Vehicle velocity estimation using effective inertia for an in-wheel electric vehicle

In this study, a vehicle velocity estimation algorithm for an in-wheel electric vehicle is proposed. This algorithm estimates the vehicle velocity using the concept of effective inertia, which is based on the motor torque, the angular velocity of each wheel and vehicle acceleration. Effective inertia is a virtual mass that changes according to the state of a vehicle, such as acceleration, deceleration, turning or driving on a low friction road. The performance of the proposed vehicle velocity estimation algorithm was verified in various conditions that included straight driving, circle driving and low friction road driving using the in-wheel electric vehicle that was equipped with an in-wheel system in each of its rear wheels.     

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.     

Realization of pmp-based control for hybrid electric vehicles in a backward-looking simulation

Optimal control is generally not possible without information about the future coming up, and it is not easy to obtain an optimal solution even though the information is given a priori. In this paper, a control concept based on Pontryagin’s Minimum Principle (PMP) is introduced as an efficient solution to generate an optimal control trajectory for Hybrid Electric Vehicles (HVEs) when the performance of the vehicles is evaluated on scheduled driving cycles at a simulation level. The main idea of the control concept is to minimize Hamiltonian, which is interpreted as equivalent fuel consumption, and the Hamiltonian is characterized by a co-state, which is interpreted as a weighting factor for the electrical usage. A key aspect of the control problem is that an appropriate initial condition of the co-state is required to satisfy the boundary condition of the problem. In this study, techniques to calculate the Hamiltonian in different hybrid configurations are introduced, and a methodology to look for the initial condition of the co-state is studied, so that the controller is able to realize a desired State Of Charge (SOC) trajectory. To address the issue, we utilize a shooting method with multiple initial conditions based on the concept of the Newton-Raphson method, and all these techniques are realized in a backward looking simulator. The simulation results show that the PMP-based control is a very efficient approach to produce the optimal control trajectory, and the performance is compared to the optimal solution solved by Dynamic Programming (DP).     

Road boundary detection and tracking for structured and unstructured roads using a 2D lidar sensor

Road boundaries can give useful information for evaluating safe vehicle paths in intelligent vehicles. Much previous research has studied road boundary detection, using different types of sensors such as vision, radar, and lidar. Lidar sensors, in particular, show advantages for road boundary extraction including high resolution and wide field of view. However, none of the previous studies examined the problem of detecting road boundaries when roads could be either structured or unstructured. In this study, we developed a road boundary detection and tracking algorithm using lidar sensing for both structured and unstructured roads. The algorithm extracts road features as line segments in polar coordinates relative to the lidar sensor. The extracted road features are then tracked with respect to a vehicle’s local coordinates using a nearest neighbor filter. The proposed algorithm accurately detected the road boundaries regardless of the road type.     

Analysis of the shifting behavior of a novel clutchless geared smart transmission

Conventional geared transmissions use some kinds of clutches to control the power flow from an internal combustion engine to the driveline while shifting gears. However, the shifting performance is seriously affected by the clutch engagement and an unavoidable drop in the torque may occur when the clutch is disconnected. Moreover, wear of the clutch, the need for hydraulic equipment, and the load limit may together aggravate the limits of the clutch system. For this reason, as a novel transmission without a clutch, the clutchless geared smart transmission (henceforth CGST) is proposed by our research team. The CGST controls the power flow in a multiple-input gear-train by controlling the electric motor attached to the planetary gear system. However, no CGST has been realized in an actual vehicle thus far, and the performance has been predicted only theoretically. In this research, we analyzed the achievable performance based on a developed CGST dynamic model with a typical CGST structure. In addition, a CGST gear-shifting algorithm is proposed for use with the dynamic model. From the simulation results, the CGST does not show an abrupt drop in its torque or oscillation while shifting gears due to the absence of a discontinuous power flow. The developed dynamic model can serve as a performance reference for the CGST. Moreover, it can be used as a simulation tool for developing a gear-shifting control logic scheme.     

The influence of personality on acceptability of sustainable transport policies

Previous research has shown that fairness, infringement on freedom, and perceived effectiveness are determinants of transport pricing acceptability. In the present study we investigate determinants of acceptability of environmental (carbon) taxation for which trust in government and environmental concern are additional determinants. Carbon taxation is an extension of fuel taxes and may thus be viewed as transport pricing. Our main focus is on the role played by personality traits. Structural equation modeling reveals that acceptability is related to the personality traits extraversion, agreeableness, and conscientiousness. Extraverted individuals have higher levels of trust in government which leads to higher acceptability. Also correlations between agreeableness and conscientiousness as well as environmental problem awareness and personal norm are observed. We discuss strategies for effective marketing of transportation policies considering how acceptability is related to personality traits.     

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.     

Analysis of a regenerative braking system for Hybrid Electric Vehicles using an Electro-Mechanical Brake

The regenerative braking system of the Hybrid Electric Vehicle (HEV) is a key technology that can improve fuel efficiency by 20∼50%, depending on motor size. In the regenerative braking system, the electronically controlled brake subsystem that directs the braking forces into four wheels independently is indispensable. This technology is currently found in the Electronic Stability Program (ESP) and in Vehicle Dynamic Control (VDC). As braking technologies progress toward brake-by-wire systems, the development of Electro-Mechanical Brake (EMB) systems will be very important in the improvement of both fuel consumption and vehicle safety. This paper investigates the modeling and simulation of EMB systems for HEVs. The HEV powertrain was modeled to include the internal combustion engine, electric motor, battery and transmission. The performance simulation for the regenerative braking system of the HEV was performed using MATLAB/Simulink. The control performance of the EMB system was evaluated via the simulation of the regenerative braking of the HEV during various driving conditions.     

Belt routing and safety assessment through computer simulation

Most of the research on safety belt systems has involved crash simulation that only considered a dynamic human model. However, belt routing analysis, usually known as comfort level estimation, is also an important factor in safety belt design, considering that serious injuries of the abdominal region result from the infiltration of a belt into the neck or the chest. Thus, safety belt evaluations using kinematic human models are also needed. In this paper, a belt fit simulation method is suggested. Using the proposed process, both comfort and safety analyses can be performed under the same conditions continuously, and thus the safety belt design parameters, such as the location of anchor points, dummy posture and etc., can be evaluated. In conclusion, this computer process enables a belt system design to reduce injuries.