Engine clutch torque estimation for parallel-type hybrid electric vehicles

This paper mainly focuses on the accurate estimation of the torque transferred through the engine clutch installed between the engine and the drive motor in parallel-type hybrid electric vehicles. The estimation of the engine clutch torque primarily relies on the forward-direction observer which uses the nominal engine net torque information. To overcome the limitation of using the nominal engine torque information that it may not be accurate during the transient states or due to the influence of external disturbance such as the road condition and wind, the forward-direction observer is supplemented by the use of reverse-direction observer which uses the driveline model and wheel speed measurements. In addition, the drive motor torque information is used to calibrate the nominal engine torque during the idle charging state, so that the driveline characteristic unique to parallel-type hybrid electric vehicle can be utilized to increase the estimation accuracy. Finally, the estimation performance of the designed observer is tested via simulation and experiments based on a real vehicle.     

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).     

Economic conditions for minibus usage in a multimodal feeder network

The introduction of minibuses into the transit fleets of some cities makes it more important than ever to investigate the economic and operating conditions of these vehicles for various transit and paratransit services. A high performance transit system is of great interest, owing to its desirable characteristics. However, it is not feasible to establish and use such a system all over a city. It is therefore necessary to support a high performance transit system with other modes to extend its area of operation (owing to higher demand) and also to connect those areas that are not covered by the high performance system to it. This paper considers the use of minibuses in the design of metro line feeders and to characterize the economic domain in which it is efficient to use them in a feeder network. Based on results from ant colony optimization, the performance measures of minibuses are compared with those of conventional buses to help make decision makers aware of the use and role of these vehicles. The study contradicts the common belief that, since minibuses require similar driver costs to those of conventional buses, it is not economical to use them in a transit fleet; in fact, it is shown that minibuses can displace conventional buses in some situations in a feeder network.     

Geometric stress distribution along the weld toe of the outer brace in two-planar tubular DKT-joints: Parametric study and deriving the SCF design equations

Regarding the research efforts expended so far on the calculation of stress concentration factors (SCFs) in tubular joints, two major shortcomings can be noted: (a) significant effort has been devoted to the study of SCFs in various uni-planar connections. Nevertheless, for multi-planar joints which cover the majority of practical applications, very few investigations have been reported due to the complexity and high cost involved; (b) majority of these research works focused on the study of SCFs at certain positions such as the saddle, crown toe, and crown heel, and they have ignored the hot-spot stress (HSS) at other positions along the weld toe. In the present paper, effects of dimensionless geometrical parameters on the SCF distribution along the weld toe of main (outer) braces in the axially loaded right-angle two-planar tubular DKT-joints are investigated. In order to study the multi-planar effect, SCF distribution in two-planar joints is compared with the distribution in a uni-planar joint having the same geometrical properties. A complete set of SCF database is constructed based on the two-planar DKT-joint Finite element models which are verified against experimental results and the predictions of Lloyd’s Register (LR) equations. The FE models cover a wide range of geometrical parameters. Six new SCF parametric formulae are developed through nonlinear regression analyses for the accurate and reliable fatigue design of two-planar DKT-joints under axial loads. An assessment study of these equations is conducted against the experimental data, the original FEM database and the acceptance criteria recommended by the UK Department of Energy.     

Effect of UWS injection at low exhaust gas temperature on NO<Subscript>x</Subscript> removal efficiency of diesel engine

Urea-SCR systems have been widely used in diesel vehicles according to the strengthened NO_x (Nitrogen Oxides) emission standard. The NO_x removal efficiencies of the latest well optimized urea-SCR system are above 90 % at moderate exhaust gas temperature of 250 ~ 450 °C. However, a large amount of NO_x is emitted from diesel vehicles at cold start or urban driving conditions, when the exhaust gas temperature is not high enough for SCR catalyst activation. Although many researchs have been stuied to improve NO_x conversion efficiency at these low temperature conditions, it is still one of important technical issues. In this study, the effect of UWS injection at low exhaust gas temperature conditions is studied. This study uses a 3.4 L diesel engine equipped with a commertial urea SCR system. As a result, it is found that about 5 % of NO_x removal efficiency is improved in the NRTC test when UWS injection starts at the SCR inlet temperature of 150 °C compared to 200 °C. It is also found that urea deposits can be formed on the wall of exhaust pipe, when the local wall temperature is lower than temperature of urea decomposition.     

