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An automated process was developed for the calibration of numerical aftertreatment models. The chemical kinetic mechanism examined in this case was part of a simplified SCR model. The process adopted for calibrating the SCR model was based on a micro-population multi-objective genetic algorithm. The algorithm developed was used to calibrate the SCR model using data derived from another, more detailed model to ensure that the evaluation focused only on the effectiveness of the calibration process and was not affected by issues of experimental inaccuracies or details of the model chemistry involved.

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.

Engine operating strategies typically geared towards higher fuel economy and lower NOx widely affect exhaust composition and temperature. These exhaust variables critically drive the performance of After Treatment (AT) components, and hence should guide their screening and selection. Towards this end, the effect of H2 level in diesel exhaust on the performance of a Diesel Oxidation Catalyst (DOC) was studied using flow reactor experiments, vehicle emission measurements and mathematical models. Vehicle chassis dynamometer data showed that exhaust from light-duty and heavy-duty diesel trucks contained very little to almost no H2 (FTP average CO/H2 ∼ 40 to 70) as compared to that of a gasoline car exhaust (FTP average CO/H2 ∼ 3). Two identical flow reactor experiments, one with H2 (at CO/H2 ∼ 3) and another with no H2 in the feed were designed to screen DOCs under simulated feed gas conditions that mimicked these two extremes in the exhaust H2 levels.

A thorough understanding of vehicle exhaust aftertreatment system performance requires time-resolved emissions measurements that accurately follow driving transients, and that are correctly time-aligned with exhaust temperature and flow measurements. The transient response of conventional gas analyzers is characterized by both a time delay and an attenuation of high-frequency signal components. The distortion that this imposes on transient emissions measurements causes significant errors in instantaneous calculations of aftertreatment system efficiency, and thus in modal mass analysis. This creates difficulties in mathematical modeling of emissions system performance and in optimization of powertrain control strategies, leading to suboptimal aftertreatment system designs. A mathematical method is presented which improves the response time of emissions measurements. This begins with development of a model of gas transport and mixing within the sampling and measurement system.

Hybrid drivetrain systems are becoming increasingly prevalent in Automotive and Commercial Vehicle applications and have also been introduced for the 2009 Formula1 motorsport season. The F1 development has the clear intent of directing technical development in motorsport to impact the key issue of fuel efficiency in mainstream vehicles. In order to promote all technical developments, the type of system (electrical, mechanical, hydraulic, etc) for the F1 application has not been specified. A significant outcome of this action is renewed interest and development of mechanical hybrid systems comprising a high speed composite flywheel and a full-toroidal traction drive Continuously Variable Transmission (CVT). A flywheel based mechanical hybrid has few system components, low system costs, low weight and dispenses with the energy state changes of electrical systems producing a highly efficient and power dense hybrid system.

Piezoelectric self-sensing allows to measure frequency response functions of dynamical systems with one single piezoelectric element. This piezoceramics is used as actuator and sensor simultaneously. In this study, a model-based piezoelectric self-sensing technique is presented to obtain potential squealing frequencies of an automotive disc brake. The frequency-response function of the brake system is obtained during operation by measuring the current flowing through the piezoelectric element while the piezoelectric element is driven by a harmonic voltage signal with constant amplitude. The current flow is composed of the part which is required to drive the piezoelectric element as an actuator and a second part which is the sensor signal that is proportional to the vibration amplitude of the attached mechanical system. Typically the first part is dominant and the influence of the mechanical system is marginal.

This paper describes the joint development by Tenneco and Pi Shurlok of a complete diesel engine aftertreatment system for controlling particulate matter emissions. The system consists of a DOC, DPF, sensors, controller and an exhaust fuel injection system to allow active DPF regeneration. The mechanical components were designed for flow uniformity, low backpressure and component durability. The overall package is intended as a complete PM control system solution for OEMs, which does not require any significant additions to the OEM's engine control strategies and minimizes integration complexity. Thus, to make it easier to adapt to different engine platforms, ranging from small off-road vehicle engines to large locomotive engines, model-based control algorithms were developed in preference to map-based controls.

