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A DMS (Driver Monitoring System) is one of the most important safety features that assist in the monitoring functions and alert drivers when distraction or drowsiness is detected. The system is based in a DSMC (Driver Status Monitoring Camera) mounted in the vehicle's dash, which has a predefined set of operational requirements that must be fulfilled to guarantee the correct operation of the system. These conditions represent a trade space analysis challenge for each vehicle since both the DSMC and the underlying vehicle’s requirements must be satisfied. Relying upon the camera’s manufacturer evaluation for every iteration of the vehicle’s design has proven to be time-consuming, resources-intensive, and ineffective from the decision-making standpoint.

There are a multitude of dynamics faced by any industry. There is also a consistent search and development of technological platforms and services to address these changes. This necessitates a shared work philosophy which involves multiple stakeholders. Verification and validation are integral part of any development irrespective of product, process, or services. Also, every industry has a regulatory compliance to adhere too. But the extent of complexity and the level of dependencies or interactions between modules as well as stakeholders involved, creates slippage at some or other level. Nowadays the industries are also driven by reuse for cost effectiveness. Though it marks the significant improvement in the capability to compete, compatibility is a key measure to a successful product or service launch and sustainability.

In this paper, a novel mixed-integer programming model is developed to optimally assign the die sets to candidate plants to minimize the total costs. The total costs include freight shipping stamped parts to assembly plants, die set movement, outsourcing, and utilization. Therefore, the objective function is weighted multi-criteria and it takes into consideration some of the key constraints in the real-world condition including “must-move die sets”. An optimization tool has been developed that takes several inputs and feeds them as the input to the mathematical model and generates the optimal assignments with the directional costs as the output. The tool has been tested for several plants at Ford and has proved its robustness by saving millions of dollars. The developed tool can easily be applied to other manufacturing systems and original equipment manufacturers (OEMs).

The steering system is to provide the driver with the possibility of lateral vehicle guidance, i.e. to influence the lateral dynamics of the vehicle; moreover, it is crucial to promptly translate the steering input to have the vehicle in high-quality directional stability. An electrical power assisted steering (EPAS) system is the sophisticated variant to meet higher requirements for vehicle safety, ride comfort, and driver-assist. This research is to investigate if a CAE methodology could be innovated to better simulate the durability of a steering system under various working scenarios; figure out the critical features of the modeling; conduct a correct analysis procedure for validating the modeling and collecting data for evaluation. With step by step in modeling and analysis, a well-established example of CAE model of EPAS is enabled to highlight the novelty of steering vehicle level CAE methodology and therefore achieve the research goal.

Historical driver behavior and drive style are crucial inputs in addition to V2X connectivity data to predict future events as well as fuel consumption of the vehicle on a trip. A trip is a combination of different maneuvers a driver executes to navigate a route and interact with his/her environment including traffic, geography, topography, and weather. This study leverages big data analytics on real-world customer driving data to develop analytical modeling methodologies and algorithms to extract maneuver-based driving characteristics and generate a corresponding maneuver distribution. The distributions are further segmented by additional categories such as customer group and type of vehicle. These maneuver distributions are used to build an aggressivity distribution database which will serve as the parameter basis for further analysis with traffic simulation models.

Green zones are challenging problems for the thermal management systems of hybrid vehicles. This is because within the green zone the engine is turned off, and the only way to keep the aftertreatment system warm is lost. This means that there is a risk of leaving the green zone with a cold and ineffective aftertreatment system, resulting in high emissions. A thermal management strategy that heats the aftertreatment system prior to turning off the engine, in an optimal way, to reduce the NOx emissions when the engine is restarted, is developed. The strategy is also used to evaluate under what conditions pre-heating is a suitable strategy, by evaluating the performance in simulations using a model of a heavy-duty diesel powertrain and scenario designed for this purpose.

As an alternative lightweight material, chopped carbon fiber reinforced Sheet Molding Compound (SMC) composites, formed by compression molding, provide a new material for automotive applications. In the present study, the monotonic and fatigue behavior of chopped carbon fiber reinforced SMC is investigated. Tensile tests were conducted on coupons with three different gauge length, and size effect was observed on the fracture strength. Since the fiber bundle is randomly distributed in the SMC plaques, a digital image correlation (DIC) system was used to obtain the local modulus distribution along the gauge section for each coupon. It was found that there is a relationship between the local modulus distribution and the final fracture location under tensile loading. The fatigue behavior under tension-tension (R=0.1) and tension-compression (R=-1) has also been evaluated.

