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The Ford GT Program Team was allocated just 22 months from concept to production to complete the Electrical and Electronics systems of the Ford GT. This reduced vehicle program timing - unlike any other in Ford's history -- demanded that the team streamline the standard development process, which is typically 54 months. This aggressive schedule allowed only 12 weeks to design the entire electrical and electronic system architecture, route the wire harnesses, package the components, and manufacture and/or procure all components necessary for the first three-vehicle prototype build.

The High Voltage Power Distribution Unit (PDU) is constructed of magnesium in support of Fuel Cell Electric Vehicle (FCEV) weight reduction efforts. The PDU distributes and controls a nominal 75 kilowatts of power generated by the Fuel Cell, the primary source of High Voltage power, to all the vehicle loads and accessories. The constraints imposed on the design of the PDU resulted in a component highly susceptible to general and galvanic corrosion. Corrosion abatement was the focus of the PDU redesign. This paper describes the redesign efforts undertaken by Ford personnel to improve the part robustness and corrosion resistance.

A large amount of heat is generated in electric vehicle battery packs during high rate charging, resulting in the need for effective cooling methods. In this paper, a prototype liquid cooled large format Lithium-ion battery module is modeled and tested. Experiments are conducted on the module, which includes 31Ah NMC/Graphite pouch battery cells sandwiched by a foam thermal pad and heat sinks on both sides. The module is instrumented with twenty T-type thermocouples to measure thermal characteristics including the cell and foam surface temperature, heat flux distribution, and the heat generation from batteries under up to 5C rate ultra-fast charging. Constant power loss tests are also performed in which battery loss can be directly measured.

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.

A study was conducted using Ford's nine standard CFD calibration models as described in SAE paper 940323. The models are identical from the B-pillar forward but have different back end configurations. These models were created for the purpose of evaluating the effect of back end geometry variations on aerodynamic lift and drag. Detailed experimental data is available for each model in the form of surface pressure data, surface flow visualization, and wake flow field measurements in addition to aerodynamic lift and drag values. This data is extremely useful in analyzing the accuracy of the numerical simulations. The objective of this study was to determine the capability of a digital physics based commercial CFD code, PowerFLOW ® to accurately simulate the physics of the flow field around the car-like benchmark shapes.

The reduction of automotive emissions is instrumental in the fight against air pollution and its impact on global warming. This realization has empowered governments around the world to mandate lower levels of vehicle emissions requiring the Original Equipment Manufacturers (OEMs) to implement advanced aftertreatment technologies in their applications. Achieving emission levels as low as SULEV30 or SULEV20 would have been impossible only a couple of decades ago, however, these lower levels of emissions are now a possibility through advanced control strategies and aftertreatment systems. As a part of this mandate to lower emissions, OEMs are also continuously monitoring the health and performance of their aftertreatment and control components. The implementation of On Board Diagnostics (OBD) ensures that control systems are functioning robustly and the emission levels are achieved and maintained to high mileages for the life of the vehicle.

Ford Motor Company has recently implemented a Hardware-In-the-Loop (HIL) testing system for a new, highly complex, hybrid electric vehicle (HEV) Electronic Control Unit (ECU). The implementation of this HIL system has been quick and effective, since it is based on proven Commercial-Off-The-Shelf (COTS) automation tools for real-time that allow for a very flexible and intuitive design process. An overview of the HIL system implementation process and the derived development benefits will be shown in this paper. The initial concept for the use of this HIL system was a complete closed-loop vehicle simulation environment for Vehicle System Controller testing, but the paper will show that this concept has evolved to allow for the use of the HIL system for many facets of the design process.

Engine air induction shell noise is a structure borne noise that radiates from the surface of the air induction system. The noise is driven by pulsating engine induction air and is perceived as annoying by vehicle passengers. The problem is aggravated by the vehicle design demands for low weight components packaged in an increasingly tight under hood environment. Shell noise problems are often not discovered until production intent parts are available and tested on the vehicle. Part changes are often necessary which threatens program timing. Shell noise should be analyzed in the air induction system design phase and a good shell noise analytical process and targets must be defined. Several air induction clean side ducts are selected for this study. The ducts shell noise is assessed in terms of material strength and structural stiffness. A measurement process is developed to evaluate shell noise of the air induction components. Noise levels are measured inside of the clean side ducts.

In future vehicles (e.g. fuel cell vehicles, hybrid electric vehicles), the electrical system will have an important impact on the mechanical systems in the car (e.g. powertrain, steering). Furthermore, this coupling will become increasingly important over time. In order to develop effective designs and appropriate control systems for these systems, it is important that the plant models capture the detailed physical behavior in the system. This paper will describe models of two electrical components, a battery and a supercapacitor, which have been modeled in two ways: (i) modeling the plant and controller using block diagrams in Simulink and (ii) modeling the plant and controller in Dymola followed by compiling this model to an S-function for simulation in Simulink. Both the battery and supercapacitor model are based on impedance spectroscopy measurements and can be used for highly dynamic simulations.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

This paper presents a design process with data mining technique and advanced optimization strategy. The proposed design method provides insights in three aspects. First, data mining technique is employed for analysis to identify key factors of design variables. Second, relationship between multiple types of size and shape design variables and performance responses can be analyzed. Last but not least, design preference can be initialized based on data analysis to provide priori guidance for the starting design points of optimization algorithm. An exhaust system design problem which largely contributes to the improvement of vehicular Noise, Vibration and Harshness (NVH) performance is employed for the illustration of the process. Two types of design parameters, structural variable (gauge of component) and layout variable (hanger location), are considered in the studied case.

