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Wheel Fight is the undesirable rotational response of a vehicle's steering wheel due to road input at any or all of the road/wheel tire patches. The type of road input that will cause wheel fight comes in two forms: continuous rough road surfaces such as broken concrete or transient inputs such as pot-holes and tar strips. An objective method to quantify a vehicle's wheel fight sensitivity would be of great value to the vehicle development engineer. To that end, a study was conducted on Ford's Vehicle Vibration Simulator (VVS) to gather subjective responses and use those as a basis for correlation to an objective metric. One road surface known to induce wheel fight consists of using a rubber strip and driving over it while impacting only one side of the vehicle. Under this condition, steering wheel data was acquired on five different light trucks from which paired comparison studies were conducted.

Ford Motor Company has investigated a series hybrid electric vehicle (SHEV) configuration to move further toward powertrain electrification. This paper first provides a brief overview of the Vehicle System Controls (VSC) architecture and its development process. The paper then presents the energy management strategies that select operating modes and desired powertrain operating points to improve fuel efficiency. The focus will be on the controls design and optimization in a Model-in-the-Loop environment and in the vehicle. Various methods to improve powertrain operation efficiency will also be presented, followed by simulation results and vehicle test data. Finally, opportunities for further improvements are summarized.

Vehicle thermal loads in sunny climates are strongly influenced by the absorption of solar thermal energy. Reduction of the absorptivity in the near infrared (IR) spectrum would decrease vehicle soak temperatures, reduce air conditioning power consumption and not affect the vehicle visible spectrum radiation properties (color). The literature [1] indicates that paint formulations with carbon-black pigment removed or reduced can be made to be reflective to near infrared frequencies. Experiments indicated that the reflectivity can be improved with existing basecoats and primers. Experiments and numerical simulations indicate that vehicle soak temperatures can be reduced by over 2 °C with existing basecoats and primers.

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.

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.

An intervention of vehicle stability control systems is more likely on slippery surfaces, e.g. when the road is covered with snow or ice. Contrary to testing on dry asphalt, testing on such surfaces is restricted by weather and proving grounds. Another drawback in testing is the reproducibility of measurements, since the surface condition changes during the tests, and the vehicle reaction is more sensitive on slippery surface. For that, simulation enables a good pre-assessment of the control systems independent from testing conditions. Essential for this is a good knowledge about the contact between vehicle and road, meaning a good tyre model and a reasonable set of tyre model parameters. However, the low friction surface has a high variation in the friction coefficient. For instance, the available lateral acceleration on scraped ice could vary between 0.2 and 0.4 g within a day. These facts lead to the idea of using generic tyre parameters that vary in a certain range.

The air-hybrid engine absorbs the vehicle kinetic energy during braking, puts it into storage in the form of compressed air, and reuses it to assist in subsequent vehicle acceleration. In contrast to electric hybrid, the air hybrid does not require a second propulsion system. This approach provides a significant improvement in fuel economy without the electric hybrid complexity. The paper explores the fuel economy potential of an air hybrid engine by presenting the modeling results of a 2.5L V6 spark-ignition engine equipped with an electrohydraulic camless valvetrain and used in a 1531 kg passenger car. It describes the engine modifications, thermodynamics of various operating modes and vehicle driving cycle simulation. The air hybrid modeling projected a 64% and 12% of fuel economy improvement over the baseline vehicle in city and highway driving respectively.

This paper compares the results from three human factors studies conducted in a motion-based simulator in 2008, 2014 and 2023, to highlight the trends in driver's response to Forward Collision Warning (FCW). The studies were motivated by the goal to develop an effective HMI (Human-Machine Interface) strategy that enables the required driver's response to FCW while minimizing the level of annoyance of the feature. All three studies evaluated driver response to a baseline-FCW and no-FCW conditions. Additionally, the 2023 study included two modified FCW chime variants: a softer FCW chime and a fading FCW chime. Sixteen (16) participants, balanced for gender and age, were tested for each group in all iterations of the studies. The participants drove in a high-fidelity simulator with a visual distraction task (number reading). After driving 15 minutes in a nighttime rural highway environment, a surprise forward collision threat arose during the distraction task.

This paper identifies and analyzes steady-state and transient tire properties affecting vehicle directional response characteristics. The study is limited to the relationship between lateral force and slip angle. It shows fundamental differences between steady-state and transient properties. Tire transient properties are described by a force-slip angle loop with cornering stiffness and dynamic lateral force offset as parameters. Cornering stiffness is presented as a variable that changes with speed and steer rate. An interrelationship between cornering stiffness and dynamic lateral force offset resulting from the time lag between lateral force and slip angle is shown. Ramp steer techniques for measuring transient tire properties on a road trailer and on an external drum machine are described. A need for transient tire data for computer simulations of vehicle transient steer maneuvers is shown.

The Vehicle Handling Model (VHM) is representative of a new type of vehicle dynamics programs which can be easily used on a personal computer by vehicle development engineers. It consists of a simulation kernel which solves the vehicle equations of motion and a hypertext GUI which controls the model data input, execution, and post processing. The vehicle model has 5 DOF, including the vehicle lateral, vertical, yaw, pitch, and roll motions. The simulation also includes suspension compliance, a simple non-linear tire model, a wind gust model and a human driver model to provide realistic vehicle and steering inputs. The simulation program was generated by AUTOSIM which uses a high level description of the system to generate Fortran source code. The GUI allows an engineer to setup the model, run the analysis, and display the results with just a few clicks of the mouse.