The fatigue analysis of a full car body requires the sheet metal (sheet fatigue), spot welds (spot weld fatigue) and seam welds (seam weld fatigue) to be thoroughly evaluated for durability. Traditionally this has always been done in the time domain, but recently new frequency domain techniques are able to perform these tasks with numerous advantages. This paper will summarize the frequency domain process and then compare the results and performance against the more usual time domain process.

This study deals with passenger side restraint system design for frontal impact and four impact modes are considered in optimization. The objective is to minimize the Relative Risk Score (RRS), defined by the National Highway Traffic Safety Administration (NTHSA)'s New Car Assessment Program (NCAP). At the same time, the design should satisfy various injury criteria including HIC, chest deflection/acceleration, neck tension/compression, etc., which ensures the vehicle meeting or exceeding all Federal Motor Vehicle Safety Standard (FMVSS) No. 208 requirements. The design variables include airbag firing time, airbag vent size, inflator power level, retractor force level. Some of the restraint feature options (e.g., some specific features on/off) are also considered as discrete design variables. Considering the local variability of input variables such as manufacturing tolerances, the robustness and reliability of nominal designs were also taken into account in optimization process.

In the recent years, adjoint optimization has gained popularity in the automotive industry with its growing applications. Since its inclusion in the mainstream commercial CFD solvers and its continuously added capabilities over the years, its productive usage became readily available to many engineers who were previously limited to interfacing the customized adjoint source code with CFD solvers. The purpose of this work is to demonstrate using an adjoint solver a method to optimize duct shape that meets multiple design objectives simultaneously. To overcome one of the biggest challenges in the duct design, i.e. the severe packaging constraints, the method here uses geometrically constrained adjoint to ensure that the optimum shape always fits into the user-defined packaging space. In this work, adjoint solver and surface sensitivity calculations are used to develop the optimization method.

This paper describes a joint SAE automotive and aerospace Recommended Practice SAE J3168 now in development to standardize a process for Reliability Physics Analysis. This is a science-based approach to implement Physics-of-Failure research in conducting durability simulations in a Computer Aided Engineering Environment. It is used to calculate failure mechanism susceptibilities and estimate the likelihood of failure and the expected durability life of Electrical, Electronic and Electromechanical components and equipment, due to stresses such as mechanical shock, vibration, temperature cycling, etc. Reliability Physics Analysis is based on the material science principle of stress driven damage accumulation in materials. The process enables the identification of potential failure risks early in the design phase so that such risks can be designed out in order to efficiently design high reliable and robustness into electronic products.

Friction reduction in lubricated components through engine oil formulations has been investigated in the present work. Three different DI packages in combination with one friction modifier were blended in SAE 5 W-20 and SAE 0 W-16 viscosity grades. The friction performance of these oils was compared with GF-5 SAE 5 W-20 oil. A motored cranktrain assembly has been used to evaluate these, in which friction mean effective pressure (FMEP) as a function of engine speeds at different lubricant temperatures is measured. Results show that the choice of DI package plays a significant role in friction reduction. Results obtained from the mini-traction machine (MTM2) provide detailed information on traction coefficient in boundary, mixed and elastohydrodynamic (EHD) lubrication regimes. It has been shown that the results from the cranktrain rig are fairly consistent with those found in MTM2 tests for all the lubricants tested.

This paper summarizes the development of a wireless message from infrastructure-to-vehicle (I2V) for safety applications based on Dedicated Short-Range Communications (DSRC) under a cooperative agreement between the Crash Avoidance Metrics Partners LLC (CAMP) and the Federal Highway Administration (FHWA). During the development of the Curve Speed Warning (CSW) and Reduced Speed Zone Warning with Lane Closure (RSZW/LC) safety applications [1], the Basic Information Message (BIM) was developed to wirelessly transmit infrastructure-centric information. The Traveler Information Message (TIM) structure, as described in the SAE J2735, provides a mechanism for the infrastructure to issue and display in-vehicle signage of various types of advisory and road sign information. This approach, though effective in communicating traffic advisories, is limited by the type of information that can be broadcast from infrastructures.

