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The potential for Augmented Reality (AR) spans many domains. Among other applications, AR can improve the discovery and learning experience for users inspecting a particular item. This paper discusses the use of AR in the automotive context; particularly, on improving the user experience in a dealership show room. Visual augmentation, through a tablet computer or glasses allows users to take part in a self-guided tour in learning about the various features, details, and options associated with a vehicle. The same approach can be applied to other learning scenarios, such as training and maintenance assistance. We evaluated a set of AR Glasses and a general purpose tablet. A table-top showroom was developed demonstrating what the actual user experience would be like for a self-guided dealership tour using natural markers and three-dimensional content spatially registered to physical objects in the user’s field of view.

In this paper, the tradeoff relationship between the Air Conditioning (A/C) system performance and vehicle fuel economy for a hybrid electric vehicle during the SC03 drive cycle is presented. First, an A/C system model was integrated into Ford’s HEV simulation environment. Then, a system-level sensitivity study was performed on a stand-alone A/C system simulator, by formulating a static optimization problem which minimizes the total energy use of actuators, and maintains an identical cooling capacity. Afterwards, a vehicle-level sensitivity study was conducted with all controllers incorporated in sensitivity analysis software, under three types of formulations of cooling capacity constraints. Finally, the common observation from both studies, that the compressor speed dominates the cooling capacity and the EDF fan has a marginal influence, is explained using the thermodynamics of a vapor compression cycle.

Software for autonomous vehicles is highly complex and requires vast amount of vehicle testing to achieve a certain level of confidence in safety, quality and reliability. According to the RAND Corporation, a 100 vehicle fleet running 24 hours a day 365 days a year at a speed of 40 km/hr, would require 17 billion driven kilometers of testing and take 518 years to fully validate the software with 95% confidence such that its failure rate would be 20% better than the current human driver fatality rate [1]. In order to reduce cost and time to accelerate autonomous software development, Hardware-in-the-Loop (HIL) simulation is used to supplement vehicle testing. For autonomous vehicles, path following controls are an integral part for achieving lateral control. Combining the aforementioned concepts, this paper focuses on a real-time implementation of a path-following lateral controller, developed by Freund and Mayr [2].

Demand for enhanced infotainment systems with features like navigation, real-time traffic, music streaming service, mirroring and others is increasing, forcing automakers to develop solutions that fulfill customer needs. However, many of those systems are too expensive to be fitted to an entry-level vehicle leaving a gap in the market that fails customer’s expectation. This gap is usually filled by a smartphone which may have all the features the customer wants but in many cases it cannot be properly fitted in the vehicle due to lack of specific storage space. This paper describes how the engineering team developed an innovative, flexible and effective solution that holds a smartphone in an ergonomic location.

Use of sensors to monitor dynamic performance of machine tools at Ford’s powertrain machining plants has proven to be effective. The traditional approach to convert sensor data to actionable intelligence consists of identifying single features from cycle based signatures and setting thresholds above acceptable performance limits based on trials. The thresholds are used to discriminate between acceptable and unacceptable performance during each cycle and raise alarms if necessary. This approach requires a significant amount of resource & time intensive set up work up-front and considerable trial and error adjustments. The current state does not leverage patterns that might be discernible using multiple features simultaneously. This paper describes enhanced methods for processing the data using supervised and unsupervised machine learning methods. The objective of using these methods is to improve the prediction accuracy and reduce up-front set up.

Increasing number of vehicles are equipped with telematics devices and are able to transmit vehicle CAN bus information remotely. This paper examines the possibility of identifying individual drivers from their driving signatures embedded in these telematics data. The vehicle telematics data used in this study were collected from a small fleet of 30 Ford Fiesta vehicles driven by 30 volunteer drivers over 15 days of real-world driving in London, UK. The collected CAN signals included vehicle speed, accelerator pedal position, brake pedal pressure, steering wheel angle, gear position, and engine RPM. These signals were collected at approximately 5Hz frequency and transmitted to the cloud for offline driver identification modeling. A list of driving metrics was developed to quantify driver behaviors, such as mean brake pedal pressure and longitudinal jerk. Random Forest (RF) was used to predict driver IDs based on the developed driving metrics.

Liquid sloshing is an important issue in ground transportation, aerospace and automotive applications. Effects of sloshing in a moving liquid container can cause various issues related to vehicle stability, safety, component fatigue, audible noise and, liquid level measurement. The sloshing phenomenon is a highly nonlinear oscillatory movement of the free-surface of liquid inside a container under the effect of continuous or momentarily excitation forces. These excitation forces can result from sudden acceleration, braking, sharp turning or pitching motions. The sloshing waves generated by the excitation forces can impact on the tank surface and cause additional vibrations. For the loads with the frequencies between 2 to 200 Hz, the structural fatigue failure is a major concern for automotive applications.

