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Ford Motor Company is introducing “EcoBoost” gasoline turbocharged direct injection (GTDI) engine technology in the 2010 Lincoln MKS. A logical enhancement of EcoBoost technology is the use of E85 for knock mitigation. The subject of this paper is the optimal use of E85 by using two fuel systems in the same EcoBoost engine: port fuel injection (PFI) of gasoline and direct injection (DI) of E85. Gasoline PFI is used for starting and light-medium load operation, while E85 DI is used only as required during high load operation to avoid knock. Direct injection of E85 (a commercially available blend of ∼85% ethanol and ∼15% gasoline) is extremely effective in suppressing knock, due to ethanol's high inherent octane and its high heat of vaporization, which results in substantial cooling of the charge. As a result, the compression ratio (CR) can be increased and higher boost levels can be used.

In order to detect degradation of engine oil lubricant, bench testing along with a number of diesel-powered Ford trucks were instruments and tested. The purpose of the bench testing was primarily to determine performance aspects such as repeatability, hysteresis effects and so on. Vehicle testing was conducted by designing and installing a separate oil reservoir along with a circulation system which was mounted in the vicinity of the oil pan. An innovative oil sensor was directly installed on the reservoir which can measure five (5) independent oil parameters (viscosity, density, permittivity, conductance, temperature). In addition, the concept is capable of detecting the oil level continuously during normal engine operation. The sensing system consists of an ultrasonic transducer for the oil level detection as well as a Tuning Fork mechanical resonator for the oil condition measurement.

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.

Advanced High Strength Steels (AHSS) have been implemented in the automotive industry to balance the requirements for vehicle crash safety, emissions, and fuel economy. With lower ductility compared to conventional steels, the fracture behavior of AHSS components has to be considered in vehicle crash simulations to achieve a reliable crashworthiness prediction. Without considering the fracture behavior, component fracture cannot be predicted and subsequently the crash energy absorbed by the fractured component can be over-estimated. In full vehicle simulations, failure to predict component fracture sometimes leads to less predicted intrusion. In this paper, the feasibility of using computer simulations in predicting fracture during crash deformation is studied.

Electrochemical models of lithium ion batteries are today a standard tool in the automotive industry for activities related to the computer-aided engineering design, analysis, and optimization of energy storage systems for electrified vehicles. One of the challenges in the development or use of such models is the need of detailed information on the cell and electrode geometry or properties of the electrode and electrolyte materials, which are typically unavailable or difficult to retrieve by end-users. This forces engineers to resort to “hand-tuning” of many physical and geometrical parameters, using standard cell-level characterization tests. This paper proposes a method to provide information and data on individual electrode performance that can be used to simplify the calibration process for electrochemical models.

Engine downsizing, boosting, direct injection and variable valve actuation, have become industry standards for reducing CO2 emissions in current production vehicles. Because of the increasing complexity of the engine air path system and the high number of degrees of freedom for engine charge management, the design of air path control algorithms has become a difficult and time consuming process. One possibility to reduce the control development time is offered by Software-in-the-Loop (SIL) or Hardware-in-the-Loop (HIL) simulation methods. However, it is significantly challenging to identify engine air path system simulation models that offer the right balance between fidelity, mathematical complexity and computational burden for SIL or HIL implementation.

At various milestones during a vehicle’s development program, different CAE models are created to assess NVH error states of concern. Moreover, these CAE models may be developed in different commercial CAE software packages, each one with its own unique advantages and strengths. Fortunately, due to the wide spread acceptance that the Functional Mock-up Interface (FMI) standard gained in the CAE community over the past few years, many commercial CAE software now support cosimulation in one form or the other. Cosimulation allows performing multi-domain/multi-resolution simulations of the vehicle, thereby combining the advantages of various modeling techniques and software. In this paper, we explore cosimulation of full 3D vehicle model developed in MSC ADAMS with 1D driveline model developed in LMS AMESim. The target application of this work is investigation of vehicle NVH error states associated with both hybridized and non-hybridized powertrains.

