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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.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Since their inception, the design of airbag sensing systems has continued to evolve. The evolution of air bag sensing system design has been rapid. Electromechanical sensors used in earlier front air bag applications have been replaced by multi-point electronic sensors used to discriminate collision mechanics for potential air bag deployment in front, side and rollover accidents. In addition to multipoint electronic sensors, advanced air bag systems incorporate a variety of state sensors such as seat belt use status, seat track location, and occupant size classification that are taken into consideration by air bag system algorithms and occupant protection deployment strategies. Electronic sensing systems have allowed for the advent of event data recorders (EDRs), which over the past decade, have provided increasingly more information related to air bag deployment events in the field.

High silicon molybdenum (HiSiMo) ductile cast iron (DCI) is commonly used for high temperature engine components, such as exhaust manifolds, which are also subjected to severe thermal cycles during vehicle operation. It is imperative to understand the thermomechanical fatigue (TMF) behavior of HiSiMo DCI to accurately predict the durability of high temperature engine components. In this paper, the effect of the minimum temperature of a TMF cycle on TMF life and failure behavior is investigated. Tensile and low cycle fatigue data are first presented for temperatures up to 800°C. Next, TMF data are presented for maximum temperatures of 800°C and minimum cycle temperatures ranging from 300 to 600°C. The data show that decreasing the minimum temperature has a detrimental effect on TMF life. The Smith-Watson-Topper parameter applied at the maximum temperature of the TMF cycle is found to correlate well with out-of-phase (OP) TMF life for all tested minimum temperatures.

Long fiber reinforced plastics (LFRP) have exhibited superior mechanical performance and outstanding design flexibility, bringing them with increasing popularity in the automotive structural design. Due to the injection molding process, the distribution of long fibers varies at different locations throughout the part, resulting in anisotropic and non-uniform mechanical properties of the final LFRP parts. Images from X-ray CT scan of the materials show that local volume fraction of the long fibers tends to be higher at core than at skin layer. Also fibers are bundled and tangled to form clusters. Most of the current micromechanical material models used for LFRP are extended from those for short fibers without adequate validation. The effect of the complexity of long fibers on the material properties is not appropriately considered. Thus, modeling of these materials is lagging behind the material manufacturing and design development, which in turn limits their further development.

One of the passive methods to reduce drag on the unshielded underbody of a passenger road vehicle is to use a vertical deflectors commonly called air dams or chin spoilers. These deflectors reduce the flow rate through the non-streamlined underbody and thus reduce the drag caused by underbody components protruding in to the high speed underbody flow. Air dams or chin spoilers have traditionally been manufactured from hard plastics which could break upon impact with a curb or any solid object on the road. To alleviate this failure mode vehicle manufacturers are resorting to using soft plastics which deflect and deform under aerodynamic loading or when hit against a solid object without breaking in most cases. This report is on predicting the deflection of soft chin spoiler under aerodynamic loads. The aerodynamic loads deflect the chin spoiler and the deflected chin spoiler changes the fluid pressure field resulting in a drag change.

As vehicle fuel economy continues to grow in importance, the ability to accurately measure the level of efficiency on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently and provides data to make comparisons. In addition, the procedure offers a reliable process to assess enablers for efficiency improvements. Previous published studies have outlined the development of a comprehensive test procedure to measure transfer case speed-dependent parasitic losses at key speed, load, and environmental conditions. This paper will take the same basic approach for the Power Transfer Units (PTUs) used on Front Wheel Drive (FWD) based All Wheel Drive (AWD) vehicles. Factors included in the assessment include single and multi-stage PTUs, fluid levels, break-in process, and temperature effects.

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.

Pending reductions in light duty vehicle PM emissions standards from 10 to 3 mg/mi and below will push the limits of the gravimetric measurement method. At these levels the PM mass collected approaches the mass of non-particle gaseous species that adsorb onto the filter from exhaust and ambient air. This introduces an intrinsic lower limit to filter based measurement that is independent of improvements achieved in weighing metrology. The statistical variability of back-up filter measurements at these levels makes them an ineffective means for correcting the adsorption artifact. The proposed subtraction of a facility based estimate of the artifact will partially alleviate the mass bias from adsorption, but its impact on weighing variability remains a problem that can reach a significant fraction of the upcoming 3 and future 1 mg/mi standards. This paper proposes an improved PM mass method that combines the gravimetric filter approach with real time aerosol measurement.

The helical spring is one of fundamental mechanical elements used in various industrial applications such as valves, suspension mechanisms, shock and vibration absorbers, hand levers, etc. In high speed applications, for instance in the internal combustion engine or in reciprocating compressor valves, helical springs are subjected to dynamic and impact loading, which can result in a phenomenon called “surge”. Hence, proper design and selection of helical springs should consider modeling the dynamic and impact response. In order to correctly characterize the physics of a helical spring and its response to dynamic excitations, a comprehensive model of spring elasticity for various spring coil and wire geometries, spring inertial effects as well as contacts between the windings leading to a non-linear spring force behavior is required. In practical applications, such models are utilized in parametric design and optimization studies.

We present research in progress to develop and implement a transportable instrumentation package (TIP) to collect driver data in a vehicle. The overall objective of the project is to investigate the symbiotic relationship between humans and their vehicles. We first describe the state-of-art technologies to build the components of TIP that meet the criteria of ease of installation, minimal interference with driving, and sufficient signals to monitor driver state and condition. This method is a viable alternative to current practice which is to first develop a fully instrumented test vehicle, often at great expense, and use it to collect data from each participant as he/she drives a prescribed route. Another practice, as for example currently being used in the SHRP-2 naturalistic driving study, is to install the appropriate instrumentation for data collection in each individual's vehicle, often requiring several hours.

Chrysler, Ford, General Motors, the U.S. Environmental Protection Agency (EPA) and the California Air Resources Board (CARB) have collaborated over the past two years to develop an efficiency test for mobile air conditioner (MAC) systems. Because the effect of efficiency differences between different MAC systems and different technologies is relatively small compared to overall vehicle fuel consumption, quantifying these differences has been challenging. The objective of this program was to develop a single dynamic test procedure that is capable of discerning small efficiency differences, and is generally representative of mobile air conditioner usage in the United States. The test was designed to be conducted in existing test facilities, using existing equipment, and within a sufficiently short time to fit standard test facility scheduling. Representative ambient climate conditions for the U.S. were chosen, as well as other test parameters, and a solar load was included.

In this paper, a microstructure-based three-dimensional (3D) finite element modeling method is adopted to investigate the effects of porosity in thin-walled high pressure die-cast (HPDC) magnesium alloys on their ductility. For this purpose, the cross-sections of AM60 casting samples are first examined using optical microscope and X-ray tomography to obtain the general information on the pore distribution features. The experimentally observed pore distribution features are then used to generate a series of synthetic microstructure-based 3D finite element models with different pore volume fractions and pore distribution features. Shear and ductile damage models are adopted in the finite element analyses to induce the fracture by element removal, leading to the prediction of ductility.

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.

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.

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.

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.