An engine cooling system in an automotive vehicle comprises of heat exchangers such as a radiator, charge air cooler and oil coolers along with engine cooling fan. Typical automotive engine-cooling fan assembly includes an electric motor mounted on a shroud that encloses the radiator core. One of main drivers of fan shroud design is Noise, Vibration, and Harshness (NVH) requirements without compromising the main function of airflow for cooling requirements. In addition, there is also a minimum stiffness requirement of fan shroud which is often overlooked in arriving at optimal design of it. Low Speed Damageability (LSD) assessment of an automotive vehicle is about minimizing the cost of repair of vehicle damages in low speed crashes. In low speed accidents, these fan motors are subjected to sudden decelerations which cause fan motors to swing forward thereby damaging the radiator core. So designing fan shroud for low speed damageability is of importance today.

The Pelvis-Thorax Side Air Bag (PTSAB) is a typical restraint countermeasure offered for protection of occupants in the vehicle during side impact tests. Currently, the dynamic performance of PTSAB for occupant injury assessment in side impact is limited to full-vehicle evaluation and sled testing, with limited capability in computer aided engineering (CAE). The widely used CAE method for PTSAB is a flat bag with uniform pressure. The flat PTSAB model with uniform pressure has limitations because of its inability to capture airbag deployment during gap closure which results in reduced accuracy while predicting occupant responses. Hence there is a need to develop CAE capability to enhance the accuracy of prediction of occupant responses to meet performance targets in regulatory and public domain side impact tests. This paper describes a new CAE methodology for assessment of PTSAB in side impact.

Pedestrian protection assessment methods require multiple head impact tests on a vehicle’s hood and other front end parts. Hood surfaces are often lifted up by using pyrotechnic devices to create more deformation space prior to pedestrian head impact. Assessment methods for vehicles equipped with pyrotechnic devices must also validate that the hood deployment occurs prior to head impact event. Estimation of pedestrian head impact time, thus, becomes a critical requirement for performance validation of deployable hood systems. In absence of standardized physical pedestrian models, Euro NCAP recommends a list of virtual pedestrian models that could be used by vehicle manufacturers, with vehicle FEA (Finite Element Analysis) models, to predict the potential head impact time along the vehicle front end profile. FEA simulated contact time is used as target for performance validation of sensor and pyrotechnic deployable systems.

Industry trends towards lighter, more aerodynamically efficient road vehicles have the potential to degrade a vehicle’s response to crosswinds. In this paper, a methodology is outlined that indirectly couples a computational fluid dynamics (CFD) simulation of the vehicle’s aerodynamic characteristics with a multi-body dynamics simulation (MBD) to determine yaw, roll and pitch response characteristics during a severe crosswind event. This one-way coupling approach mimics physical test conditions outlined in open loop test procedure ISO 12021:2010 that forms part of the vehicle sign-off criterion at Ford Motor Company. The methodology uses an overset mesh CFD method to drive the vehicle through a prescribed crosswind event, providing unfiltered predictions of vehicle force and moment responses that are used as applied forces in the MBD model. The method does not account for changes in vehicle attitude due to applied aerodynamic forces and moments.

Powertrain drivability evaluation and calibration is an important part of vehicle development to enhance the customer experience. This step mainly takes place on vehicle testing very late in the product development cycle, and is associated with a considerable amount of prototype, test facility, human resource and time cost. Design change options at this stage are also very limited. To reduce the development cost, a model based computer aided engineering (CAE) method is introduced and combined with hardware-in-the-loop (HIL) simulation technology. The HIL simulation method offers a possibility for drivability prediction and development in early phase of product cycle. This article describes the drivability HIL simulation process under development in Ford. The process consists of real time capable multi-domain CAE model integration, powertrain control module (PCM) and HIL simulator interface development and drivability HIL simulation.

Simulink is a very successful and popular method for modelling and auto-coding embedded automotive features, functions and algorithms. Due to its history of success, university feeder programs, and large third party tool support, it has, in some cases, been applied to areas of the software system where other methods, principles and strategies may provide better options for the software and systems engineers and architects. This paper provides approaches to determine when best to apply UML and when best to apply Simulink to a typical automotive feature. Object oriented software design patterns as well as general guidelines are provided to help in this effort. This paper's intent is not to suggest a replacement for Simulink but to provide the software architects and designers additional options when decomposing high level requirements into reusable software components.