Complexity and nonlinearity of engines makes precise first principle engine models often difficult to obtain, as for instance for emissions. System identification is a well known possible alternative, successfully used in several automotive applications. In most cases system identification is concerned with the estimation of the unknown parameters of a known set of equations. Unfortunately, for many engine subsystems, there is no sufficiently precise or real time suitable model. This paper presents a sequential algorithm which allows to derive real time suitable models on line by a combination of model structure hypothesis of increasing complexity and an associated optimal input design and selection process. This paper introduces the method and shows its use both for a rather simple and a very difficult engine identification task, a dynamical model of the airpath of a Diesel engine and a dynamical model of nitrogen oxides and particulate matter.

Compliance with tighter emission regulations has increased the proportion of parasitic weight in commercial vehicles. In turn, the amount of payload must be reduced to comply with transportation weight requirements. A re-design of commercial vehicle components is necessary to decrease the vehicle weight and improve payload capacity. Side rails have traditionally been manufactured from high strength steels, but significant weight reductions can be achieved by substituting steel side rails with 6013 high strength aluminum alloy side rails. Material and stress analyses are presented in this paper in order to understand the effect of manufacturing process on the material's mechanical behavior. Metallographic and tensile test experiments for the 6013-T4 alloy were performed in preparation for residual stress measurements of a punching operation. Punched holes are critical to the function of the side rail and can lead to high stress levels and cracking.

This paper develops a novel integrated navigational system for autonomous vehicle motion control. Vehicle control is defined in terms of the required vehicle steering angle, rate of steering change and speed. This paper proposes predictive control in the image plane. The proposed predictive control enables the navigation on the desired path, reduces the control complexity and increases the application space for multiple types of vehicles. The paper investigates vehicle control stability; especially in scenarios containing varying curvature turns and variable vehicle speeds. The primary emphasis of this paper is on vehicle control rather than scene analysis. To demonstrate the proposed vehicle control, a computer vision based multi-lane detection algorithm is introduced. The control strategy is applied such that the vehicle maintains position within the lane boundaries. Stability of the control algorithm is tested and demonstrated in multiple scenarios using 3-D simulation results.

In 2006, there were approximately 23,500 rear-end crashes involving heavy trucks (i.e., gross vehicle weight greater than 4,536 kg). The Enhanced Rear Signaling (ERS) for Heavy Trucks project was developed by the Federal Motor Carrier Safety Administration (FMCSA) to investigate methods to reduce or mitigate those crashes where a heavy truck has been struck from behind by another vehicle. Visual warnings have been shown to be effective, assuming the following driver is looking directly at the warning display or has his/her eyes drawn to it. A visual warning can be placed where it is needed and it can be designed so that its meaning is nearly unambiguous. FMCSA contracted with the Virginia Tech Transportation Institute (VTTI) to investigate potential benefit of additional rear warning-light configurations as rear-end crash countermeasures for heavy trucks.

Virtual testing is a method that simulates lab testing using multi-body dynamic analysis software. The main advantages of this approach include that the design can be evaluated before a prototype is available and virtual testing results can be easily validated by subsequent physical testing. The disadvantage is that accurate specimen models are sometimes hard to obtain since nonlinear components such as tires, bushings, dampers, and engine mounts are hard to model. Therefore, virtual testing accuracy varies significantly. The typical virtual rigs include tire and spindle coupled test rigs for full vehicle tests and multi-axis shaker tables for component tests. Hybrid simulation combines physical and virtual components, inputs and constraints to create a composite simulation system. Hybrid simulation enables the hard to model components to be tested in the lab.

Intelligent tires are envisioned to be an important part of the future vehicle control systems and the three dimensional wireless MEMS accelerometers embedded inside the tire stand out as a promising candidate for the development of intelligent tires. The first part of the paper focuses on accelerometer based tire sensors for the estimation of slip angle and tire/road friction coefficient. We use a simple tire finite element model to generate lateral, tangential and radial tire accelerations for a fixed load and slip angle. The profiles are validated by using experimental data. The simulated acceleration profiles are used for the estimation of slip angle and tire/road friction coefficient. We present the estimation algorithms, promising simulative results and output sensitivities studies focused on the effects of changes in normal load, tire pressure and vehicle velocity.

