Effects of Injection Timing on Transient Performance of A Regulated Two-Stage Turbocharged Diesel Engine with Turbine Bypass Valve

The object of this paper is to reduce soot emissions under typical 5s transient conditions of constant speed and increasing torque. And effects of fuel injection timing on combustion and emissions parameters were experimentally and numerically studied in a regulated two-stage turbocharged diesel engine with a turbine bypass valve (TBV). The test results indicated that: the smaller TBV opening could improve deterioration of smoke emissions and BSFC at medium and heavy loads. Afterward, the full-stage injection timing (FSIT) strategies (delaying injection timing during the entire transient process) could reduce soot and NO_X emissions simultaneously. However, when TBV opening became larger, smoke emissions and BSFC were deteriorated gradually. Moreover, the sectional-stage injection timing (SSIT) strategies (advancing injection timing from 10 % load to a preset load and delaying injection timing from the preset load to 100 % load) could markedly reduce soot emissions by 75.8 % with TBV opening 20 %; the degradation of fuel consumption could be effectively suppressed. Finally, coupling the SSIT strategies with the TBV control strategies could significantly improve the transient performance.     

Vehicle stability control based on driver’s emergency alignment intention recognition

In this work, the reference model modification strategy for vehicle stability control based on driver's intention recognition under emergent obstacle avoidance situation was proposed. First the conflicts between the driver's emergency alignment (EA) intention and vehicle response characteristics were analyzed in critical emergent obstacle avoidance situation. Second combining steering wheel angle and its speed, the driver's EA intention was recognized. The reference model modification strategy based on steering operation index (SOI) was presented. Then a LQR model following controller with tire cornering stiffness adaption was used to generate direct yaw moment for tracking modified reference yaw rate and reference sideslip angle. Finally based on the four-in-wheel-motor-drive (FIWMD) electric vehicles (EV), double lane change and slalom tests were conducted to compare the results using modified reference model with the results using normal reference model. The experimental tests have proved the effectiveness of the reference model modification strategy based on driver's intention recognition.     

Hydraulic retarders for heavy vehicles: Analysis of fluid mechanics and computational fluid dynamics on braking torque and temperature rise

Hydraulic retarders are auxiliary braking devices that reduce the velocity of a vehicle, particularly when a vehicle is driven downhill. Such velocity reduction could reduce the potential risk caused by brake failure caused by the service brake working for a long time and the temperature of the brake shoe becomes extremely high. This paper introduces the construction of the hydraulic retarder and proposes two mathematical models for the hydraulic retarder. The first mathematical model is deduced by using fluid mechanics, which is used to analyze the mechanism of how braking torque is produced and the key factors that can influence the value of the braking torque. The second mathematical model is deduced by using thermodynamics, which is used to quantify the heat produced by the hydraulic retarder. This research emphasizes that the flow rate and the average velocity of the working fluid in the working chamber mainly determine the braking torque of the hydraulic retarder. The flow rate into and out of the working chamber determines the temperature rise of the working fluid. Computational fluid dynamics (CFD) simulations are conducted with the Reynolds-averaged Navier-Stokes (RANS) and Shear Stress Transport (SST) turbulent models. Experiments are carried out to justify the two mathematical models and the CFD simulations. The results show that the mathematical models are capable of describing the force analysis and energy conversion of the hydraulic retarder and SST is more accurate for CFD simulation and the error is within 6 %.     

Iterative Learning Control Algorithm for Feedforward Controller of EGR and VGT Systems in a CRDI Diesel Engine

The modern diesel engines equip the exhaust gas recirculation (EGR) system to suppress the NOx emissions. In addition, the variable geometric turbocharger (VGT) system is installed to improve the drivability and fuel efficiency. These EGR and VGT systems have cross-coupled behavior because they interact with the intake and the exhaust manifolds. Furthermore, the turbocharger time delay, gas flow dynamics through EGR pipe cause the nonlinearity characteristics. These nonlinear multi-input-multi-output characteristics cause the degradation of control accuracy, especially during the transient condition. In order to improve the control accuracy, this study proposes an iterative learning control (ILC) algorithm for feedforward controller of EGR and VGT systems. The feedforward controller obtains the values about EGR and VGT actuators using the previous control results in predefined transient states. The ILC algorithm consists of a PD-type learning function and a low-pass filter. The control gains of learning function are determined to guarantee the convergence of learning results. In addition, the low-pass filter is designed for robustness against plant disturbance. The proposed control algorithm was validated by engine experiment which repeated predefined transient states. The error was reduced by 15 % according to the gain. As a result, the proposed algorithm is affordable for improving the transient control performance.     

Harmonizing heavy goods vehicle taxes in Europe: a British view

One of the biggest unresolved problems regarding the completion of the free internal transport market within the European community is the problem of harmonization of taxation on the road haulage industry. As the highest tax country, Britain might be expected to have to reduce its level of taxes as part of any harmonization.

