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An operational scheme with fuel-lean and exhaust gas dilution in spark-ignited engines increases thermal efficiency and decreases NOx emission, while these operations inherently induce combustion instability and thus large cycle-to-cycle variation in engine. In order to stabilize combustion variations, the development of an advanced ignition system is becoming critical. To quantify the impact of spark-ignition discharge, ignitability tests were conducted in an optically accessible combustion vessel to characterize the flame kernel development of lean methane-air mixture with CO₂ simulating exhaust diluent. A shrouded fan was used to generate turbulence in the vicinity of J-gap spark plug and a Variable Output Ignition System (VOIS) capable of producing a varied set of spark discharge patterns was developed and used as an ignition source. The main feature of the VOIS is to vary the secondary current during glow discharge including naturally decaying and truncated with multiple strikes.

To date, model validation metric is prominently designed for non-dynamic model responses. Though metrics for dynamic responses are also available, they are specifically designed for the vehicle impact application and uncertainties are not considered in the metric. This paper proposes the validation metric for general dynamic system responses under uncertainty. The metric makes use of the popular U-pooling approach and extends it for dynamic responses. Furthermore, shape deviation metric was proposed to be included in the validation metric with the capability of considering multiple dynamic test data. One vehicle impact model is presented to demonstrate the proposed validation metric.

Design optimization methods are commonly used for weight reduction subjecting to multiple constraints in automotive industry. One of the major challenges remained is to deal with a large number of design variables for large-scale design optimization problems effectively. In this paper, a new approach based on fuzzy rough set is proposed to address this issue. The concept of rough set theory is to deal with redundant information and seek for a reduced design variable set. The proposed method first exploits fuzzy rough set to screen out the insignificant or redundant design variables with regard to the output functions, then uses the reduced design variable set for design optimization. A vehicle body structure is used to demonstrate the effectiveness of the proposed method and compare with a traditional weighted sensitivity based main effect approach.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

This study demonstrates the use of pressure sensing technology to predict the crash severity of frontal impacts. It presents an investigation of the pressure change in the front structural elements (bumper, crush cans, rails) during crash events. A series of subsystem tests were conducted in the laboratory that represent a typical frontal crash development series and provided empirical data to support the analysis of the concept. The pressure signal energy at different sensor mounting locations was studied and design concepts were developed for amplifying the pressure signal. In addition, a pressure signal processing methodology was developed that relies on the analysis of the air flow behavior by normalizing and integrating the pressure changes. The processed signal from the pressure sensor is combined with the restraint control module (RCM) signals to define the crash severity, discriminate between the frontal crash modes and deploy the required restraint devices.

Rare earths are a group of elements whose availability has been of concern due to monopolistic supply conditions and environmentally unsustainable mining practices. To evaluate the risks of rare earths availability to automakers, a first step is to determine raw material content and value in vehicles. This task is challenging because rare earth elements are used in small quantities, in a large number of components, and by suppliers far upstream in the supply chain. For this work, data on rare earth content reported by vehicle parts suppliers was assessed to estimate the rare earth usage of a typical conventional gasoline engine midsize sedan and a full hybrid sedan. Parts were selected from a large set of reported parts to build a hypothetical typical mid-size sedan. Estimates of rare earth content for vehicles with alternative powertrain and battery technologies were made based on the available parts' data.

Advanced high strength steels (AHSS) have been widely accepted as a material of choice in the automotive industry to balance overall vehicle weight and stringent vehicle crash test performance targets. Combined with efficient use of geometry and load paths through shape and topology optimization, AHSS has enabled vehicle manufacturers to obtain the highest possible ratings in safety evaluations by the Insurance Institute for Highway Safety (IIHS) and the National Highway Traffic Safety Administration (NHTSA). In this study, vehicle CAE side impact models were used to evaluate three side impact crash test conditions (IIHS side impact, NHTSA LINCAP and FMVSS 214 side pole) and the IIHS roof strength test condition and to identify several key components affecting the side impact test performance. HyperStudy® optimization software and LS-DYNA® nonlinear finite element software were utilized for shape and gauge optimization.

Finite Element (FE) models are widely used in automotive for vehicle design. Even with increasing speed of computers, the simulation of high fidelity FE models is still too time-consuming to perform direct design optimization. As a result, response surface models (RSMs) are commonly used as surrogates of the FE models to reduce the turn-around time. However, RSM may introduce additional sources of uncertainty, such as model bias, and so on. The uncertainty and model bias will affect the trustworthiness of design decisions in design processes. This calls for the development of stochastic model interpolation and extrapolation methods that can address the discrepancy between the RSM and the FE results, and provide prediction intervals of model responses under uncertainty.

This study presents the utilization of the hardware-in-the-loop (HIL) approach for regenerative braking (regen) control enhancement efforts for the power split hybrid vehicle architecture. The HIL stand used in this study includes a production brake control module along with the hydraulic brake system, constituted of an accelerator/brake pedal assembly, electric vacuum booster and pump, brake hydraulic circuit and four brake calipers. This work presents the validation of this HIL simulator with real vehicle data, during mild and heavy braking. Then by using the HIL approach, regen control is enhanced, specifically for two cases. The first case is the jerk in deceleration caused by the brake booster delay, during transitions from regen to friction braking. As an example, the case where the regen is ramped out at a low speed threshold, and the hydraulic braking ramped in, can be considered.

