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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.

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.

The study of spray-wall interaction is of great importance to understand the dynamics during fuel-surface impingement process in modern internal combustion engines. The identification of droplet post-impingement pattern (contact, transition, non-contact) and droplet characteristics can quantitatively provide an estimation of energy transfer for spray-wall interaction, thus further influencing air-fuel mixing and emissions under combusting conditions. Theoretical criteria of single droplet post-impingement pattern on a dry wall have been experimentally and numerically studied by many researchers to quantify the hydrodynamic droplet behaviors. However, apart from model fidelity, another issue is the scalability. A theoretical criterion developed from one case might not be well suited to another scenario. In this paper, a data-driven approach for single droplet-dry wall post-impingement pattern utilizing arithmetical machine learning classification methods is proposed and demonstrated.

There are several reasons why it can be daunting to apply Six Sigma to product creation. Foremost among them, the functional performance of new technologies is unknown prior to starting a project. Although, Design For Six Sigma (DFSS) was developed to overcome this difficulty, a lack of applicable in-class case studies makes it challenging to train the product creation community. The current paper describes an in-class project which illustrates how Six Sigma is applied to a simulated product creation environment. A toy construction set (TCS) project is used to instruct students how to meet customer expectations without violating cost, packaging volume and design-complexity constraints.

Subjective steering feel tuning and objective verification tests are conducted on vehicle prototypes that are a subset of the total number of buildable combinations of body style, drivetrain and tires. Limited development time, high prototype vehicle cost, and hence limited number of available prototypes are factors that affect the ability to tune and verify all the possible configurations. A new model-based process and a toolset have been developed to enhance the existing steering development process such that steering tuning efficiency and performance robustness can be improved. The innovative method utilizes the existing vehicle dynamics simulation and/or physical test data in conjunction with steering system control models, and provides users with simple interfaces which can be used by either CAE or development engineers to perform virtual tuning of the vehicle steering feel to meet performance targets.

Today's automotive and electronics technologies are evolving so rapidly that educators and industry are both challenged to re-educate the technological workforce in the new area before they are replaced with yet another generation. In early November 2009 Ford's Product Development senior management formally approved a proposal by the University of Detroit Mercy to transform 125 of Ford's “IC Engine Automotive Engineers” into “Advanced Electric Vehicle Automotive Engineers.” Two months later, the first course of the Advanced Electric Vehicle Program began in Dearborn. UDM's response to Ford's needs (and those of other OEM's and suppliers) was not only at the rate of “academic light speed,” but it involved direct collaboration of Ford's electric vehicle leaders and subject matter experts and the UDM AEV Program faculty.

The forming effects along with strain rate, actual material properties and weld effects have been found to be very critical for accurate prediction of crash responses especially the prediction of local deformation. As a result, crash safety engineers started to consider these factors in crash models to improve the accuracy of CAE prediction and reduce prototype testing. The techniques needed to incorporate forming simulation results, including thickness change, residual stresses and strains, in crash models have been studied extensively and are well known in automotive CAE community. However, a challenge constantly faced by crash safety engineers is the availability of forming simulation results, which are usually supplied by groups conducting forming simulations. The forming simulation results can be obtained by either using incremental codes with actual stamping processes or one-step codes with final product information as a simplified approach.

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.

A robust Vehicle Model Architecture (VMA) has been developed to support model-based Vehicle System Control (VSC) design work and, in general, model-based vehicle system engineering activities. It is based on a logical breakdown of the vehicle into key subsystems with supporting bus infrastructure for distribution of signals between subsystems. Primary physical interfaces between the top level subsystems have been defined. Subsystem models that comply with these interfaces can be easily plugged into the architecture for complete simulation of vehicle systems. The VMA encourages model re-use and sharing between project teams and, furthermore, removes key obstacles to sharing of models with suppliers.

Vehicle dynamics CAE capabilities has increased in the past few years, specially, for handling and steering attributes. However, secondary ride simulations are still highly depended on the tire model. Such tire model must be capable to simulate high order phenomenon such as impact and harshness transmissibility in three directions. In order to gather tire information sufficient to cope with these phenomena, one needs to perform a series of specific tests, and so be able to build the intended tire model. Still, there could be correlation issues. This whole process takes a lot of time and resources. This article presents a semi-analytical approach, using data gathered via wheel force transducers (WFTs) that are typically used for load cascading and durability purposes. The method main advantage is that since it relies on measured data at the wheel center, it is independent of a tire model, and, as such, it demands less time and resources.

