Major sources of hydrocarbon emissions in a premixed charge compression ignition engine

Although premixed charge compression ignition (PCCI) combustion engines are praised for potentially high efficiency and clean exhaust, experimental engines built to date emit more hydrocarbons (HCs) and carbon monoxide (CO) than the conventional machines. These compounds are not only strictly controlled components of the exhaust gas of road vehicles but are also an energy loss indicator. The prime objective of this study was to investigate the major sources of the HCs formed in the combustion chamber of an experimental PCCI engine in order to suggest some effective technologies for HC reduction. In this study, to explore the dominant sources of HC emissions in both operation modes, a single cylinder engine was prepared such that it could operate using either conventional diesel combustion or PCCI combustion. Specifically, the contributions of the top-ring crevice volume in the combustion chamber and the bulk quenching of the lean mixture were investigated. To understand the influence of the shape and magnitude of the crevice on HC emissions, the engine was operated with 12 specially prepared pistons with different top-ring crevices installed one after another. The engine emitted proportionally more HCs as the depth of the crevice increased as long as the width remained narrower than the prevailing quench distance. The top-ring-crevice-originated exhaust HCs comprised approximately 31% of the total HC emissions in the baseline condition. In a series of tests to estimate the effects of bulk quench on exhaust HC emissions, intake air was heated from 300K to 400K in steps of 25K. With the intake air heated, HC and CO emissions decreased with a gradually diminishing rate to zero at 375K. In conclusion, the most dominant sources of HC emissions in PCCI engines were the crevice volumes in the combustion chamber and the bulk quenching of the lean mixtures. The key methods for reducing HC emissions in PCCI engines are minimizing crevice volume in the combustion chamber and maximizing intake air temperature allowed based on the permissible NOx level.     

Application of a modified thermostatic control strategy to parallel mild HEV for improving fuel economy in urban driving conditions

A modified thermostatic control strategy is applied to the powertrain control of a parallel mild hybrid electric vehicle (HEV) to improve fuel economy. This strategy can improve the fuel economy of a parallel mild HEV by operating internal combustion engine (ICE) in a high-efficiency region. Thus, in this study, experiments of a parallel mild HEV were conducted to analyze the characteristics of the hybrid electric powertrain and a numerical model is developed for the vehicle. Based on the results, the thermostatic control strategy was modified and applied to the vehicle model. Also, battery protection logic by using electrochemical battery model is applied because the active usage of battery by thermostatic control strategy can damage the battery. The simulation results of the vehicle under urban driving conditions show that the thermostatic control strategy can improve the vehicle’s fuel economy by 3.7 % compared with that of the conventional strategy. The results also suggest that the trade-off between the fuel economy improvement by efficient ICE operation and the battery life reduction by active battery usage should be carefully investigated when a thermostatic control strategy is applied to a parallel mild HEV.     

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.     

Tire Lateral Force Estimation Using Kalman Filter

As for the tire analysis, lateral tire force is a fundamental factor that describes the stability of vehicle handling. Attempts to analyze the vehicle stability have been made based on various objective test methods and some specific factors such as yaw, lateral acceleration and roll angle. However, the problem to identify which axle is lack of the tire grip at a certain situation still remains. Since indoor tire force measurement system cannot represent a real road and vehicle conditions, tire force measurement through a real vehicle test is inevitable. Due to the high price of the tire force measurement device, tire force estimator can be an alternative toward cost reduction and device failure. In this paper, nonlinear planar full car model combined with tire model is proposed. Then, using discrete-time extended Kalman-Bucy filter (EKBF), individual tire lateral force are estimated with modified relaxation length model.     

Investigation into the development of a bus electrical cooling fan system

Manufacturers of commercial vehicles are facing a substantial increase of heat release into their cooling systems. The main sources for this increase are more stringent emissions leading to new combustion technologies and the increased power of these engines. The total increase in the cooling requirement may be up to 20% over the current level. At the same time, the noise levels must be decreased, and fuel economy has to improve. This forces manufacturers to consider new concepts and optimize the efficiency of the cooling system. A bus engine cooling fan system is one of the main means of vehicular fuel efficiency reduction. This is becoming a major factor in city noise, and the necessity of electromagnetic technical development is very great. This study features a highly effective BLDC motor for engine cooling fans with high effectiveness and low noise, which is most suitable for fan blade technical development and cooling fan performance evaluation technical development.     