There is an ongoing effort in the industry to develop an accelerated corrosion test for automotive heat exchangers. This has become even more important as automakers are focusing on corrosion durability of 15 years in the field versus current target of 10 years. To this end an acid immersion test was developed and reported in a previous paper for condensers (1). This paper extends those results to evaporators and establishes the efficacy of the test using these results and those reported in the literature. The paper also discusses variability in corrosion test results as observed in tests such as ASTM G85:A3 Acidified Synthetic Sea Water Test (SWAAT), and its relation to field durability.

To assess the fuel consumption of vehicles, three sets of input data are required; drive cycles, vehicle parameters, and environmental conditions. As the first part of a series of studies on real-world fuel consumption, this study focuses on the drive cycles. In principle, drive cycles should represent real-world usage. Some of them aim at a specific usage such as a city driving condition or an aggressive driving style. However, the definition of city or aggressive driving is very subjective and difficult to quantitatively correlate with the real-world usage. This study proposes a methodology to quantify the speed and dynamics of drive cycles, or vehicle speed traces in general, against the real-world usage. After reviewing parameter sets found in other studies, relative cubic speed (RCS) and positive kinetic energy (PKE) are selected to represent the speed and dynamics through energy flow balance at the wheels.

SiC devices have inherent fast switching capabilities due to their superior material properties, and are considered potential candidates to replace Si devices for traction inverters in electrified vehicles in future. However, due to the comparatively low gate threshold voltage, SiC devices may encounter oscillatory false triggering especially during fast switching. This paper analyzed the causes of false triggering, and also studied the impact of a critical parasitic parameter - common source inductance. It is shown that crosstalk is the main cause for the false triggering in the case and some positive common source inductance help to mitigate the crosstalk issue. A packaging layout method is proposed to create the positive common source inductance through layout of control terminals / busbars, and/or the use of control terminal bonded wires at different height.

Electric vehicles are highly sensitive to variations in environmental factors (like temperature, drive style, grade, etc.). The distribution of real-world range of electric vehicles due to these environmental factors is an important consideration in target setting. This distribution can be obtained by running several simulations of an electric vehicle for a number of high-frequency velocity, grade, and temperature real-world trip profiles. However, in order to speed up simulation time, a unique set of drive profiles that represent the entire real-world data set needs to be developed. In this study, we consider 40,000 unique velocity and grade profiles from various real-world applications in EU. We generate metadata that describes these profiles using trip descriptor variables. Due to the large number of descriptor variables when considering second order effects, we normalize each descriptor and use principal component analysis to reduce the dimensions of our dataset to six components.

Self-pierced rivet (SPR) and adhesive are two important joining technologies widely used in automobile industry, and they are often used together to form a hybrid joint. SPR and adhesives can often be used in close proximity in a component, leading to an interaction of the two joints. This interaction can influence the corrosion and noise, vibration and harshness (NVH) characteristics of the structure, as well as its strength and durability. In this paper, the stress distribution in an SPR-adhesive hybrid joint is evaluated by using the finite element method, and then compared with that in an adhesive joint. Results indicate that the stress concentrates at the edge of adhesive layer in hybrid joint and adhesive joint and around the rivet in an SPR joint. The effect of rivet is numerically investigated by either removing the rivet from the hybrid joint or changing the position of the rivet on the overlapping area.

Ultrasonic fatigue tests (testing frequency around 20kHz) have been conducted on A356 aluminum alloys with different copper contents and AS7GU aluminum alloy. Tests were performed in dry air and submerged in water conditions. The effect of copper content was investigated and it was concluded that copper content plays an important role influencing the humidity effect on A356 aluminum alloy ultrasonic fatigue lives. Also, for the same copper content, copper in solute solution or in precipitate have different humidity sensitivities.

In early design activities (typically before the hardware is built), a reliability prediction is often required for the electronic components and systems in order to assess their future reliability and in many cases to meet customer specifications. These specifications may include the allocated reliability for a particular electronic unit and in the cases of functional safety products to meet the ASIL (Automotive Safety and Integrity Level) requirement specified by the functional safety standard ISO 26262. The standard allows for the use of “statistics based on field returns or tests” as a valid alternative to the handbook-based reliability prediction. This paper presents a newly developed SAE-J3083 standard “Reliability Prediction for Automotive Electronics Based on Field Return Data”, which covers the types of the required data, ways to collect it, and the methodology of how to process this data to calculate the failure rates and meet the expected safety goals.