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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.

In this paper, thermal models are developed based on experimental test data, and the physics of thermal systems. If experimental data is available, the data can be fitted to mathematical models that represent the system response to changes in its input parameters. Therefore, empirical models which are based on test data are developed. The concept of time constant is presented and applied to development of transient models. Mathematical models for component temperature changes during transient vehicle driving conditions are also presented. Mathematical models for climate control system warm up and cool-down are also discussed. The results show the significance of adopting this concept in analysis of vehicle test data, and in development of analytical models. The developed models can be applied to simulate the system or component response to variety of changes in input parameters. As a result, significant testing and simulation time can be saved during the vehicle development process.

Fatigue analysis in the time domain using the rainflow cycle counting algorithm is considered the most accurate method for estimating damage. Dirlik's method has been found to be very accurate for damage estimation in the frequency domain. Previous studies have demonstrated the usefulness of Dirlik's method for ocean engineering and wind turbines but few have shown how well Dirlik performs in automotive applications. This study compares Dirlik's method with the rainflow cycle counting and with other frequency domain methods. The study analyzes measured data for an automotive component subjected to five test track load conditions. In addition, fourteen of Dirlik's original spectra and seven additional spectra which combine sine and random spectra are studied. It was found that Dirlik's method predicts more damage than the rainflow cycle counting method when applied to the original data used in creating the method.

In this paper, a transient thermal analysis model for Diesel fuel systems is presented. The purpose of this work is to determine the fuel temperature at various locations along the system, especially inside the tank and at the returned fuel inlet to the tank. Due to the fact that the fuel level is continuously changing during any driving condition, the fuel mass inside the tank is also continuously changing. Consequently, the fuel temperature will change even under steady driving or idle conditions, therefore, this problem should be analyzed using transient thermal analysis models. Effective thermal management requires controlling the surface temperature of the fuel tank, fuel lines and the fuel temperature at the fuel return line as well as inside the tank [1, 2]. Based on the thermal analysis results, it is possible to determine the major source of heat input at several locations of the fuel system.

Two on-line algorithms have been developed to acquire driveline component loads in terms of revolutions at torque and rainflow cycle counting matrix. These algorithms have been implemented in real-time on a standard engine controller unit and have been optimized for fast run-time and low memory requirements. The revolutions at torque algorithm is intended to count the number of driveshaft revolutions in each torque level for each gear and store the number of counts in the engine controller memory. The rainflow cycle counting algorithm is intended to count driveshaft torque cycles and to store the number of counts in a two dimensional “from-to” matrix format in the engine controller memory. The revolutions at torque histogram data and the rainflow cycle counting matrix are then downloaded from the vehicle using the data collection device. Download occurs when the vehicle is serviced at a dealership.

In today's light-weight vehicles, the strength of spot welds plays an important role in overall product integrity, reliability and customer satisfaction. Naturally, there is a need for a quick and reliable technique to inspect the quality of the welds. In the past, the primary quality control tests for detecting weld defects are the destructive chisel test and peel test [1]. The non-destructive evaluation (NDE) method currently used in industry is based on ultrasonic inspection [2, 3, 4]. The technique is not always successful in evaluating the nugget size, nor is it effective in detecting the so-called “cold” or “stick” welds. Therefore, it is necessary to develop a precise and reliable noncontact NDE method for spot welds. There have been numerous studies in predicting the weld nugget size by considering the spot-weld process [5, 6].

One method of reducing the number and/or severity of vehicle crashes is to warn the driver of a potential crash. The theory is that there will be driving conditions in which the drivers are unaware of a potential crash and a warning system will allow them to, in some manner, avoid the accident or reduce the severity. In an attempt to develop an analytical understanding of Forward Collision Warning systems (FCW) for frontal impacts a 2-d mathematical/kinematic model representing a set of pre-crash vehicle dynamic maneuvers has been built. Different driving scenarios are studied to explore the potential improvement of warning algorithms in terms of headway reduction and minimization of false alarm rates. The results agree with the field data. NHTSA's new NCAP active safety criteria are evaluated using the model. The result from the analysis indicates that the NHTSA criteria may drive higher false alarm rates. Opportunities of minimizing false positive rates are discussed.

Driven by diesel engine emission regulation, more emission aftertretment products have been under development by Tenneco to address the Particular Matter (PM) and NOx reduction needs. The T.R.U.E. (Thermal Regeneration Unit for Exhaust) Clean active thermal management system is one of the examples to reduce PM. The system is designed to increase exhaust temperatures for DPF (Diesel Particulate Filter) regeneration. This product is exposed to high temperature and high oxidation. Therefore, thermal fatigue, creep, oxidation and the interaction become critical mechanism to be considered for its durability. One of the key challenges to validate this product is to find a way of accelerated testing for thermal, creep, and oxidation as well as for vibration. In this paper, accelerated durability test strategy for high temperature device like T.R.U.E Clean is addressed.

With the increased market share of electric vehicles, the demand for energy-efficient routing algorithms specifically optimized for electric vehicles has increased. Traditional routing algorithms are focused on optimizing the shortest distance or the shortest time in finding a path from point A to point B. These traditional methods have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power limits, battery capacity limits, and vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present an ant colony based, energy-efficient routing algorithm that is optimized and designed for electric vehicles. Simulation results show improvements in the energy consumption of electric vehicles when applied to a start-to-destination routing problem.

This paper presents the design of a hybrid powertrain damping control algorithm using the sliding mode control (SMC) scheme. Motor control-based active damping control strategy is used to ensure smooth drive line operation and provide the driver with seamless driving experience. In the case of active damping control, motor and engine speeds are measured to monitor the driveline state, and corrective motor torques are generated to dampen out drive line vibrations. Drive lines are prone to internal vibration (engine, clutches and motors) as well as external disturbances caused by road inputs. As such, fast-response actuator-based damping control systems are desirable in a hybrid powertrain application, where a torque converter is generally not used. The most significant aspect of an active damping control algorithm is the error calculation, based on proper states information, and torque determination based on the adaptive control gain applied to the nonlinear system.

Growing concerns about the environment, energy dependency, and unstable fuel prices have increased the market share of electric vehicles. This has led to an increased demand for energy efficient routing algorithms that are optimized for electric vehicles. Traditional routing algorithms are focused on finding the shortest distance or the least time route between two points. These approaches have been working well for fossil fueled vehicles. Electric vehicles, on the other hand, require different route optimization techniques. Negative edge costs, battery power and capacity limits, as well as vehicle parameters that are only available at query time, make the task of electric vehicle routing a challenging problem. In this paper, we present a simulated solution to the energy efficient routing for electric vehicles using Particle Swarm Optimization. Simulation results show improvements in the energy consumption of the electric vehicle when applied to a start-to-destination routing problem.