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Quantitative measurements of direct injection fuel spray density and mixing are difficult to achieve using optical diagnostics, due to the substantial scattering of light and high optical density of the droplet field. For multi-hole sprays, the problem is even more challenging, as it is difficult to isolate a single spray plume along a single line of sight. Time resolved x-ray radiography diagnostics developed at Argonne's Advanced Photon Source have been used for some time to study diesel fuel sprays, as x-rays have high penetrating power in sprays and scatter only weakly. Traditionally, radiography measurements have been conducted along any single line of sight, and have been applied to single-hole and group-hole nozzles with few plumes. In this new work, we extend the technique to multi-hole gasoline direct injection sprays.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies.

This paper analyzes the difference in impact response of the forehead of the Hybrid III and THOR-NT dummies in free motion headform tests when a dummy strikes the interior trim of a vehicle. Hybrid III dummy head is currently used in FMVSS201 tests. THOR-NT dummy head has been in development to replace Hybrid III head. The impact response of the forehead of both the Hybrid III dummy and THOR dummy was designed to the same human surrogate data. Therefore, when the forehead of either dummy is impacted with the same initial conditions, the acceleration response and consequently the head Injury criterion (HIC) should be similar. A number of manufacturing variables can affect the impacted interior trim panels. This work evaluates the effect of process variation on the response in the form of Head Injury Criterion (HIC).

Springback compensation studies on a few selected auto panels from the hot selling Chrysler 300C are presented with details. LS-DYNA® is used to predict the springback behavior and to perform the iterative compensation optimization. Details of simulation parameters using LS-DYNA® to improve the prediction accuracy are discussed. An iterative compensation algorithm is also discussed with details. Four compensation examples with simulation predictions and actual panel measurement results are included to demonstrate the effectiveness of LS-DYNA® predictions. An aluminum hood inner and a high strength steel roof bow are compensated, constructed and machined based on simulation predictions. The measurements on actual tryout panels are then compared with simulation predictions and good correlations were achieved. Iterative compensation studies are also done on the aluminum hood inner and the aluminum deck lid inner to demonstrate the effectiveness of LS-DYNA® compensation algorithm.

This paper presents an assessment of competing algorithms for normalizing volume levels between tracks and/or sources in an automotive infotainment system. Portable media players such as smartphones and iPod® devices are extremely popular for listening to music collections or streaming content from the Internet. The lack of normalization is a source of dissatisfaction if the user experiences significant changes in audio level between tracks. Several commercially available algorithms exist to solve this problem. This research includes a double-blind listening test comparing an audio sample processed with the different leveling algorithms to an unprocessed reference. The listener preference rating is recorded and results indicate which algorithm is preferred.

The first commercially available Plug-In Hybrid Electric Vehicle (PHEV), the General Motors (GM) Volt, was introduced into the market in December 2010. The Volt's powertrain architecture provides four modes of operation, including two that are unique and maximize the Volt's efficiency and performance. The electric transaxle has been specially designed to enable patented operating modes both to improve the electric driving range when operating as a battery electric vehicle and to reduce fuel consumption when extending the range by operating with an internal combustion engine (ICE). However, details on the vehicle control strategy are not widely available because the supervisory control algorithm is proprietary. Since it is not possible to analyze the control without vehicle test data obtained from a well-designed Design-of-Experiment (DoE), a highly instrumented GM Volt, including thermal sensors, was tested at Argonne National Laboratory's Advanced Powertrain Research Facility (APRF).

This paper describes a front road wheel steer-by-wire system with two actuator motors on the rack and pinion assembly to move the road wheels. Dual actuators are used to provide actuator redundancy and to enhance the fault tolerance capability. When one actuator faults or fails, the other actuator is designed to work independently and maintain full system performance. The paper emphasizes control method to implement the motion control for the front road wheel steer-by-wire system with two actuators on the common load. The proposed dual servo synchronization motion control implements the angle tracking for the road wheel reference input by controlling two actuators synchronously and cooperatively. It includes two servo feedback control loops to track the common reference input. The angular position error between two feedback loops is compensated using a synchronized compensator.