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization for steady-state engine calibration for single speed-load point. GA is a branch of meta-heuristic methods that has shown a great potential on solving difficult problems in automotive engineering. Bayesian optimization is an efficient global optimization method that solves problems with computationally expensive testing such as hyperparameter tuning in deep neural network (DNN), engine testing, etc.

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.

With increasingly stringent emissions regulations and concurrent requirements for enhanced engine thermal efficiency, a comprehensive characterization of the automotive gasoline fuel spray has become essential. The acquisition of accurate and repeatable spray data is even more critical when a combustion strategy such as gasoline direct injection is to be utilized. Without industry-wide standardization of testing procedures, large variablilities have been experienced in attempts to verify the claimed spray performance values for the Sauter mean diameter, Dv90, tip penetration and cone angle of many types of fuel sprays. A new SAE Recommended Practice document, J2715, has been developed by the SAE Gasoline Fuel Injection Standards Committee (GFISC) and is now available for the measurement and characterization of the fuel sprays from both gasoline direct injection and port fuel injection injectors.

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.

Engine dynamometer testing was performed comparing E10, E20, and E30 splash-blended fuels in a Ford 3.5L EcoBoost direct injection (DI) turbocharged engine. The engine was tested with compression ratios (CRs) of 10.0:1 (current production) and 11.9:1. In this engine, E20 (96 RON) fuel at 11.9:1 CR gave very similar knock performance to E10 (91 RON) fuel at 10:1 CR. Similarly, E30 (101 RON) fuel at 11.9:1 CR resulted in knock-limited performance equivalent to E20 at 10:1 CR, indicating that E30 could have been run at even higher CR with acceptable knock behavior. The data was used in a vehicle simulation of a 3.5L EcoBoost pickup truck, which showed that the E20 (96 RON) fuel at 11.9:1 CR offers 5% improvement in U.S. EPA Metro-Highway (M/H) and US06 Highway cycle tank-to-wheels CO₂ emissions over the E10 fuel, with comparable volumetric fuel economy (miles per gallon) and range before refueling.

The fuel consumption of a vehicle is shown to be linearly proportional to (1) total vehicle work required to drive the cycle due to mass and acceleration, tire friction, and aerodynamic drag and (2) the powertrain (PT) mechanical losses, which are approximately proportional to the engine displaced volume per unit distance travelled (displacement time gearing). The fuel usage increases linearly with work and displacement over a wide range of applications, and the rate of increase is inversely proportional to the marginal efficiency of the engine. The theoretical basis for these predictions is reviewed. Examples from current applications are discussed, where a single PT is used across several vehicles. A full vehicle cycle simulation model also predicts a linear relationship between fuel consumption, vehicle work, and displacement time gearing and agrees well with the application data.

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.

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.

This work discusses the development of SAE procedure J2747, “Hydraulic Pump Airborne Noise Bench Test”. This is a test procedure describing a standard method for measuring radiated sound power levels from hydraulic pumps of the type typically used in automotive power steering systems, though it can be extended for use with other types of pumps. This standard was developed by a committee of industry representatives from OEM's, suppliers and NVH testing firms familiar with NVH measurement requirements for automotive hydraulic pumps. Details of the test standard are discussed. The hardware configuration of the test bench and the configuration of the test article are described. Test conditions, data acquisition and post-processing specifics are also included. Contextual information regarding the reasoning and priorities applied by the development committee is provided to further explain the strengths, limitations and intended usage of the test procedure.

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.

This paper will describe the development of an in-vehicle data acquisition and analysis system. The problem facing the Vehicle Dynamics Test Section of Ford Motor Company was to replace an antiquated data recorder with a versatile in-vehicle data acquisition system capable of supporting vehicle dynamics testing and development. The following criteria for a system was developed: Quick and easy quick software and hardware setup Off-the-shelf hardware wherever possible User-friendly software Flexible Open-ended and modular design Rugged Cost effective Utilizing the above criteria a number of commercially available systems were evaluated and found to be lacking. Therefore it was decided that a system suitable for vehicle dynamics testing would have to be developed.

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.