This paper considers the current methodology of the British Department of Transport regarding the allocation of the track and external costs of road transport, in the light of developments in methodology and experience elsewhere in Western Europe and North America. We find a number of reasons for believing that, far from overstating the marginal social cost of use of roads by the road haulage industry, the current British approach actually understates it. Thus any move towards harmonizing tax levels may reduce taxes in Britain at a time when they should be increasing.     

Future of road safety and SDG 3.6 goals in six Indian cities

In this paper, we use estimates of the effectiveness of existing road safety technologies and interventions to estimate the reduction in road safety deaths in six Indian cities over the next decade, in order to achieve the SDG 3.6 goal of a 50% reduction by 2030. Only the existing interventions are considered and technologies that might be available in the future are not accounted for. The results show that similar policies for all cities will not produce the same results, and that achieving the SDG 3.6 goal does not automatically reduce fatality rates in cities with high fatality rates. The introduction of safer cars with currently available safety technologies, although necessary, will have much less effect than the combined effect of motorcycle safety technologies, speed control and traffic calming. This study suggests that while SDG 3.6 targets may be satisfied by 2030 with great effort in India, the presently available safety interventions may not be adequate to bring about death rates below 2.0 per hundred thousand persons. Since the situation differs significantly between cities, it will be important to evolve city-specific policies for safety intervention priorities and changes in travel behavior. The desired reduction in road traffic injuries in India will not be possible without much greater investment in road safety research and road design for safer travel.     

A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data

A variety of automatic data collection technologies have been used to gather road and highway system data. The majority of these automatic data collection technologies are designed to collect vehicle-based data and either do not have the capability to collect other travel mode data (e.g., bicycles and pedestrians), or may need to be deployed differently to support this capability.

One type of wireless-based data collection system that has been deployed recently is based on Bluetooth technology. A key feature of Bluetooth-based data collection systems that makes travel mode identification feasible is that the Bluetooth-enabled devices within vehicles are also present on bicyclists and pedestrians. This research explores the effectiveness of applying cluster analysis methods when processing data collected via Bluetooth technology from vehicles, bicyclists, and pedestrians to automatically identify the associated travel modes. The results of several experiments utilizing multiple Bluetooth-based data collection units arranged linearly and in relatively close proximity on a simulated intersection demonstrate the potential of cluster analysis to accurately differentiate transportation modes from the collected data.     

Autonomous Emergency Braking Considering Road Slope and Friction Coefficient

The Autonomous Emergency Braking (AEB) systems have been actively studied for the safety enhancement and commercialized for the past few years. Because the driver tends to overly rely upon active safety systems, AEB needs to be designed to reflect the real road situations such as various road slope and friction coefficient. In this study, an AEB control algorithm is proposed to compensate for the effects of the slope and the friction of road. Based on the maximum possible deceleration for the real road conditions, the minimum braking distance is described with margin parameters for AEB activation control. The deceleration controller with a feedforward term is designed to avoid the collision during AEB operation on real road conditions. The proposed algorithm is validated in simulations first and the experimental verification is performed in the various slope conditions.     

Development of a Driving Behavior-Based Collision Warning System Using a Neural Network

An advanced driver assistance system (ADAS) uses radar, visual information, and laser sensors to calculate variables representing driving conditions, such as time-to-collision (TTC) and time headway (THW), and to determine collision risk using empirically set thresholds. However, the empirically set threshold can generate differences in performance that are detected by the driver. It is appropriate to quickly relay collision risk to drivers whose response speed to dangerous situations is relatively slow and who drive defensively. However, for drivers whose response speed is relatively fast and who drive actively, it may be better not to provide a warning if they are aware of the collision risk in advance, because giving collision warnings too frequently can lower the reliability of the warnings and cause dissatisfaction in the driver, or promote disregard. To solve this problem, this study proposes a collision warning system (CWS) based on an individual driver’s driving behavior. In particular, a driver behavior model was created using an artificial neural network learning algorithm so that the collision risk could be determined according to the driving characteristics of the driver. Finally, the driver behavior model was learned using actual vehicle driving data and the applicability of the proposed CWS was verified through simulation.     

Component Sizing of Parallel Hybrid Electric Vehicle Using Optimal Search Algorithm

In designing a parallel hybrid electric vehicle, it is essential to select the optimal capacity of power sources and the optimal gear ratio of the torque coupler. The capacity of the power sources and the gear ratio of the torque coupler should be optimized simultaneously. However, since this process is excessively time-consuming, previous studies have selected the gear ratio of the torque coupler and then selected the capacity of power source. However, this approach cannot guarantee global optimization. In this paper, a feasible region is defined to satisfy the required performance of vehicle such as maximum speed, hill-climbing. and feasible points are selected inside the feasible region. In the conventional technique, the global optimal solution is obtained by simulating all feasible points. In the optimization technique, optimal points are simulated within the feasible region using several optimal search algorithms, such as the golden section search algorithm and the hillclimbing search algorithm. And using these algorithms, the number of simulations is reduced and the capacity of the power source and the gear ratio of the torque coupler are optimized simultaneously. Finally, the validity of the component sizing results is verified by comparing the global optimal solution obtained by applying the conventional technique with the solution obtained by applying the proposed optimization technique.