Electro-pneumatic valve actuators are used to eliminate the cam shaft of a traditional internal combustion engine. They are used to control the opening timing, duration, and lift of both intake and exhaust valves. A physics based nonlinear mathematical model called the level one model was built using Newton's law, mass conservation and thermodynamic principles. A control oriented model, the level two model, was created by partially linearizing the level one model for model reference parameter identification. This model reduces computational throughput and enables real-time implementation. A model reference adaptive control system was used to identify the nonlinear parameters that were needed for generating a feedforward control signal. The closed-loop valve lift tracking, valve opening and closing timing control strategies were proposed.

This paper presents the studies on how to efficiently and easily implement ECU application algorithms using the Signal Processing Engine (SPE) of the Copperhead microcontroller. With the introduced development and testing concepts and methods, users can easily establish their own PC based SPE emulation system. All application unit testing and verification work for the fixed point implementation using SPE functions can be easily conducted in PC without relying on a costly real time test bench and expensive third party dedicated software. With this simple development environment, the code can be run in both embedded controllers and PCs with exact bit to bit numerical behavior. The paper also demonstrates many other benefits such as code statistics information retrieval, floating simulation mode, automated code verification, online and offline code sharing.

The new Chrysler six-speed transaxle makes use of an underdrive assembly to extend a four-speed automatic transmission to six-speed. It is achieved by introducing double-swap shifts. During double-swap shift, learning the initial clutch torque capacity of the underdrive assembly's subsystem has a direct impact on the shift quality. A new method is proposed to compute and learn the initial clutch torque capacity of the releasing element. In this paper, we will outline a new mathematical method to compute and learn the accurate starting point of the clutch torque capacity for double swap shift control. The performance of the shift is demonstrated and the importance of the adaptation to shift quality is highlighted. An nth order lookup table is presented; this table contains n rows and m columns. Every row defines a relationship between the dependent variable such as actuator duty cycle and one independent variable such as transmission oil temperature, input torque or battery voltage.

A practical and low cost Blind Spot Monitoring system is proposed. By using a single camera, the range and azimuth position of a vehicle in a blind spot are measured. The algorithm is based on the proposed RWA (Range Window Algorithm). The camera is installed on the door mirror and monitoring the side and rear of the host vehicle. The algorithm processes the image and identifies range and azimuth angle of the vehicle in the adjacent lane. This algorithm is applied to real situations. The 388 images including several kinds of vehicles are analyzed. The detection rate is 86% and the range accuracy is 1.6[m]. The maximum detection range is about 30[m].

Argonne National Laboratory (Argonne) and Idaho National Laboratory (INL), working with the FreedomCAR and Fuels Partnership, lead activities in vehicle dynamometer and fleet testing as well as in modeling activities. By using Argonne’s Advanced Powertrain Research Facility (APRF), the General Motors (GM) Tahoe 2-mode was instrumented and tested in the 4-wheel-drive test facility. Measurements included both sensors and controller area network (CAN) messages. In this paper, we describe the vehicle instrumentation as well as the test results. On the basis of the analysis performed, we discuss the vehicle model developed in Argonne’s vehicle simulation tool, the Powertrain System Analysis Toolkit (PSAT), and its comparison with test data. Finally, on-road vehicle data, performed by INL, is discussed and compared with the dynamometer results.

Model-based control system design improves quality, shortens development time, lowers engineering cost, and reduces rework. Evaluating a control system's performance, functionality, and robustness in a simulation environment avoids the time and expense of developing hardware and software for each design iteration. Simulating the performance of a design can be straightforward (though sometimes tedious, depending on the complexity of the system being developed) with mathematical models for the hardware components of the system (plant models) and control algorithms for embedded controllers. This paper describes a software tool and a methodology that not only allows a complete system simulation to be performed early in the product design cycle, but also greatly facilitates the construction of the model by automatically connecting the components and subsystems that comprise it.

