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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.

Automotive OBD freeze frame storage is mandated by regulations since the creation of OBD-II in 1994. The main purpose is to help service engineers to identify the cause of the associated fault. Although OBD regulations [1] have gone through multiple updates and major changes since 1994, the regulations requirements on freeze frame storage, however, remain almost the same. The flexibility to comply with the mandated requirements allows OEMs to come up with very different designs, and potentially would confuse the service engineers when repairing different powertrains and could compromise the main purpose of helping identify the root cause of faults. In 2015, GM fellows [2], together with SAE J1979 committee members, proposed a set of future requirements on the OBD freeze frame storage with the intention to standardize the requirements by mandating the rules what to store and when to store, the minimum number of frames, and the numbering of the frames.

A high performance rigid airfoil profile sunroof wind deflector has been developed for high speed freeway driving with the sunroof open. This deflector is clearly superior to the conventional bar type deflector and less expensive compared to tall flexible fabric mesh deflectors applied on high end vehicles today. It provides superior speech intelligibility under high speed driving with sunroof open. The criterion for designing this deflector was to get the highest airspeed possible to span the sunroof opening under all conditions. The customized shape also utilizes flow unsteadiness, including those at the onset of buffeting, in order to condition the shear layer. The airfoil profiled deflector yielded superior mid and high frequency acoustic performance with acceptable low frequency performance. A shorter airfoil deflector was sufficient to keep the external airflow from entering the forward tilted sunroof opening on a mid-size SUV under test.

A linear parameter varying (LPV) reduced order model (ROM) is used to approximate the volume-averaged temperature of battery cells in one of the modules of the battery pack with varying mass flow rate of cooling fluid using uniform heat source as inputs. The ROM runs orders of magnitude faster than the original CFD model. To reduce the time it takes to generate training data, used in building LPV ROM, a divide-and-conquer approach is introduced. This is done by dividing the battery module into a series of mid-cell and end-cell units. A mid-cell unit is composed of a cooling channel sandwiched in between two half -cells. A half-cell has half as much heat capacity as a full-cell. An end-cell unit is composed of a cooling channel sandwiched in between full-cell and a half-cell. A mass flow rate distribution look-up-table is generated from a set of steady-state simulations obtained by running the full CFD model at different inlet manifold mass flow rate samples.

Recent trends in vehicle light-weighting and tire design requirements have created an increased awareness to tire flat-spotting. Tire flat-spotting occurs when tires remain in a loaded condition without rolling for an extended period of time. Tire flat-spotting can either be temporary or permanent depending on the length of storage and other environmental factors. Tire non-uniformity caused due to flat-spots often induces shake and shimmy (back and forth oscillation of steering wheel) vibration in vehicles due to increased tire-wheel force variation input into the chassis. This can result in increased warranty costs for OEMs (Original Equipment Manufacturers) as well as customer dissatisfaction exhibited in third party quality surveys like the annual J. D. Power IQS (Initial Quality Survey).

Powertrain and driveline systems interaction in rear wheel drive vehicle development has recently gained attention for the improvement of interior noise and vibration in emerging markets. The driveline is a significant path for engine-generated noise and vibration to reach the interior occupant interfaces, where it affects refinement perception. The interaction of powertrain excitation orders and driveline resonant responders covers a wide range of frequency and vehicle operating conditions. This interaction poses significant challenges during vehicle development. With recent increased demand for higher specific power from diesel engines, driveline refinement has become even more challenging, especially for rear wheel drive vehicles. Two driveline related refinement issues were observed during evaluation of a RWD vehicle. Root cause analysis determined that the first issue (lower rpm boom noise and vibration) was due to engine torsional excitation of the driveline.

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.

A copula-based approach for model bias characterization was previously proposed [18] aiming at improving prediction accuracy compared to other model characterization approaches such as regression and Gaussian Process. This paper proposes an adaptive copula-based approach for model bias identification to enhance the available methodology. The main idea is to use cluster analysis to preprocess data, then apply the copula-based approach using information from each cluster. The final prediction accumulates predictions obtained from each cluster. Two case studies will be used to demonstrate the superiority of the adaptive copula-based approach over its predecessor.