The use of Variable Valve Actuation (VVA) systems offers many advantages in terms of increased engine power, reduced fuel consumption and pollutant emissions, accomplishing a significant improvement of the global efficiency of the engine. In the last decade different devices have been proposed to implement advanced and innovative VVA managements on four-stroke engines. ElectroMechanical Valve Actuator (EMVA) formed by two opposite magnets and two balanced springs seem to be a very promising solution among several camless actuation systems. This type of valve actuator is characterized by highly nonlinear and strongly coupled dynamics which makes very difficult to govern engine valve motion during the last part of the closing and opening strokes, where an unstable behavior is exhibited. In this regard the control problem of the EMVA is tackled in this paper.

This paper presents an integrated design method for active trailer steering (ATS) systems of articulated heavy vehicles (AHVs). Of all contradictory design goals of AHVs, two of them, i.e. path-following at low speeds and lateral stability at high speeds, may be the most fundamental and important, which have been bothering vehicle designers and researchers. To tackle this problem, a new design synthesis approach is proposed: with design optimization techniques, the active design variables of ATS systems and passive design variables of trailers can be optimized simultaneously; the ATS controller derived from this approach has two operational modes, one for improving lateral stability at high speeds and the other for enhancing path-following at low speeds. To demonstrate the effectiveness of the proposed approach, it is applied to the design of an ATS system for an AHV with a tractor and a full trailer.

One of the most important challenges for electronic stability control (ESC) systems is the identification and monitoring of tire rolling environment, especially actual tire-road friction parameters. The presented research considers an advanced variant of the ESC system deducing the mentioned factors based on intelligent methods as fuzzy sets. The paper includes: Overview of key issues in prototyping the algorithms of Electronic Stability Control. Case study for vehicle model. Procedures for monitoring of tire rolling environment: theoretical backgrounds, computing methods, fuzzy input and output variables, fuzzy inference systems, interface with ESC algorithm. Case study for ESC control algorithm. Examples of simulation using Hardware-in-the-Loop procedures. The proposed approach can be widely used for the next-generation of ESC devices having the close integration with Intelligent Transport Systems.

Torsional stiffness properties were developed for both a 53-foot box trailer and a 28-foot flatbed control trailer based on experimental measurements. In order to study the effect of torsional stiffness on the dynamics of a heavy truck vehicle dynamics computer model, static maneuvers were conducted comparing different torsional stiffness values to the original rigid vehicle model. Stiffness properties were first developed for a truck tractor model. It was found that the incorporation of a torsional stiffness model had only a minor effect on the overall tractor response for steady-state maneuvers up to 0.4 g lateral acceleration. The effect of torsional stiffness was also studied for the trailer portion of the existing model.

The objective of the paper is to present the results of verification of ABS models applied in PC-Crash and HVE (Human-Vehicle-Environment) computer programs in various road conditions. The aim was reached by comparison of the road tests results obtained and calculations performed using the programs for the same initial values of the measured variables. First static tests were carried out for parameterization of the mathematical model of the object of the research - a passenger car equipped with ABS. Road tests were performed including extreme braking on a μ-split with and without corrective steer, and extreme braking during lane-change maneuver.

As the number of electrical devices in modern vehicles increases, the battery becomes more critical component for the operation of vehicles. To ensure the startability of the vehicle, battery conditions such as state of charge and state of health should be properly monitored and maintained. To reduce walk-home incidents due to no-start situation, appropriate warning should be issued to the driver to advise necessary actions such as replacing or re-charging the battery. For the last couple of years, General Motors has studied and developed several battery health monitoring methods based on different battery health signatures. Yet, it is found that relying on a single method may lead to false alarm or misdetection due to lack of information or uncertainty. This paper develops the algorithm for more robust and reliable battery health monitoring and prognosis, by applying Evidence Theory to fuse different battery health signatures.

A truck-trailer combination is modeled using ADAMS/Car from MSC Software for handling and ride comfort performance simulations. The handling events include a double lane change and lateral roll stability. The ride comfort performance events include several sized half-rounds and various RMS courses. The variables for handling performance evaluation include lateral acceleration, roll angles and tire patch normal loads. The variables for ride performance evaluation are absorbed power and peak acceleration. This study considers the trailer spring stiffness, anti-roll bar and jounce bumper gap as the design variables. Through DOE simulations, we derived the response surface models of various performance variables so that we could consider the performance sensitivities to the design variables.