Internal combustion engines are designed to maximize power subject to meeting exhaust emission requirements and minimizing fuel consumption. Maximizing engine power and fuel economy is limited by engine knock for a given air-to-fuel charge. Therefore, the ability to detect engine knock and run the engine at its knock limit is a key for the best power and fuel economy. This paper shows inaudible knock detection ability using in-cylinder ionization signals over the entire engine speed and load map. This is especially important at high engine speed and high EGR rates. The knock detection ability is compared between three sensors: production knock (accelerometer) sensor, in-cylinder pressure and ionization sensors. The test data shows that the ionization signals can be used to detect inaudible engine knock while the conventional knock sensor cannot under some engine operational conditions.

Generalized predictive control (GPC) is a discrete time control strategy proposed by Clark et al [1]. The controller tries to predict the future output of a system or plant and then takes control action at present time based on future output error. Such a predictive control algorithm is presented in this paper for deceleration slip regulation in an automobile. Most of the existing literature on the anti-lock brake control systems lacks the effectiveness of the wheel lockup prevention when the automobile is in a skid condition (in a low friction coefficient surface with panic braking situation). Simulation results show that the predictive feature of the proposed controller provides an effective way to prevent wheel lock-up in a braking event.

This paper introduces the Driver Stability System (DSS) at Visteon. DSS is a new active comfort / safety system for automobiles which controls the seat bolsters independently in real time to enhance the lateral support of the occupants. Under turning maneuvers, DSS reacts to the vehicle dynamics to provide an increased contact area between the occupants and their seats, allowing optimal occupant location with respect to such variables as steering wheel angle, lateral acceleration, yaw rate, and vehicle velocity. The lateral force compensation is directly coupled to the dynamic movement of vehicle chassis and the change of road profile. The system consists of the seat bolster assembly including DC motors, wheel speed sensors, steering wheel sensor, lateral accelerometer, yaw rate sensor, and electronic control unit (ECU). This paper also discusses the control concept of DSS and its realistic controller structure.

The process for accurately estimating product reliability early in the development process can be a difficult and costly task. Traditional methods like Reliability Prediction Models and Life Testing Strategies yield beneficial results when relative information is known about the product that is to be analyzed. When there is minimal information known (prior failure rates…) such a new concept design these above reliability methods have limitations. For these cases a Virtual Testing Strategies have proven to yield valuable results. This paper will demonstrate a reliability analysis procedure for a new automotive concept design. This analysis procedure composes of a mathematical model, model validation, parameter diagram, design of experiment (DOE), response surface, and optimization.

Application of Ultra Thin Wall (UTW) ceramic substrates in the catalytic converter system requires the canner and component manufacturers to better understand the root cause and physics behind substrate breakage during the canning process. For this purpose, a ceramic substrate strength study for shoebox design has been conducted within Visteon Corporation. Computer Numerical Control (CNC) machined top and bottom fixtures, with identical inner surfaces as shoebox converter upper and lower shells, were used to crush mat wrapped substrates. Thin film pressure sensor technology enables the recording of substrate surface pressure during the compression process. Shell rib, washcoat, canning speed and cell density effects on substrate failure have been experimentally investigated. The development of a mathematical model helps to identify a better indicator to evaluate the substrate strength in the canning process and establish the strength for uncoated & coated substrates.

Generalized predictive control (GPC) is a discrete time control strategy proposed by Clark et al [1]. The controller tries to predict the future output of a system or plant and then takes control action at present time based on future output error. Such a predictive control algorithm is presented in this paper for yaw stability management of an automobile. Most of the existing literature on the yaw stability management systems lacks the insight into the yaw rate error growth when the automobile is in a understeer or oversteer condition on a low friction coefficient surface in a handling maneuver. Simulation results show that the predictive feature of the proposed controller provides an effective way to control the yaw stability of a vehicle.