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An L18 Taguchi-style Design of Experiments (DOE) with eight factors was used to optimize exterior mirrors for wind noise and drag. Eighteen mirror properties were constructed and tested on a full size greenhouse buck at the Lockheed low-speed wind tunnel in Marietta, GA. Buck interior sound data and drag measurements were taken at 80 MPH wind speed (0° yaw angle). Key wind noise parameters were the fore/aft length of mirror housing and the plan view angle of the mirror housing's inboard surface. Key drag parameters were the fore/aft length of the mirror housing, the cross-section shape of the mirror pedestal, and the angle of the pedestal (relative to the wind).

Traditionally, the controls system in production vehicles with automatic transmission interprets the driver’s accelerator pedal position as a demand for transmission input torque. However, with the advent of electrified vehicles, where actuators are located at different positions in the drivetrain, and of autonomous vehicles, which are self-driving, it is more convenient to interpret the demand (either human or virtual) in vehicle acceleration or wheel torque domain. To this end, a Wheel Torque-based longitudinal Control (WTC) framework was developed, wherein demands can be converted accurately between the vehicle acceleration or wheel torque domain and the transmission assembly input torque domain.

North American drivers particularly dislike wheel dust (brake dust on their wheels). For some vehicle lines, customer surveys indicate that wheel dust is a significant concern. For this reason, Ford and its suppliers are investigating the root causes of brake dust and developing test procedures to detect wheel dust issues up-front. Intuitively, it would appear that more brake wear would lead to more wheel dust. To test this hypothesis, a gage was needed to quantitatively measure the wheel dust. Gages such as colorimeters were evaluated to measure the brightness (L*) of the wheel, which ranged from roughly 70-80% (clean) to 10-20% (very dirty). Gage R&R's and subjective ratings by a panel of 30 people were used to validate the wheel dust gages. A city traffic vehicle test and an urban dynamometer procedure were run to compare the level of wheel dust for 10 different lining types on the same vehicle.

Under various real-world scenarios, vehicles can become disabled and require towing. OEMs allow a few options for vehicle wrecker towing that include wheel lift tow using a stinger or towing on a flatbed. These methods entail multiple loading events that need to be assessed for damage to the towed vehicle. OEMs have several testing and evaluation methods in place for those scenarios with majority requiring physical vehicle prototypes. Recent focus to reduce product development time and cost has replaced the need for prototype testing with analytical verification methods. In this paper, the CAE method involving multibody dynamic simulation (MBDS) as well as finite element analysis (FEA) of vehicle flatbed operation, winching onto a flatbed, and stinger-pull towing are discussed.

Exhaust temperature models are widely used in the automotive industry to estimate catalyst and exhaust gas temperatures and to protect the catalyst and other vehicle hardware against over-temperature conditions. Modeled exhaust temperatures rely on air, fuel, and spark measurements to make their estimate. Errors in any of these measurements can have a large impact on the accuracy of the model. Furthermore, air-fuel imbalances, air leaks, engine coolant temperature (ECT) or air charge temperature (ACT) inaccuracies, or any unforeseen source of heat entering the exhaust may have a large impact on the accuracy of the modeled estimate. Modern universal exhaust gas oxygen (UEGO) sensors have heaters with controllers to precisely regulate the oxygen sensing element temperature. These controllers are duty cycle based and supply more or less current to the heating element depending on the temperature of the surrounding exhaust gas.

High gas prices combined with demand for improved fuel economy have prompted increased interest in diesel engine applications for both light-duty and heavy-duty vehicles. The development of aftertreatment systems for these vehicles requires significant investments of capital and time. A reliable and robust qualification testing procedure will allow for more rapid development with lower associated costs. Qualification testing for DPFs has its basis in methods similar to DOCs but also incorporates a PM loading method and regeneration testing of loaded samples. This paper examines the effects of accelerated loading using a PM generator and compares PM generator loaded DPFs to engine dynamometer loaded samples. DPFs were evaluated based on pressure drop and regeneration performance for samples loaded slowly and for samples loaded under accelerated conditions. A regeneration reactor was designed and built to help evaluate the DPFs loaded using the PM generator and an engine dynamometer.

