Impact of hilly road information on fuel economy of FCHEV based on parameterization of hilly roads

Under real-life driving conditions, hilly roads are prevalent. Hilly road profile substantially influences fuel economy (FE) due to large impacts (increase or decrease) on power demand profile. Thus, the utilization of future altitude profile information has large potential to improve FE. In this paper, for optimal energy management of fuel cell hybrid electric vehicles (FCHEV), we investigate how much FE could potentially be improved when future altitude profile information is available. In particular, the simulation results are analyzed to justify the reason for this potential improvement and to identify which characteristics of hilly roads leads to large FE improvements. First of all, four statistical parameters are defined to characterize hilly roads: mean value, standard deviation (STD), distance interval (DI), and total distance. Then, several types of virtual hilly roads are generated based on various parameter combinations. In order to evaluate the potential FE improvement two energy management strategies (EMSs) are utilized: the first is Dynamic Programming, which evaluates the globally optimal FE when future hilly road information is available; the other is the Equivalent Consumption Minimization Strategy (ECMS) with adaptive equivalent factor for charge-sustenance, which represents the baseline EMS when future hilly road information is not available. The results show that downhill roads have much larger potential than uphill roads do for FE improvements when the future altitude profile is properly used for EMS. Furthermore, if the battery capacity is not large enough to handle the difference in potential energy, future hilly road information is more important to prevent violations of the maximum state-of-charge bound.     

Review on characterization of nano-particle emissions and PM morphology from internal combustion engines: Part 1

This paper is review of the characterization of exhaust particles from state-of-the-art internal combustion engines. We primarily focus on identifying the physical and chemical properties of nano-particles, i.e., the concentration, size distribution, and particulate matter (PM) morphology. Stringent emissions regulations of the Euro 6 and the LEV III require a substantial reduction in the PM emissions from vehicles, and improvements in human health effects. Advances in powertrains with sophisticated engine control strategies and engine after-treatment technologies have significantly improved PM emission levels, motivating the development of new particle measurement instruments and chemical analysis procedures. In this paper, recent research trends are reviewed for physical and chemical PM characterization methods for gasoline and diesel fueled engines under various vehicle certification cycles and real-world driving conditions. The effects of engine technologies, fuels, and engine lubricant oils on exhaust PM morphology and compositions are also discussed.     

Fatigue strength assessment including welding residual stress of spot welded joints subjected to cross-tension loads

This paper presents a method to assess of fatigue strength for resistance spot welded joints, which incorporates welding residual stress effects. To achieve this, first, a non-linear finite element analysis (FEA) was performed to simulate the spot-welding process. To validate the FEA results, the numerically calculated welding residual stresses of spot welds were then compared with experimental results measured by X-ray diffraction method. The residual stress distributions showed good agreement between calculations and experiments. To evaluate the effects of welding residual stress on the fatigue design criterion of resistance spot welded joints subjected to cross-tension load, the stress amplitude (σ_a-res) taking into account welding residual stress at a spot weld was proposed based on a modified Goodman equation incorporating the residual stress effect. Using the stress amplitude σ_a-res at the nugget edge of a spot weld, the ΔP ? N_f relations obtained as the fatigue test results for spot welded joints were systematically rearranged to the σ_a-res ? N_f relation. It was found that the proposed stress amplitude (σ_a-res) provides more reasonable and accurate fatigue design criterion of spot welded joints subjected to cross-tension load.     

