Experimental investigation of B20 combustion and emissions under various intake conditions for low-temperature combustion

Recently, biodiesel has emerged as an alternative fuel for achieving low-temperature combustion (LTC). Several articles in the literature have showed that oxygenated biofuels, including biodiesel, can improve combustion stability under high exhaust gas recirculation (EGR) operation, which is considered to be necessary for the removal of nitric oxides (NOx). The objective of this study was to investigate the performance and emissions of 20% biodiesel blended diesel fuel (B20) at various intake pressures and oxygen concentration levels to characterize the fuel for LTC application. The experimental investigation of B20 was carried out using a single-cylinder engine (SCE) at 1400 rpm and 50% load condition. A set of critical flow orifices with synthetic EGR was employed to simulate various intake pressures and EGR levels. The behavior of the B20 was first characterized under various intake conditions. The results showed that with high oxygen intake, B20 exhibited combustion and emission levels that were very similar to conventional diesel. However, B20 reduced combustion deterioration while exhibiting lower carbon monoxide (CO) and hydrocarbon (HC) emissions than diesel under low oxygen intake conditions.     

Design of a compressor-power-based exhaust manifold pressure estimator for diesel engine air management

In accordance with the development of hardware configurations in diesel engines, research on model-based control for these systems has been conducted for years. To control the air management system of a diesel engine, the exhaust manifold pressure should be selected as one of the control targets due to its internal dynamic stability and its physical importance in model-based control. However, it is difficult to measure exhaust pressure using sensors due to gas flow oscillation in the exhaust manifold in a reciprocated diesel engine. Moreover, the sensor is too costly to be equipped on production engines. Hence, the estimation strategies for exhaust manifold pressure have been regarded as a primary issue in diesel engine air management control. This paper proposes a new estimation method for determining the exhaust manifold pressure based on compressor power dynamics. With its simple and robust structure, this estimation leads to improved control performance compared with that of general observers. To compensate for the compressor efficiency error that varies with turbine speed, some correction maps are adopted in the compressor power equation. To verify the control system performance with the new estimator, a HiLS (hardware in the loop simulation) of the NRTC mode is performed. Experimental verification is also conducted using a test bench for the C1-08 mode.     

Development of an autonomous braking system using the predicted stopping distance

An autonomous braking system is designed using the prediction of the stopping distance. The stopping distance needs to be determined by considering several factors such as the desired deceleration and the speed of the hydraulic brake actuator. In particular, the actuator speed is very critical because it affects the shape of the deceleration response and it determines the accuracy of the predicted stopping distance. The autonomous braking control algorithm is designed based on the predicted stopping distance. The proposed autonomous braking system has been validated in autonomous vehicle tests and demonstrates that the subject vehicle can avoid the collision effectively.     

Nonlinear sliding mode observer for exhaust manifold pressure estimation in a light-duty diesel engine

In this paper, a sliding mode observer is proposed to estimate exhaust pressure for a diesel engine equipped with variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) systems. Since the exhaust pressure directly affects generation of the VGT power and the EGR rate in the cylinder, the exhaust pressure information is important for precise control of the VGT and EGR systems. In order to estimate the exhaust pressure accurately, a dynamic model of intake and exhaust pressure was derived. Furthermore, the mass flow rate and temperature of the air system in the diesel engines were modeled by consideration of physical phenomena and the thermodynamic law. Based on the developed models, a nonlinear sliding mode observer was designed to estimate the exhaust pressure. Convergence of the proposed observer was verified by the Lyapunov stability criterion. The proposed observer was implemented on a real-time embedded system and validated with the engine experiments. The experimental results show that the observer estimates the exhaust pressure accurately in both steady and transient engine operating conditions. Moreover, as a case study, the estimation results of the proposed observer could be applied for detecting a fault of the EGR system. The fault of the EGR system was detected precisely using the estimation result and the limited sensor information in mass-produced engines.     

Abnormality detection via SVDD technique of motor-generator system in HEV

This paper introduces a method to detect abnormality of MGS (Motor-Generator System) in HEV (Hybrid Electric Vehicle) using its temperature. The MGS in HEV consists of two Motor-Generators (MG1, MG2), Compound Gear Unit, and etc. The MG1 is to act as a generator in conventional internal combustion engine. And the MG2 is an electric motor to rotate wheel of vehicle using saved electricity in battery or using produced electricity via the MG1. In case of overheating, the electric motors are easily damaged because resistance of wires in motor is abnormally changed. Therefore, detection of abnormally changed temperature in motors (MG1 and MG2) is essential. In this study, the temperature distribution of two Motor-Generators is observed simultaneously in 2-dimensional space. A boundary region of normal operation temperature of two motors is obtained via SVDD technique utilizing Gaussian kernel, one of the most widely being used Mercer kernels. Linear SVDD technique generates boundary of exact ball shape, however SVDD technique using Gaussian kernel can generate nonlinear boundary of distorted ball shape. Abnormality boundary comparison is made between the obtained boundary via SVDD technique and those obtained from conventional temperature range checking method. In order to compare the performance of proposed method, the actual vehicle operation data in excessive driving condition on mountain road is adopted. In verification, simulation shows that warning time due to proposed method is faster and more efficient than those due to conventional method. It is also shown that the reliability of the Motor-Generator System can be improved by using the proposed abnormality detection method.     