Through the use of hybrid technology, Ford Motor Company continues to realize enhanced vehicle fuel economy while meeting customer performance and drivability targets. As is characteristic of all Ford Hybrid Electric Vehicles (HEVs), the basis for resolving these competing requirements resides with its Vehicle System Control (VSC) strategy. This strategy implements complex high-level executive controls to coordinate and optimize the desired operational state of the major HEV powertrain subsystems. To ensure that the VSC software meets its intended functionality, a software validation process developed at Research and Advanced Engineering has been integrated as part of the vehicle controls development process. In this paper, this VSC software validation process implemented for a next generation hybrid powertrain is presented. First, an overview of the hybrid powertrain application and the VSC software architecture is introduced.

The USDOT and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications 2 (CAMP-VSC2) Consortium (Ford, GM, Honda, Mercedes, and Toyota) initiated, in December 2006, a three-year collaborative effort in the area of wireless-based safety applications under the Vehicle Safety Communications-Applications (VSC-A) Project. The VSC-A Project developed and tested communications-based vehicle safety systems to determine if Dedicated Short Range Communications (DSRC) at 5.9 GHz, in combination with vehicle positioning, would improve upon autonomous vehicle-based safety systems and/or enable new communications-based safety applications.

The United States Department of Transportation (USDOT) and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications 2 (CAMP-VSC2) Consortium (Ford, General Motors, Honda, Mercedes-Benz, and Toyota) initiated, in December 2006, a three-year collaborative effort in the area of wireless-based safety applications under the Vehicle Safety Communications-Applications (VSC-A) Project. The VSC-A Project developed and tested Vehicle-to-Vehicle (V2V) communications-based safety systems to determine if Dedicated Short Range Communications (DSRC) at 5.9 GHz, in combination with vehicle positioning, would improve upon autonomous vehicle-based safety systems and/or enable new communications-based safety applications.

The increasing trend toward electric and hybrid-electric vehicles (HEVs) has created unique challenges for NVH development and refinement. Traditionally, characterization of in-vehicle powertrain noise and vibration has been assessed through standard operating conditions such as fixed gear engine speed sweeps at varied loads. Given the multiple modes of operation which typically exist for HEVs, characterization and source-path analysis of these vehicles can be more complicated than conventional vehicles. In-vehicle NVH assessment of an HEV powertrain requires testing under multiple operating conditions for identification and characterization of the various issues which may be experienced by the driver. Generally, it is necessary to assess issues related to IC engine operation and electric motor operation (running simultaneously with and independent of the IC engine), under both motoring and regeneration conditions.

Onboard, embedded cellular modems are enabling a range of new connectivity features in vehicles and rich, real-time data set transmissions from a vehicle’s internal network up to a cloud database are of particular interest. However, there is far too much information in a vehicle’s electrical state for every vehicle to upload all of its data in real-time. We are thus concerned with which data is uploaded and how that data is processed, structured, stored, and reported. Existing onboard data processing algorithms (e.g. for DTC detection) are hardcoded into critical vehicle firmware, limited in scope and cannot be reconfigured on the fly. Since many use cases for vehicle data analytics are still unknown, we require a system which is capable of efficiently processing and reporting vehicle deep data in real-time, such that data reporting can be switched on/off during normal vehicle operation, and that processing/reporting can be reconfigured remotely.

A traditional vane type oil pump used inside the engines and the transmissions has equal angles or spacing between the vanes. The equal spacing intensifies pressure fluctuations generated within the pump leading to narrowband pressure spikes at the pump main order and its harmonics. Unequal spacing, however, can relax the severity of the spikes by breaking down the narrowband peaks and distributing them over a larger frequency range. Optimization of the angles within the pump design constraint can maximize the benefit of unequal spacing in reducing the pressure pulsations for a lower risk of engine or transmission whine. The scope of this paper is around the optimization process for vane spacing and different objective functions which can be used to obtain optimized solutions. The simulation results for optimized spacing based on two different objective functions for 7, 8 and 9 vanes are presented. The design constraints for the optimization are discussed as well.

The validation of an assembly of component finite element models to high frequencies is a difficult challenge. Basic steps coupled with advanced correlation techniques are required to produce system finite element models that correlate to modal test data. This paper describes those steps as they were applied to a system model of a complex transaxle.