Evaluation and visualization of stratified ultra-lean combustion characteristics in a spray-guided type gasoline direct-injection engine

To comply with reinforced emission regulations for harmful exhaust gases, including carbon dioxide (CO₂) emitted as a greenhouse gas, improved technologies for reducing CO₂ and fuel consumption are being developed. Stable lean combustion, which has the advantage of improved fuel economy and reduced emission levels, can be achieved using a sprayguided-type direct-injection (DI) combustion system. The system comprises a centrally mounted injector and closely positioned spark plugs, which ensure the combustion reliability of a stratified mixture under ultra-lean conditions. The aim of this study is to investigate the combustion and emission characteristics of a lean-burn gasoline DI engine. At an excess air ratio of 4.0, approximately 23% improvement in fuel economy was achieved through optimal event timing, which was delayed for injection and advanced for ignition, compared to that under stoichiometric conditions, while NO_x and HC emissions increased. The combustion characteristics of a stratified mixture in a spray-guided-type DI system were similar to those in DI diesel engines, resulting in smoke generation and difficulty in three-way catalystutilization. Although a different operating strategy might decrease fuel consumption, it will not be helpful in reducing NO_x and smoke emissions; therefore, alternatives should be pursued to achieve compliance with emission regulations.     

Simulation of flow in diesel particulate filter system using metal fiber filter media

Metal fiber is introduced as a new filter media in wall-flow Diesel Particulate Filter (DPF) system. This technology has high temperature durability which is required for filter regeneration, and can maintain the mechanical strength even in the extreme exhaust-related vibrations of vehicles. However, the regeneration near the wall (outer layer) is more difficult because of the heat loss and reduced gas flow near the wall. In this study, a flow is simulated to determine the flow control method for the more uniform flow in all filter layer. By using Star CCM+ commercial software, we obtain local velocity, streamline, and pressure distributions in the filter, which are typically difficult to obtain from measurements. The major control factors are the filter porosity, size and location of the distribution plate, and the number of blades of the swirler. By placing the distribution plate in front of the filter, the flow velocity near the wall was increased. The optimum location and size of the flat plate were chosen. By attaching the blade on the plate the flow velocity near the wall was increased more. Therefore, the regeneration efficiency is increased by using the swirler-type metal fiber DPF system.     

An experimental study of the effect of low-noise wheels in reducing noise and vibration

Wheel/rail interaction is a major source of railway noise. A low-noise wheel structure is developed and its effect on noise reduction is investigated. This low-noise wheel employs a rubber material inserted into the steel rim or mounted on the wheel surface. The low-noise wheel has low stiffness and a high-damping ratio compared to a solid wheel. Measurement shows that it reduces rolling and squealing. It turns out that a subway line with the proposed wheel could reduce its interior noise level by 4–5 dB(A) and vehicle vibration level by 7–8 dB. While the proposed structure seems promising in noise reduction for railway vehicles, the endurance and cost effectiveness of the low-noise wheel are yet to be verified.     

Numerical simulation of three-dimensional breaking waves

Three-dimensional (3D) wave breaking around bodies of complex geometry has been numerically investigated by use of two types of Navier-Stokes solvers, namely the finite-difference and the finite-volume methods employing rectangular and curvilinear coordinate systems, respectively. Both methods employ the density-function technique to capture the free surface location and can cope with complicated free surface configurations such as breaking waves. The accuracy of the density-function method is examined through the comparison with experimental results, and it is confirmed to be satisfactory when the grid spacing and the time increment are sufficiently small. New computational methods are applied to several problems including 3D breaking waves around ships and wave diffraction around offshore structures. The computed results show good agreement with experimental results indicating that wave breaking phenomena are successfully simulated. The qualitative accuracy, however, could be improved by including the dissipating effect of breaking waves.     

Ship-berth link performance evaluation: simulation and analytical approaches

In this paper we consider the performance evaluation of ship-berth link in port. The efficiency of operations and processes on the ship-berth link has been analysed through the basic operating parameters such as berth utilization, average number of ships in waiting line, average time that a ship spends in waiting line, average service time of a ship, average total time that a ship spends in port, average quay crane (QC) productivity and average number of QCs per ship. All the main performances of the ship-berth link are given. This is one of the problems faced by planners and terminal operators in ports. In this paper, we propose two models based on simulation and queuing theory, respectively, in order to determine the performance evaluation of ship-berth link in port. Numerical results and computational experiments are reported to evaluate the efficiency of the models for Pusan East Container Terminal (PECT).     

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).