Transient modeling and validation of lithium ion battery pack with air cooled thermal management system for electric vehicles

A transient numerical model of a lithium ion battery (LiB) pack with air cooled thermal management system is developed and validated for electric vehicle applications. In the battery model, the open circuit voltage and the internal resistance map based on experiments are used. The Butler-Volmer equation is directly considered for activation voltage loss estimation. The heat generation of cells and the heat transfer from cells are also calculated to estimate temperature distribution. Validations are conducted by comparison between experimental results at the cell level and the pack level. After validations, the effects of module arrangement in a battery pack are studied with different ambient temperature conditions. The configuration that more LiB cells are placed near the air flow inlet is more effective to reduce the temperature deviation between modules.     

Inferring attribute non-attendance from stated choice data: implications for willingness to pay estimates and a warning for stated choice experiment design

There is a growing interest in traveller behaviour research to explore alternative information processing strategies (often referred to as heuristics or rules) adopted by individuals when assessing packages of attributes describing alternatives in a choice set, and making a choice. One popular attribute processing rule relates to attributes not being considered (i.e., being ignored), for all manner of reasons, referred to in the small but growing literature as attribute non-attendance or non-preservation. Researchers have used a mixture of methods to study the role of attribute non-attendance, including supplementary questions on whether each attribute is ignored or not, and methods in which the functional form of the utility expressions defining an alternative can recognise the possibility, up to a probability, of an attribute being ignored. Although supplementary questions are worthy of further consideration, despite the controversy as to the reliability of the response, recent interest has focused on ways to establish the incidence of attribute non-attendance without recourse to such evidence. In this paper we use an existing data set of choice amongst four attributes describing alternative car non-commuting trips, to illustrate the proposed method, and to compare values of travel time savings under each possible combination of non-attendance attributes relative to a model in which all attributes are assumed to be fully attended to. The paper reveals a major concern with the way that attribute levels and ranges are selected in the design of choice experiments, which can induce non-attendance situations where willingness to pay estimates cannot be obtained.     

Adaptive data dissemination for time-constrained messages in dynamic vehicular networks

With the advent of emerging wireless communication technologies, tremendous efforts have been put on promoting the safety and efficiency of transportation services by developing innovative applications. In particular, there has been significant interest in accessing information stored at RSUs (Roadside Units). The unique characteristics in vehicular networks, such as dynamic traffic factors including vehicle arrival rate, dwell time and data access patterns, bring us new challenges on data dissemination. This work dedicates to the investigation of timely and adaptive data dissemination in the dynamically changing traffic environment. Firstly, we derive an analytical model to explore and examine the effects of the dynamic traffic factors. In light of the theoretical results, an on-line scheduling algorithm is proposed for adaptive data dissemination. Finally, we evaluate performance of the new algorithm in a variety of circumstances. The simulation results demonstrate satisfactory performance of the proposed algorithm.     

The economics of airport noise: How to manage markets for noise licenses

This paper discusses various issues in the implementation of a local market for aircraft noise licenses to solve the noise externalities harming the residents located near airports. The design of such markets is affected by aircraft heterogeneity, wind contingencies, peak times, runways capacity constraints, hub strategies, and airport planning is discussed.     

Can scale and coefficient heterogeneity be separated in random coefficients models?

There is growing interest in the notion that a significant component of the heterogeneity retrieved in random coefficients models may actually relate to variations in absolute sensitivities, a phenomenon referred to as scale heterogeneity. As a result, a number of authors have tried to explicitly model such scale heterogeneity, which is shared across coefficients, and separate it from heterogeneity in individual coefficients. This direction of work has in part motivated the development of specialised modelling tools such as the G-MNL model. While not disagreeing with the notion that scale heterogeneity across respondents exists, this paper argues that attempts in the literature to disentangle scale heterogeneity from heterogeneity in individual coefficients in discrete choice models are misguided. In particular, we show how the various model specifications can in fact simply be seen as different parameterisations, and that any gains in fit obtained in random scale models are the result of using more flexible distributions, rather than an ability to capture scale heterogeneity. We illustrate our arguments through an empirical example and show how the conclusions from past work are based on misinterpretations of model results.