To manage the function of a vehicle's engine, transmission, and related subsystems, almost all modern vehicles make use of one or more electronic controllers running embedded software, henceforth referred to as a Powertrain Controller System or PCS. Fully validating this PCS is a necessary step of vehicle development, and the validation process requires extensive amounts of testing. Traditionally, this validation testing is done with open-loop signal generators, powertrain dynamometers, and real vehicles. Such testing methods either cannot simulate complex control system interactions, or are expensive and subject to variability. To address these concerns while decreasing development time and improving vehicle quality, Ford Motor Company is placing increasing focus on validating a PCS through simulation. One such testing method is a Hardware-in-the-Loop (HIL) simulation, which mates the physical elements of a PCS to a real-time computer simulation of a powertrain.

Bluetooth Low Energy (BLE) is an energy-efficient radio communication technology that is rapidly gaining popularity for various Internet of Things (IoT) applications. While BLE was not designed specifically with vehicular communications in mind, its simple and quick connection establishment mechanisms make BLE a potential inter-vehicle communication technology, either replacing or complementing other vehicle-to-vehicle (V2V) technologies (such as the yet to be deployed DSRC). In this paper we propose a framework for V2V communication using BLE and evaluate its performance under various configurations. BLE uses two major methods for data transmission: (1) undirected advertisements and scanning (unconnected mode) and (2) using the central and peripheral modes of the Generic Attribute Profile (GATT) connection (connected mode).

Statistical Energy Analysis (SEA) is used to predict high-frequency acoustic and vibration response in vehicle NVH design. Early in the design cycle prototype hardware is not yet available for testing and the geometry is still too poorly defined and changing too quickly for Finite Element Analysis or Boundary Element Analysis to be an effective NVH analysis tool. For most of the concept phase and early design phase, SEA uniquely offers the ability to virtually predict the main noise transfer paths and to support target setting for component and full vehicle NVH design. At later stages of the design process, SEA combines with NVH testing to provide more accurate predictions and to provide guidance for more efficient testing. This paper describes the established uses of SEA in the vehicle industry and presents an overview of the NVH design cycle and how SEA is used to support NVH development at different stages.

Compressible plastic foams are used throughout the interior and bumper systems of modern automobiles for safety enhancement and damage prevention. Consequently, modeling of foams has become very important for automobile engineers. To date, most work has focused on predicting foam performance up to approximately 80% compression. However, in certain cases, it is important to predict the foam under maximum compression, or ‘bottoming-out.’ This paper uses one such case-a thin low-density bumper foam impacted by a pedestrian leg-form at 11.1 m/s-to investigate the ‘bottoming-out’ phenomenon. Multiple material models in three different explicit Finite Element Method (FEM) packages (RADIOSS, FCRASH, and LS-DYNA) were used to predict the performance. The finite element models consisted of a foam covered leg-form impacting a fixed bumper beam with a foam energy absorber.

Previously fuel consumption on a drive cycle has been shown to be proportional to traction work, with an offset for powertrain losses. This model had different transfer functions for different drive cycles, performance levels, and applied powertrain technologies. Following Soltic it is shown that if fuel usage and traction work are both expressed in terms of cycle average power, a wide range of drive cycles collapse to a single transfer function, where cycle average traction power captures the drive cycle and the vehicle size. If this transfer function is then normalized by weight, i.e. by working in cycle average power/weight (P/W), a linear model is obtained where the offset is mainly a function of rated performance and applied technology. A final normalization by rated power/weight as the primary performance metric further collapses the data to express the cycle average fuel power/rated power ratio as a function of cycle average traction power/rated power ratio.

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

Wet clutch packs are widely used in today’s automatic transmission systems for gear-ratio shifting. The frictional interfaces between the clutch plates are continuously lubricated with transmission fluid for both thermal and friction management. The open clutch packs shear transmission fluid across the rotating plates, contributing to measurable energy losses. A typical multi-speed transmission includes as many as 5 clutch packs. Of those, two to three clutches are open at any time during a typical drive cycle, presenting an opportunity for fuel economy gain. However, reducing open clutch drag is very challenging, while meeting cooling requirements and shift quality targets. In practice, clutch design adjustment is performed through trial-and-error evaluation of hardware on a test bench. The use of analytical methodologies is limited for optimizing clutch design features due to the complexity of fluid-structure interactions under rotating conditions.