Modern vehicles become increasingly software intensive. Software development therefore is critical to the success of the manufacturer to develop state of the art technology. Standards like ISO 26262 recommend requirement-based verification and test cases that are derived from requirements analysis. Agile development uses continuous integration tests which rely on test automation and evaluation. All these drove the development of a new model-based software verification environment. Various aspects had to be taken into account: the test case specification needs to be easily comprehensible and flexible in order to allow testing of different functional variants. The test environment should support different use cases like open-loop or closed-loop testing and has to provide corresponding evaluation methods for continuously changing as well as for discrete signals.

The proposed technique is a tailored deep neural network (DNN) training approach which uses an iterative process to support the learning of DNNs by targeting their specific misclassification and missed detections. The process begins with a DNN that is trained on freely available annotated image data, which we will refer to as the Base model, where a subset of the categories for the classifier are related to the automotive theater. A small set of video capture files taken from drives with test vehicles are selected, (based on the diversity of scenes, frequency of vehicles, incidental lighting, etc.), and the Base model is used to detect/classify images within the video files. A software application developed specifically for this work then allows for the capture of frames from the video set where the DNN has made misclassifications. The corresponding annotation files for these images are subsequently corrected to eliminate mislabels.

Engine air induction systems hydrocarbon trap (HC trap) designs to limit evaporative fuel emissions, have evolved over time. This paper discusses a range of HC traps that have evolved in engine air induction systems. (AIS) The early zeolite flow through HC trap utilized an exhaust catalyst technology internal stainless steel furnace brazed substrate coated with zeolite media. This HC trap was installed in the AIS clean air tube. This design was heavy, complicated, and expensive but met the urgency of the implementation of the new evaporative emissions regulation. The latest Ford Motor Company HC trap is a simple plastic tray containing activated carbon with breathable non-woven polyester cover. This design has been made common across multiple vehicle lines with planned production annual volume in the millions. The cost of the latest HC trap bypass design is approximately 5% of the original stainless steel zeolite flow through HC trap.

Thermal modeling of liquid-cooled vehicle traction battery assemblies using Computational Fluid Dynamics (CFD) usually involves large models to accurately resolve small cooling channel details, and intensive computation to simulate drive-cycle transient solutions. This paper proposes a segregated method to divide the system into three parts: the cells, the cold plate and the interface between them. Each of the three parts can be separated and thermally characterized and then combined to predict the overall system thermal behavior for both steady-state and transient operating conditions. The method largely simplifies battery thermal analysis to overcome the limitations of using large 3D CFD models especially for pack level dynamic drive cycle simulations.

This paper proposes an approach that uses the road preview data to optimize a shift schedule for a vehicle equipped with an automatic transmission. The road preview is inferred here from the so-called electronic horizon of a digital map that includes road attributes such as road grade, curvature, segment speed limit, functional class, etc. The optimized shift schedule selects the gear ratio whose optimization is conducted through applying a hybrid model predictive control method to the powertrain system, which is modelled as the multiple plants associated with multiple gears together with engine models. The goal of this optimization of shift schedule includes improving real world fuel economy and drivability. The real-world fuel economy gains using the proposed approach are achieved through optimizing gear ratio w.r.t. the road grade variations of the road ahead.

Model low temperature NOx adsorbers (LTNA) consisting of Pd on a ceria/zirconia washcoat on monoliths were evaluated for low temperature NOx storage under lean conditions to assess their potential for adsorbing the cold-start NOx emissions on a diesel engine during the period before the urea/SCR system becomes operational. A reactor-based transient test was performed with and without C2H4, CO/H2, and H2O to assess the effects of these species on the NOx storage performance. In the absence of C2H4 or CO/H2, H2O severely suppressed the NOx storage of these model LTNAs at temperatures below 100°C, presumably by blocking the storage sites. When C2H4 was included in the feedgas, H2O still suppressed the NOx storage below 100°C. However, the C2H4 significantly increased the NOx storage efficiency above 100°C, attributable to the formation of alkyl nitrites or alkyl nitrates on the catalyst.

Rivets, especially self-piercing rivets (SPR), are a primary joining technology used in aluminum bodied vehicles. SPR are mechanical joining elements used to connect sheets to create a body in white (BiW) structure. To ensure the structural performance of a vehicle in crash load cases it is necessary to describe physical occurring failure modes under overloading conditions in simulations. One failure mode which needs to be predicted precisely by a crash simulation is joint separation. Within crash simulations a detailed analysis of a SPR joint would require a very high computational effort. The conflict between a detailed SPR joint and a macroscopic vehicle model needs to be solved by developing an approach that can handle an accurate macroscopic prediction of SPR behavior with a defined strength level with less computational effort. One approach is using a cohesive material model for a SPR connection. The paper describes cohesive element characteristics and calibration effort.