. The images were analyzed to quantify soot formation in units of relative image intensity as a function of important engine operating conditions, including ethanol concentration in the fuel, fuel injection timing (250, 300 and 320° bTDC), and coolant temperature (25°C and 90°C). Spatially-integrated incandescence was used as a metric to quantify the level of in-cylinder PM formed at the different operating conditions.

The integration of a HIL system involves many software and hardware elements. During the integration process, various errors may occur. In this paper, the Design For Six Sigma (DFSS) methodology is applied to develop an integration process for a generic type of HIL system. The development follows the DCOV (Design, Characterize, Optimize and Verify) four-phase approach. The result is an efficient process proven to meet the customer's needs, reduce integration errors and deliver a high quality HIL system within less time.

A model of Ford's current FTP based OBD-II catalyst monitor has been developed and used in determining the optimal monitored catalyst volume for several LEV applications. The model predictions were found to agree reasonably well with the available experimental data. Furthermore, the results of this study indicate that the optimal monitored catalyst volume for meeting LEV requirements is vehicle application specific. As a result, it is concluded that a general guideline for sizing of the monitored catalyst volume for LEVs will most likely be inadequate and could result in grossly suboptimal catalyst monitor function for some applications. The model which is described in this paper offers a potentially more effective means of determining the best monitored catalyst volume for a given vehicle application. It should be possible to utilize this model during the early phase of a vehicle program in order to provide for the optimal packaging of the catalyst monitor sensor (CMS).

Rapid Prototyping, Fabrication and Tooling is a process that blends a series of technologies (machines, tools, and methods) capable of generating physical objects directly from a CAD database. The process dramatically reduces the time spent during product development by allowing for fast visualization, verification, iteration, optimization, and fabrication of parts and tools. Many new techniques of tooling have been and are being developed by using rapid fabricated parts. These are having a dramatic impact on both timing and costs throughout the automotive industry. One area that these methods can be utilized to their full potential is motorsports. Of particular interest is the growing use of bridge tooling to provide first article through production intent parts that promote cost effective changes.

Multidimensional models that are used for engine computations must include spray sub-models when the fuel is injected into the cylinder in liquid form. One of these spray sub-models is the droplet interaction model, which is separated into two parts: first, calculation of a collision rate between drops, and second, calculation of the outcome once a collision has occurred. This paper focuses on the problem of calculating the collision rate between drops accurately. Computing the collision rate between drops or particles when they are non-uniformly distributed and sharp gradients are present in their distribution is a challenging task. Traditionally the collisions between parcels of drops have been computed using the same spatial grid as is used for the Eulerian gas-phase calculations. Recently it has been proposed to use a secondary grid for the collision rate calculation that is independent of the gas-phase grid, as is done in the NTC collision algorithm.