A driving simulator development project at the Systems Engineering and Technical Process Center (SE/TP) is exploring the role of driving simulation in the vehicle design process. The simulator provides two vehicle mockup testing arenas that support a wide field of view, computer-generated image of the road scene which dynamically responds to driver commands as a function of programmable vehicle model parameters. Two unique aspects of the simulator are the fast 65 ms response time and low incidence rate of simulator induced syndrome (about 5%). Preliminary model validation results and data comparing driver performance in a vehicle vs. the simulator indicate accurate handling response dynamics within the on-center handling region (<0.3g lateral acceleration). Applications have included supporting the development of new steering system concepts, as well as evaluating the usability of vehicle controls and displays.

This paper describes the design and development of the Ford Driving Simulator. The simulator is a fixed-base device which provides real-time, interactive feedback to the driver through a combination of visual, auditory and tactile cues. The system is comprised of a modular buck, 150° field-of-view visual scene, a steering torque controller, high fidelity dynamics models, and an interactive experimenters station. Data acquisition systems have been developed to capture a broad spectrum of driver performance metrics.

The evolution and development of the automobile radio antenna is perhaps one of the most neglected success stories in the automotive industry. Born in the twilight of the last century, it evolved from a simple wire wrapped around a tree branch, to the current heated rear screen or backlite antenna. Part One (SAE No. 870090) described seven types of antennas in detail, covering the period 1897-1937. It was shown how the early radio engineers, struggling to develop a viable car antenna, had displayed a great degree of creativity and flexibility, from the “firecracker” experiments of Guglielmo Marconi in 1897, to the ingenious systems developed to overcome the problems created by the all-metal Turret-Top vehicles introduced by General Motors in 1934. In those pioneering days, the United States public was having a love affair with both the automobile and radio broadcasting, so it was no surprise that their marriage did not take long to arrive.

Seat Effective Amplitude Transmissibility (SEAT) values can be obtained from direct measurements at seat track and top or estimated from transmissibility data and seat track input. Vertical transmissibility was measured for sixteen seats and six subjects on the Ford Vehicle Vibration Simulator, and these 96 functions used to estimate the seat top response for rough road input. SEAT values were calculated, and good correlation to values computed from direct seat top measurements obtained (R2 of 0.86). Averaging transmissibilities and direct seat measurements over the 6 subjects to obtain correlations for the 16 seats improved R2 to 0.94, validating this approach.

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.

In order to engineer Squeak & Rattle (S&R) free vehicles it is essential to develop an objective measurement method to compare and correlate with customer satisfaction and subjective S&R assessments. Three methods for exciting S&Rs -type surfaces. Excitation methods evaluated were road tests over S&R surfaces, road simulators, and direct body excitation (DBE). The principle of DBE involves using electromagnetic shakers to induce controlled, road-measured vibration into the body, bypassing the tire patch and suspension. DBE is a promising technology for making objective measurements because it is extremely quiet (test equipment noise does not mask S&Rs), while meeting other project goals. While DBE is limited in exposing S&Rs caused by body twist and suspension noises, advantages include higher frequency energy owing to electro-dynamic shakers, continuous random excitation, lower capital cost, mobility, and safety.

Globalization in the automotive industry has resulted in a tremendous competitive advantage to those companies who can internally communicate ideas and information effectively and in a timely manner. This paper discusses one such effort related to objectively testing vehicles for steering and handling characteristics by implementing standard test procedures, data acquisition hardware and analysis methods. Ford Motor Company's Vehicle Dynamics Test Section has refined a number of test procedures to the point that, with proper training, all design and development engineers can quickly acquire, analyze and share test results. Four of these procedures and output are discussed in detail.

Hydroformed components are replacing stamped parts in automotive frames and front end and roof structures to improve the crash performance of vehicles. Due to the increasing application of hydroformed components, a better understanding of the crash behavior of these parts is necessary to improve the correlation between full-vehicle crash tests and FEM analysis. Accurately predicting the performance of hydroformed components will reduce the amount of physical crash testing necessary to develop the new components and new vehicles as well as reduce cycle time. Virgin material properties are commonly used in FEM analysis of hydroformed components, which leads to erroneous prediction of the full-vehicle crash response. Changes in gauge and material properties during the hydroforming process are intuitive and can be reasonably predicted by using forming simulations. The effects of the forming process have been investigated in the FEA models that are created for crash analyses.

A hybrid electric vehicle (HEV) system model, which directly simulates vehicle drive cycles with interactions among driver, environment, vehicle hardware and vehicle controls, is a critical CAE tool used through out the product development process to project HEV fuel economy (FE) capabilities. The accuracy of the model is essential and directly influences the HEV hardware designs and technology decisions. This ultimately impacts HEV product content and cost. Therefore, improving HEV system model accuracy and establishing high-level model-test correlation are imperative. This paper presents a Parameter Diagram (P-Diagram) based model-test correlation framework which covers all areas contributing to potential model simulation vs. vehicle test differences. The paper describes each area in detail and the methods of characterizing the influences as well as the correlation metrics.

To better reflect real world driving conditions, the EPA 5-Cycle Fuel Economy method encompasses high vehicle speeds, aggressive vehicle accelerations, climate control system use and cold temperature conditions in addition to the previously used standard City and Highway drive cycles in the estimation of vehicle fuel economy. A standard Powersplit Hybrid Electric Vehicle (HEV) system simulation environment has long been established and widely used within Ford to project fuel economy for the standard EPA City and Highway cycles. Direct modeling and simulation of the complete 5-Cycle fuel economy test set for HEV's presents significant new challenges especially with respect to modeling vehicle thermal management system and interactions with HEV features and system controls. It also requires a structured, systematic approach to validate the key elements of the system models and complete vehicle system simulations.