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The Focus Electric is Ford�s first full-featured 5 passenger battery electric vehicle. The engineering team set our sights on achieving best-in-class function and efficiency and was successful with an EPA certified 1XX MPGe and range XXX then the facing competition allowing for a slightly lower capacity battery pack and larger vehicle without customer trade-off. We briefly overview the engineering method and technologies employed to deliver the results as well as sharing some of the functional challenges unique to this type of vehicle. Presenter Charles Gray, Ford Motor Co.

Building on two decades of expertise, a fourth generation fleet test protocol is presented for assessing the response of engine performance to gasoline additive treatment. In this case, the ability of additives to remove pre-existing deposit from the intake systems of port fuel injected vehicles has been examined. The protocol is capable of identifying real benefits under realistic market conditions, isolating fuel performance from other effects thereby allowing a direct comparison between different fuels. It is cost efficient and robust to unplanned incidents. The new protocol has been applied to the development of a candidate fuel additive package for the North American market. A vehicle fleet of 5 quadruplets (5 sets of 4 matched vehicles, each set of a different model) was tested twice, assessing the intake valve clean-up performance of 3 test fuels relative to a control fuel.

Conventional diesel fuel has been in the market for decades and used successfully to run diesel engines of all sizes in many applications. In order to reduce emissions and to foster energy source diversity, new fuels such as alternative and renewable, as well as new fuel formulations have entered the market. These include biodiesel, gas-to-liquid, and alternative formulations by states such as California. Performance variations in fuel economy, emissions, and compatibility for these fuels have been evaluated and debated. In some cases contradictory views have surfaced. “Sustainable”, “Renewable”, and “Clean” designations have been interchanged. Adding to the confusion, results from one fuel in one type of engine such as an older heavy-duty engine, is at times compared to that of another type such as a modern light-duty. This study was an attempt to compare the performance of several fuels in an identical environment, using the same engine, for direct comparison.

Work was performed to determine the feasibility of operating heavy-duty natural gas engines over a wide range of fuel compositions by evaluating engine performance and emission levels. Heavy-duty compressed natural gas engines from various engine manufacturers, spanning a range of model years and technologies, were evaluated using a diversity of fuel blends. Performance and regulated emission levels from these engines were evaluated using natural gas fuel blends with varying methane number (MN) and Wobbe Index in a dynamometer test cell. Eight natural gas blends were tested with each engine, and ranged from MN 75 to MN 100. Test engines included a 2007 model year Cummins ISL G, a 2006 model year Cummins C Gas Plus, a 2005 model year John Deere 6081H, a 1998 model year Cummins C Gas, and a 1999 model year Detroit Diesel Series 50G TK. All engines used lean-burn technology, except for the ISL G, which was a stoichiometric engine.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

The efforts of the ISO “Test Variability Task Force” have been aimed at improving the understanding and at reducing brake dynamometer test variability during performance testing. In addition, dynamometer test results have been compared and correlated to vehicle testing. Even though there is already a vast amount of anecdotal evidence confirming the fact that different procedures generate different friction coefficients on the same brake corner, the availability of supporting data to the industry has been elusive up to this point. To overcome this issue, this paper focuses on assessing friction levels, friction coefficient sensitivity, and repeatability under ECE, GB, ISO, JASO, and SAE laboratory friction evaluation tests.

In this work, a dynamically loaded hydrodynamic journal bearing test rig is developed and introduced. The rig is a novel design, using a hydraulic actuator with fast acting spool valves to apply load to a connecting rod. This force is transmitted through the connecting rod to the large end bearing which is mounted on a spinning shaft. The hydraulic actuator allows for fully variable control and can be used to apply either static load in compression or tension, or dynamic loading to simulate engine operation. A variable speed electric motor controls shaft speed and is synchronized to the hydraulic actuator to accurately simulate loading to represent all four engine strokes. A high precision torque meter enables direct measurements of friction torque, while shaft position is measured via a high precision encoder.

Medium- and Heavy Duty Truck fuel consumption and the resulting greenhouse gas (GHG) emissions are significant contributors to overall U.S. GHG emissions. Forecasts of medium- and heavy-duty vehicle activity and fuel use predict increased use of freight transport will result in greatly increased GHG emissions in the coming decades. As a result, the National Highway Traffic Administration (NHTSA) and the United States Environmental Protection Agency (EPA) finalized a regulation requiring reductions in medium and heavy truck fuel consumption and GHGs beginning in 2014. The agencies are now proposing new regulations that will extend into the next decade, requiring additional fuel consumption and GHG emissions reductions. To support the development of future regulations, a research project was sponsored by NHTSA to look at technologies that could be used for compliance with future regulations.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

In-cylinder ion sensing is a subject of interest due to its application in spark-ignited (SI) engines for feedback control and diagnostics including: combustion knock detection, rate and phasing of combustion, and mis-fire On Board Diagnostics (OBD). Further advancement and application is likely to continue as the result of the availability of ignition coils with integrated ion sensing circuitry making ion sensing more versatile and cost effective. In SI engines, combustion knock is controlled through closed loop feedback from sensor metrics to maintain knock near the borderline, below engine damage and NVH thresholds. Combustion knock is one of the critical applications for ion sensing in SI engines and improvement in knock detection offers the potential for increased thermal efficiency. This work analyzes and characterizes the ionization signal in reference to the cylinder pressure signal under knocking and non-knocking conditions.