Marine Protected Area Management Effectiveness: Progress and Lessons in the Philippines

Quantifying progress in management of marine protected areas (MPAs) is crucial to marine conservation and fisheries management in the Philippines. This study compiles data on the status, occurrence, and management gaps of MPAs through coordination with multiple organizations supporting and guiding MPAs in the Philippines. MPA management effectiveness was measured using a MPA Rating System. Since 2002 the modal MPA rating levels increased from level 1 (initiated) to level 4 (sustained) in 2008/9. This upward trend is attributed to factors that promoted both the establishment and improved management of MPAs. Analysis indicated that: (1) most MPAs struggle with budgetary constraints or lack of sustainable financing and (2) overall the MPAs are being maintained and progressing with notable improvement in management despite a range of difficulties encountered during the implementation process. For MPAs in the Visayan Region for which biophysical data were available, the MPA Rating System was used to assess the effectiveness of local government capacity building on MPA coral reef health. Our results suggest that MPAs with higher ratings are likely to have better reef health conditions.     

Long-term behavior of particulate matters at urban roadside and background locations in Seoul,Korea

The concentrations of particulate matter, PM_2.5, PM₁₀, and TSP at an urban roadside and an urban background station are analyzed. Data collected over a 10 year period are analyzed. The concentrations of the particulates measured at the urban site are systematically larger than at the background station. The mean PM values at the former also exhibit a slight fall over the decade unlike those at the background station. Overall, the particulate matters at both locations are in an intermediate range of global level, e.g., approximately two times lower than those in other Asian regions but higher than in Europe.     

Resistance analysis of a semi-SWATH design concept in shallow water

Resistance analysis is an important analytical method used to evaluate the hydrodynamic performance of High Speed Craft (HSC). Analysis of multihull resistance in shallow water is essential to the performance evaluation of any type of HSC. Ships operating in shallow water experience increases in resistance because of changes in pressure distribution and wave pattern. In this paper, the shallow water performance of an HSC design concept, the semi-Small Waterplane Area Twin Hull (semi-SWATH) form, is studied. The hull is installed with fin stabilizers to reduce dynamic motion effects, and the resistance components of the hull, hull trim condition, and maximum wave amplitude around the hull are determined via calm water resistance tests in shallow water. These criteria are important in analyzing semi-SWATH resistance in shallow water and its relation to flow around hull. The fore fin angle is fixed to zero degrees, while the aft fin angle is varied to 0°, 5°, 10°, and 15°. For each configuration, investigations are conducted with depth Froude numbers (Fr _H ) ranging from 0.65 to 1.2, and the resistance tests are performed in shallow water at the towing tank of UTM. Analysis results indicate that the resistance, wave pattern, and trim of the semi-SWATH hull form are affected by the fin angle. The resistance is amplified whereas the trim and sinkage are reduced as the fin angle increases. Increases in fin angle contribute to seakeeping and stability but affect the hull resistance of HSCs.     

Direct generation of level of service maps from images using convolutional and long short-term memory networks

Congestion in transport stations could result in stampede development and deadly crush situations. Closed circuit television (CCTV) cameras enable station managers to monitor the crowd and reduce overcrowding risks. However, identifying congestion conditions is a very laborious task for a human operator who has to monitor multiple locations at the same time. This paper presents a new approach to automated image-based identification of congestion as measured by level of service (LOS), which is the most widely accepted standard for measuring congestion. Existing methods for measuring LOS based on crowd density estimation from images have the disadvantages that, crowd density cannot be estimated accurately. In addition, the calculation of flow parameters involves a complex process, and consequently these parameters are not indicative of congestion in real-time. This paper proposes a novel method based on machine learning to directly classify LOS without calculating flow parameters. In the proposed method, visual features extracted by a deep convolutional neural network are classified using a support vector machine classifier and the classification results are further refined by using a long short-term memory network. A second contribution of this research is to develop a web-based LOS map visualization platform to monitor pedestrian distribution and variation of distribution in real-time. Experimental evaluation at Flinders Street Station in Melbourne shows that this method can achieve an accuracy of 81.9% and efficiency of 0.40?seconds per frame in LOS classification using CCTV images.