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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.

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.

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.

Transient road disturbances excite complex vehicle responses involving the interaction of suspension/chassis, powertrain, and body systems. Typical ones are due to the interactions between tires and road expansion joints, railway crossings and other road discontinuities. Such transient disturbances are generally perceived as “impact harshness” due to the harshness perception as sensed by drivers through both sound and vibration. This paper presents a study of quantifying the effects of sound, steering wheel and seat/floorpan vibrations on the overall perception of the “impact harshness” during impact transient events. The Vehicle Vibration Simulator (VVS) of the Ford Research Laboratory was used to conduct this study. The results of the study show that sound and vibration have approximately equal impact on the overall perception of impact harshness. There is no evidence of interaction between sound and vibration.

This paper describes the use of an interactive NVH simulator in simulating and designing the sound character of a vehicle with a multi-mode engine and active exhaust valve. When designing a vehicle for sound quality, it is not sufficient to merely record some discreet operating conditions and modify these in a traditional sound quality program. The ability to simulate the sound quality of the vehicle over the full operating envelope is a necessity. Additionally, the ability to break down the sound contributions from intake, exhaust and other key contributors to the driver's ear, and manipulate these independently is also essential. In the case described here, an additional factor makes it mandatory that an accurate vehicle sound simulation is performed. The state of the engine and exhaust contribution, and thus the sound of the vehicle, change based on several parameters - vehicle speed, load demand and gear.

Modeling of High Voltage (HV) wires is an important aspect of vehicle safety simulations for electrified powertrains to understand the potential tearing of the wire sheath or pinching of HV wiring. The behavior of the HV wires must be reviewed in safety simulations to identify potential hazards associated with HV wire being exposed, severed, or in contact with ground planes during a crash event. Modeling HV wire is challenging due to the complexity of the physical composition of the wire, which is usually comprised of multiple strands bundled and often twisted together to form the HV electrical conductor. This is further complicated by the existence of external insulating sheathing materials to prevent HV exposure during normal operating conditions. This paper describes a proposed method to model and characterize different types of HV wires for usage in component- and vehicle-level safety models.

This paper investigates the effects on response time of a forward collision event in a repeated-measures design. Repeated-measures designs are often used in forward collision warning (FCW) testing despite concerns that the first exposure creates expectancy effects that may dilute or bias future outcomes. For this evaluation, 32 participants were divided into groups of 8 for an AA, BB, AB, BA design (A= No Warning; B=FCW alert). They drove in a high-fidelity simulator with a visual distraction task. After driving 15 min in a nighttime rural highway environment, a forward collision threat arose during the distraction task (Period 1). A second drive was then run and the forward collision threat was repeated again after ∼10 min (Period 2). The response times from these consecutive events were analyzed.

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].

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.

To-date the primary challenge in conducting aerodynamic CFD simulations of actual vehicles with realistically complex geometry has been the construction of a computational mesh. The CAD-to-Mesh processes used to-date have been laborious, often requiring many weeks of engineering time. In this paper we present a new technique to greatly expedite the CAD-to-Mesh process. The fundamentals of this technique are discussed followed by case studies that show that this technique can reduce the engineering time required for the CAD-to-Mesh process to just a few hours.

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.

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.