The wide applications of automatic sensing devices and data acquisition systems in automotive manufacturing have resulted in a data-rich environment, which demands new data mining methodologies for effective data fusion and information integration to support decision making. This paper presents a new methodology for developing a diagnostic system using manufacturing system data for high-value assets in automotive manufacturing. The proposed method extends the basic attributes control charts with the following key elements: optimal feature subset selection considering multiple features and correlation structure, balancing the type I and type II errors in decision making, on-line process monitoring using adaptive modeling with control charts, and diagnostic performance assessment using shift and trend detection. The performance of the developed diagnostic system can be continuously improved as the knowledge of machine faults is automatically accumulated during production.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

This paper proposes a domain-centralized powertrain E/E (electrical and/or electronic) architecture for all-electric vehicles that features: a powerful master controller (domain controller) that implements most of the functionality of the domain; a set of smart actuators for electric motor(s), HV (High Voltage) battery pack, and thermal management; and a gateway that routes all hardware signals, including digital and analog I/O, and field bus signals between the domain controller and the rest of the vehicle that is outside of the domain. Major functional safety aspects of the architecture are presented and a safety architecture is proposed. The work represents an early E/E architecture proposal. In particular, detailed partitioning of software components over the domain’s Electronic Control Units (ECUs) has not been determined yet; instead, potential partitioning schemes are discussed.

Brake squeal has been a chronic customer complaint, often appearing high on the list of items that reduce customers' satisfaction with their vehicles. Brake squeal can emanate from either a drum brake or a disc brake even though the geometry of the two systems is significantly different. A drum brake generates friction within a cylindrical drum interacting with two semi-circular linings. A disc brake consists of a flat disc and two flat pads. The observed squeal behavior in a vehicle differs somewhat between drum and disc brakes. A drum brake may have a loud noise coming from three or more squeal frequencies, whereas a disc brake typically has one or two major squeal frequencies making up the noise. A good understanding of the operational deflection shapes of the brake components during noise events will definitely aid in design to reduce squeal occurrences and improve product quality.

One of the first steps in powertrain design is to assess its best performance and consumption in a virtual phase. Regarding hybrid electric vehicles (HEVs), it is important to define the best mode profile through a cycle in order to maximize fuel economy. To assist in that task, several off-line optimization algorithms were developed, with Dynamic Programming (DP) being the most common one. The DP algorithm generates the control actions that will result in the most optimal fuel economy of the powertrain for a known driving cycle. Although this method results in the global optimum behavior, the DP tool comes with a high computational cost. The charge-sustaining requirement and the necessity of capturing extremely small variations in the battery state of charge (SOC) makes this state vector an enormous variable. As things move fast in the industry, a rapid tool with the same performance is required.

Due to magnesium alloy's poor weldability, other joining techniques such as laser assisted self-piercing rivet (LSPR) are used for joining magnesium alloys. This research investigates the fatigue performance of LSPR for magnesium alloys including AZ31 and AM60. Tensile-shear and coach peel specimens for AZ31 and AM60 were fabricated and tested for understanding joint fatigue performance. A structural stress - life (S-N) method was used to develop the fatigue parameters from load-life test results. In order to validate this approach, test results from multijoint specimens were compared with the predicted fatigue results of these specimens using the structural stress method. The fatigue results predicted using the structural stress method correlate well with the test results.

In this study the finite element method is used to simulate a light truck multi-leaf spring system and its interaction with a driven axle, u-bolts, and interface brackets. In the first part of the study, a detailed 3-D FE model is statically loaded by fastener pre-tension to determine stress, strain, and contact pressure. The FE results are then compared and correlated to both strain gage and interface pressure measurements from vehicle hardware test. Irregular contact conditions between the axle seat and leaf spring are investigated using a design of experiments (DOE) approach for both convex and discrete step geometries. In the second part of the study, the system FE model is loaded by both fastener pre-tension and external wheel end loads in order to obtain the twist motion response. Torsional deflection, slip onset, and subsequent slip motion at the critical contact plane are calculated as a function of external load over a range of Coulomb friction coefficients.

A new method is demonstrated for rating the “severity” of a hypoid gear set duty cycle (revolutions at torque) using the intercept of T-N curve to support gearset selection and sizing decision across vehicle programs. Historically, it has been customary to compute a cumulative damage (using Miner's Rule) for a rotating component duty cycle given a T-N curve slope and intercept for the component and failure mode of interest. The slope and intercept of a T-N curve is often proprietary to the axle manufacturer and are not published. Therefore, for upfront sizing and selection purposes representative T-N properties are used to assess relative component duty cycle severity via cumulative damage (non-dimensional quantity). A similar duty cycle severity rating can also be achieved by computing the intercept of the T-N curve instead of cumulative damage, which is the focus of this study.

Variant modeling techniques have been developed to allow systems engineers to model multiple similar variants in a product line as a single variant model. In this paper, we expand on this past work to explore the extent to which variant modeling in SysML can be applied to a broad range of dissimilar systems, covering the entire domain of ground vehicles, in single reference architecture model. Traditionally, a system’s structure is decomposed into subsystems and components. However, this method is found to be ineffective when modeling variants that are functionally similar but structurally different. We propose to address this challenge by first decomposing the system not only by subsystem but also by high-level function. This pattern is particularly useful for situations where two variants perform the same function, but one variant performs the function using one subsystem, whereas the other variant performs the same function using one or more different subsystems.