This paper proposes an approach to use ABS wheel speed sensor signals together with other vehicle state information from a brake control module to detect an unbalanced tire or tires in real-time. The proposed approach consists of two-stage algorithms that mix a qualitative method using band-pass filtering with a quantitative parameter identification using conditional least squares. This two-stage approach can improve the robustness of tire imbalance or imbalances. The proposed approach is verified through vehicle testing and the test results show the effectiveness of the approach.

A three-step process was developed to estimate fracture criteria for a human body model. The process was illustrated via example wherein skull fracture criteria were estimated for the Ford Human Body Model (FHBM)~a finite element model of a mid-sized human male. The studied loading condition was anterior-to-posterior, blunt (circular/planar) cylinder impact to the frontal bone. In Step 1, a conditional reference risk curve was derived via statistical analysis of the tests involving fractures in a recently reported dataset (Cormier et al., 2011a). Therein, Cormier et al., authors reported results for anterior-to-posterior dynamic loading of the frontal bone of rigidly supported heads of male post mortem human subjects, and fracture forces were measured in 22 cases. In Step 2, the FHBM head was used to conduct some underlying model validations relative to the Cormier tests. The model-based Force-at-Peak Stress was found to approximate the test-based Fracture Force.

In an attempt to predict the responses of side crash pressure sensors, the Corpuscular Particle Method (CPM) was adopted and enhanced in this research. Acceleration-based crash sensors have traditionally been used extensively in automotive industry to determine the air bag firing time in the event of a vehicle accident. The prediction of crash pulses obtained from the acceleration-based crash sensors by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crash zones. As a result, the sensor algorithm developments for acceleration-based sensors are largely based on prototype testing. With the latest advancement in the crash sensor technology, side crash pressure sensors have emerged recently and are gradually replacing acceleration-based sensor for side impact applications.

An Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors in an attempt to assist pressure sensor algorithm development by using computer simulations. Acceleration-based crash sensors have traditionally been used to deploy restraint devises (e.g., airbags, air curtains, and seat belts) in vehicle crashes. The crash pulses recorded by acceleration-based crash sensors usually exhibit high frequency and noisy responses depending on the vehicle's structural design. As a result, it is very challenging to predict the responses of acceleration-based crash sensors by using computer simulations, especially those installed in crush zones. Therefore, the sensor algorithm developments for acceleration-based sensors are mostly based on physical testing.

This paper summarizes the Heavy-Duty In-Use Testing (HDUIT) measurement allowance program for Particulate Matter Portable Emissions Measurement Systems (PM-PEMS). The measurement allowance program was designed to determine the incremental error between PM measurements using the laboratory constant volume sampler (CVS) filter method and in-use testing with a PEMS. Two independent PM-PEMS that included the Sensors Portable Particulate Measuring Device (PPMD) and the Horiba Transient Particulate Matter (TRPM) were used in this program. An additional instrument that included the AVL Micro Soot Sensor (MSS) was used in conjunction with the Sensors PPMD to be considered a PM-PEMS. A series of steady state and transient tests were performed in a 40 CFR Part 1065 compliant engine dynamometer test cell using a 2007 on-highway heavy-duty diesel engine to quantify the accuracy and precision of the PEMS in comparison with the CVS filter-based method.

With a goal to help develop pressure sensor calibration and deployment algorithms using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this research to predict the responses of side crash pressure sensors for unitized vehicles. For occupant protection, acceleration-based crash sensors have been used in the automotive industry to deploy restraint devices when vehicle crashes occur. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. Instead of using acceleration (or deceleration) in the acceleration-based crash sensors, the pressure sensors utilize pressure change in a door structure to determine the deployment of restraint devices. The crash pulses recorded by the acceleration-based crash sensors usually exhibit high frequency and noisy responses.

Vehicle sound quality has become an important basic performance requirement. Traditionally, automobile noise studies were focused on quietness. It is now necessary for the automobile to be more than quiet. The sound must be pleasing. This paper describes a development process to improve both vehicle noise level and sound quality. Formal experimental design techniques were utilized to quantify various hardware effects. A-weighted sound pressure level, Speech Intelligibility, and Composite Rating of Preference were the three descriptors used to characterize the vehicle's sound quality. Engineering knowledge augmented with graphical and statistical techniques were utilized during data analysis. The individual component contributions to each of the sound quality descriptors were also quantified in this study.

During the 1980s, the U.S. Environmental Protection Agency (EPA) incorporated the R factor into fuel economy calculations in order to address concerns about the impacts of test fuel property variations on corporate average fuel economy (CAFE) compliance, which is determined using the Federal Test Procedure (FTP) and Highway Fuel Economy Test (HFET) cycles. The R factor is defined as the ratio of the percent change in fuel economy to the percent change in volumetric heating value for tests conducted using two differing fuels. At the time the R-factor was devised, tests using representative vehicles initially indicated that an appropriate value for the R factor was 0.6. Reassessing the R factor has recently come under renewed interest after EPA's March 2013 proposal to adjust the properties of certification gasoline to contain significant amounts of ethanol.

Sensory jury testing was utilized to characterize vibration levels perceived by the operator, with respect to levels measured using instrumentation, in order to develop a tool for the evaluation of vibration at the operator interfaces. Details of the jury testing and jury data processing method are highlighted as well as the refinement of vibration characterization for a specific application. The vibration at user interface locations of both snowmobiles and ATVs was measured along with subjective feedback from a panel of jurists. Statistical analysis was performed on the jury data to provide both a qualitative and quantitative number to represent the opinion of the jury. Correlations were developed between the measured levels of vibration and the opinions of the jury. Finally, a set of correlation functions suitable for design predictions was developed.