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.

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.

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.

Ultrasonic fatigue tests (testing frequency around 20 kHz) have been conducted on four different cast aluminum alloys each with a distinct composition, heat treatment, and microstructure. Tests were performed in dry air, laboratory air and submerged in water. For some alloys, the ultrasonic fatigue lives were dramatically affected by the environment humidity. The effects of different factors like material composition, yield strength, secondary dendrite arm spacing and porosity were investigated; it was concluded that the material strength may be the key factor influencing the environmental humidity effect in ultrasonic fatigue testing. Further investigation on the effect of chemical composition, especially copper content, is needed.

With the implementation of Advanced High Strength Steel (AHSS) becoming more common for automotive manufacturers to reduce mass and/or improve performance, special stamping considerations must be made. Certain production parts may split at trimmed edges where strain levels are well below the forming limit curve of the respective grade, which is more applicable to necking fractures/splits. Similar to the presence of hard inclusion stringers (i.e. MnS) that can cause edge fractures in high strength low alloy steels, AHSS steels most susceptible to this phenomenon typically consist of dual phase or multiphase microstructures containing both a hard phase (martensite) and a soft phase (ferrite). Specific examples of these parts will be discussed, including studies to determine the root cause of the edge fracture and to communicate the solutions for consideration in appropriate standards and specifications.

This paper presents the design and performance details of electric propulsion system for GM's second generation Extended Range Electric Vehicle (EREV). First generation Chevrolet Volts have been driven over half a billion miles in North America from October 2013 through September 2014, 74% of which were all-electric. The second generation of Volt brings a significant mass reduction and increased performance, EV driving range and fuel economy while simultaneously reducing rare earth content in its traction electric motors. The electric propulsion system is built on two electric machines; both PMAC topology. While hybrid-electric vehicles are gaining in popularity in hopes of addressing cleaner, energy sustainable technology in transportation, materials sustainability and rare earth dependence mitigation has not been the first priority in the hybrids available on the market today.

Engine downsizing and super/turbocharging is currently the most followed trend in order to reduce CO2 emissions and increase the powertrain efficiency. A key challenge for achieving the desired fuel economy benefits lies in optimizing the design and control of the engine boosting system, which requires the ability to rapidly sort different design options and technologies in simulation, evaluating their impact on engine performance and fuel consumption. This paper presents a scalable modeling approach for the characterization of flow and efficiency maps for automotive turbochargers. Starting from the dimensional analysis theory for turbomachinery and a set of well-known control-oriented models for turbocharged engines simulation, a novel scalable model is proposed to predict the flow and efficiency maps of centrifugal compressors and radial inflow turbines as function of their key design parameters.

In this paper, the problem of stability control of an electric vehicle is addressed. To this aim, it is required that the vehicle follows a desired yaw rate at all driving/road conditions. The desired yaw rate is calculated based on steering angle, vehicle speed, vehicle geometric properties as well as road conditions. The vehicle response is modified by torque vectoring on front and/or rear axles. This control problem is subject to several constraints. The electric motors can only deliver a certain amount of torque at a given rotational speed. In addition, the tire capacity also plays an important role. It limits the amount of torque they can transfer without causing wheel to slip excessively. These constraints make the Model Predictive Control (MPC) approach a suitable choice, because it can explicitly consider the constraints of the control problem, in particular the tire capacity constraint, and help prevent tire saturation, which is often the cause of vehicle instability.

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.

Gasket materials are utilized for various different types of high temperature testing to prevent leaking at bolted joints. In particular, the automotive test services field uses flanged-gasket bolted exhaust joints to provide a convenient method for installation & removal of exhaust components like catalytic converters for aging, performance testing, etc. Recent improvements in the catalyst aging methods require flanged-gasket joints that can withstand exhaust temperatures as high as 1200°C. Gasket materials previously used in these applications like the graphite based gasket materials have exhibited physical breakdowns, severe leakage, and general thermal failures under these extreme temperatures. In order to prevent these leaks, metal-reinforced gasket materials in a number of configurations were introduced to these extreme temperature environments to evaluate their robustness to these temperatures.