Effects of DOF and subjective method on evaluations of ride quality on the Ford Vehicle Vibration Simulator were studied. Seat track vibrations from 6 vehicles were reproduced on the 6 DOF seat shaker in a DOE with pitch and roll as factors. These appeared in two evaluations of ride/shake; semantic scaling by 30 subjects of 6 vehicles, and paired comparisons by 16 of the subjects on 3 of the vehicles. Both methods found significant vehicle, pitch and roll effects. Order dependence was shown for semantic scaling. The less susceptible paired comparison method gave a different ordering, and is thus preferred.

The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 11 years. In the past couple of years, significant attention has been directed toward a revision to the standard for vehicular hydrogen systems, SAE J2579(1). In addition to streamlining test methodologies for verification of Compressed Hydrogen Storage Systems (CHSSs) as discussed last year,(2) the working group has been considering the effect of vehicle fires, with the major focus on a small or localized fire that could damage the container in the CHSS and allow a burst before the Pressure Relief Device (PRD) can activate and safely vent the compressed hydrogen stored from the container.

A benchmark study was conducted to assess the capability of an open source CFD based process to accurately simulate the physics of the flow field around various vehicle types. The ICON FOAMpro process was used to simulate the flow field of four baseline geometries of a Truck, CD-Car, B-Car and an SUV. Further studies were carried out to assess the effects of geometry variations on the predicted aerodynamic lift and drag. A Detached-Eddy Simulation (DES) approach was chosen for the benchmarks. In addition to aerodynamic lift and drag values, the results for surface pressure data, surface and wake flow fields were calculated. These results were compared with values obtained using Ford's existing CFD processes.

A new diesel engine, called the 6.7L Power Stroke® V-8 Turbo Diesel and code named "Scorpion" was designed and developed by Ford Motor Company for the full-size pickup truck and light commercial vehicle markets. A high pressure Exhaust Gas Recirculation (EGR) layout in combination with a Variable Geometry Turbine (VGT) is used to deliver cooled EGR for in-cylinder NOx reduction. The cylinder-to-cylinder variation of EGR and swirl ratio is tightly controlled by the careful design of the EGR mixer and intake system flow path to reduce variability of cylinder-out PM and NOx emissions. 3D-CFD studies were used to quickly screen several EGR mixer designs based on mixing efficiency and pressure drop considerations. To optimize the intake system, 1D-3D co-simulation methodology with AVL-FIRE and AVL-BOOST has been used to assess the cylinder-to-cylinder EGR distribution and dynamic swirl.

Analytical models (math or computer simulation models) are typically built on the basis of many assumptions and simplifications and hence model prediction could be inaccurate in intended applications. Model validation is thus critical to quantify and improve the degree of accuracy of these models. So far, little work considers model validation for various design configurations so that model prediction is accurate in the intended design space. Furthermore, there is a lack of effective approaches that can be used to quantify model accuracy considering different number of experimental data. To overcome these limitations, objective of this paper is to develop a model validation approach for various design configurations with a reference metric for model accuracy check considering different number of experimental data.

ACOUSTOMIZE™ is a new method of acoustic evaluation used for the purpose of understanding and optimizing NVH performance of vehicles. The following paper documents a case study of the ACOUSTOMIZE™ test methodology on a passenger car BIW. This study includes an analysis of noise flow through BIW locations, a comparison of noise sound levels through BIW cavities with and without a sound treatment package and a comparison of the original cavity sealing design package consisting of baffles, tapes and baggies to low density polyurethane NVH Foam. The results of the study show detection of complex BIW pass throughs that the body leakage test (BLT) was not able to find. In addition, the data shows improved noise reduction with the low density polyurethane foam versus the original cavity sealing design package.

This paper is the third in the series of documents designed to record the progress on the SAE Plug-in Electric Vehicle (PEV) communication task force. The initial paper (2010-01-0837) introduced utility communications (J2836/1™ & J2847/1) and how the SAE task force interfaced with other organizations. The second paper (2011-01-0866) focused on the next steps of the utility requirements and added DC charging (J2836/2™ & J2847/2) along with initial effort for Reverse Power Flow (J2836/3™ & J2847/3). This paper continues with the following: 1. Completion of DC charging's 1st step publication of J2836/2™ & J2847/2. 2. Completion of 1st step of communication requirements as it relates to PowerLine Carrier (PLC) captured in J2931/1. This leads to testing of PLC products for Utility and DC charging messages using EPRI's test plan and schedule. 3. Progress for PEV communications interoperability in J2953/1.

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence.

The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

Multidimensional computations were carried out to simulate the in-cylinder fuel/air mixing process of a direct-injection spark-ignition engine using a modified version of the KIVA-3 code. A hollow cone spray was modeled using a Lagrangian stochastic approach with an empirical initial atomization treatment which is based on experimental data. Improved Spalding-type evaporation and drag models were used to calculate drop vaporization and drop dynamic drag. Spray/wall impingement hydrodynamics was accounted for by using a phenomenological model. Intake flows were computed using a simple approach in which a prescribed velocity profile is specified at the two intake valve openings. This allowed three intake flow patterns, namely, swirl, tumble and non-tumble, to be considered. It was shown that fuel vaporization was completed at the end of compression stroke with early injection timing under the chosen engine operating conditions.

In recent years, the residential and transportation sectors have made significant strides in reducing energy consumption, mainly by focusing efforts on low-hanging fruit in each sector independently. This independent viewpoint has been successful in the past because the user needs met and resources consumed in each sector have been clearly distinct. However, the trend towards vehicle electrification has blurred the boundary between the sectors. With both the home and vehicle now relying upon the same energy source, interactions between the systems can no longer be neglected. For example, when tiered utility pricing schemes are considered, the energy consumption of each system affects the cost of the other. In this paper, the authors present an integrated Home-Vehicle Simulation Model (HVSM), allowing the designer to take a holistic view.