This paper describes the issues encountered in using conventionally acquired tire test data for dynamic total vehicle handling simulations and the need for improved methodology. It describes the new test procedure that was used to acquire all three forces and three moments in a transient mode for a matrix of loads, slip and camber angles. A study of the test data supports the premises that the overturning moment, Mx, should not be neglected in dynamic simulations, and that the effects of camber should not be treated as only an independent, linearly additive, camber thrust. Instead of the conventional application of a bi-cubic regression fit to a six region data division, a new algorithm is applied. The data is divided differently into five regions in the α - Fz plane, and a variable format regression equation is applied as appropriate. The resulting regression coefficients matrix is readily usable in dynamic simulations, and is shown to have a superior curve fit to the test data.

Yaw rate of a vehicle is highly influenced by the lateral forces generated at the tire contact patch to attain the desired lateral acceleration, and/or by external disturbances resulting from factors such as crosswinds, flat tire or, split-μ braking. The presence of the latter and the insufficiency of the former may lead to undesired yaw motion of a vehicle. This paper proposes a steer-by-wire system based on fuzzy logic as yaw-stability controller for a four-wheeled road vehicle with active front steering. The dynamics governing the yaw behavior of the vehicle has been modeled in MATLAB/Simulink. The fuzzy controller receives the yaw rate error of the vehicle and the steering signal given by the driver as inputs and generates an additional steering angle as output which provides the corrective yaw moment.

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 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.

Automotive manufacturing presents unique challenges for ergonomic analysis. The variety of tasks and frequencies are typically not seen in other industries. Moving these challenges into the realm of digital human modeling poses new challenges and offers the opportunity to create and enhance tools brought over from the traditional reactive approach. Chiang et al. (2006) documented an enhancement to the Siemen's Jack Static Strength Prediction tool. This paper will document further enhancements to the ErgoSolver (formerly known as the Ford Static Strength Prediction Solver).

This paper focuses on the development of a framework of nonlinear finite element model validation for vehicle crash simulation. Integrated computational and test-based methods were discussed for validating computational models under physical, informational and model uncertaintes. Several methods were investigated to quantify transient time-domain data (functional data). The concept of correlation index was proposed to determine the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model. The methodologies developed in this paper can also be used for CAE model updating, parameter tuning, and model calibration.

A low-frequency vehicle shuffle can be excited when a reversal of torque occurs in a vehicle's drivetrain. It usually occurs during a throttle tip-in or tip-out event, or a static engagement shift event. This drivetrain shuffle vibration can introduce a vehicle fore-aft vibration that may affect the customer satisfaction of ride comfort and/or powertrain performance. Vehicle test data of the seat track acceleration from a 30 MPH wide-open-throttle tip-out event suggested a strong coupling between the CVT drivetrain shuffle and vehicle fore-aft vibration. An ADAMS based CVT model was developed and integrated into a full vehicle model for dynamic simulation of this vehicle shuffle issue. CAE DOE studies were performed to identify key vehicle and powertrain design parameters that could directly impact the vehicle shuffle vibration. Experimental tests were performed to verify the CAE design improvements of the CVT vehicle shuffle vibration.

Oil formulation has been varied to modify oil viscosity characteristics; the effect on the rubbing friction losses of a 2.4litre diesel engine has been investigated for a range of temperatures from -20 to around 60°C. The aims of the study were first, to examine the extent to which viscosity alone determined the effect of formulation changes, and second, to define an effective viscosity to relate changes in viscosity due to formulation and temperature to changes in engine friction. This effective viscosity is based on cold cranking simulator measurements at -30°C, high temperature high shear viscosity at 150°C and kinematic viscosity measurements at three intermediate temperatures to define the variation with temperature. The effective viscosity has been described using a modified Vogel equation, which is presented.

Living in the era of rising environmental sensibility and increasing gasoline prices, the development of a new environmentally friendly generation of vehicles becomes a necessity. Hybrid electric vehicles are one means of increasing propulsion system efficiency and decreasing pollutant emissions. In this paper, the series-parallel power-split configuration for Michigan Technological University's FutureTruck is analyzed. Mathematical equations that describe the hybrid power-split transmission are derived. The vehicle's differential equations of motion are developed and the system's need for a controller is shown. The engine's brake power and brake specific fuel consumption, as a function of its speed and throttle position, are experimentally determined. A control strategy is proposed to achieve fuel efficient engine operation. The developed control strategy has been implemented in a vehicle simulation and in the test vehicle.