Quantitative assessment of hydrocarbon explosion and fire risks in offshore installations

A risk-based design framework should involve both risk assessment and risk management. This article introduces and describes a number of procedures for the quantitative assessment and management of fire and gas explosion risks in offshore installations. These procedures were developed in a joint industry project on the explosion and fire engineering of floating, production, storage and off-loading units (the EFEF JIP), which was led by the authors. The present article reports partial results, focussing on defining the frequency of fires and explosions in offshore installations. Examples of the aforementioned procedures’ application to a hypothetical floating, production, storage, and off-loading unit (FPSO) are presented. A framework for the quantitative risk assessment of fires and explosions requires the definition of both the frequency and consequences of such events. These procedures can be efficiently applied in offshore development projects, and the application includes the assessment of design explosion and fire loads as well as the quantification of effects of risk control options (RCO) such as platform layout, location and number of gas detectors, isolation of ignition sources etc.     

Integration of a single cylinder engine model and a boost system model for efficient numerical mapping of engine performance and fuel consumption

A numerical engine mapping methodology is proposed for the engine performance and fuel consumption map generation. An integrated model is developed by coupling a single cylinder GT-Power® engine model with a MATLAB/ Simulink® based boost system model to simulate a turbocharged diesel engine over the entire engine operating speed and load ranges within reasonable computational constraints. A single cylinder engine model with the built-in multi-zone combustion modeling option in GT-Power® is configured as a predictive engine model. The cycle averaged simulation result from the engine model is used as the boundary conditions of the boost system including intake and exhaust manifolds and a turbocharger. The boost system model developed in MATLAB/Simulink® platform calculates the intake and exhaust conditions which are fed back to the engine model. The integrated system model predicts the performance and fuel consumption of a turbocharged diesel engine with better predictive capability than mean value engine models. Its computational time is fast enough to simulate the engine over the entire engine operation range compared to multi-cylinder engine models.     

Development of a self-driving car that can handle the adverse weather

Lane and road recognition are essential for self-driving where GPS solution is inaccurate due to the signal block or multipath in an urban environment. Vision based lane or road recognition algorithms have been studied extensively, but they are not robust to changes in weather or illumination due to the characteristic of the sensor. Lidar is a sensor for measuring distance, but it also contains intensity information. The road mark on the road is made to look good with headlight at night by using a special paint with good reflection on the light. With this feature, road marking can be detected with lidar even in the case of changes in illumination due to the rain or shadow. In this paper, we propose equipping autonomous cars with sensor fusion algorithms intended to operate in a different weather conditions. The proposed algorithm was applied to the self-driving car EureCar (KAIST) in order to test its feasibility for real-time use.     

Worst-case scenario development based on Sine with Dwell test and evaluation for vehicle dynamics controller in UCC HILS

The current test methods are insufficient to evaluate and ensure the safety and reliability of vehicle systems for all possible dynamic situations, including the worst case scenarios such as rollover, spin-out and so on. Although the known NHTSA Sine with Dwell steering maneuvers have been applied for the vehicle performance assessment, they are not enough to estimate other possible worst case scenarios. Therefore, it is crucial for us to verify the various worst case scenarios, including the existing severe steering maneuvers. This paper includes useful worst case scenarios based upon the existing worst case scenarios mentioned above and worst case evaluation for the vehicle dynamic controller in a simulation basis and UCC HILS. The only human steering angle was selected as a design parameter here and optimized to maximize the index function to be expressed in terms of both yaw rate and side slip angle. The obtained scenarios were enough to generate the worst case scenario to meet NHTSA worst case definition. It has been concluded that the new procedure in this paper is adequate to create other feasible worst case scenarios for a vehicle dynamic control system.     

Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving

Traffic accidents are caused by various factors, which can be classified into human factors, vehicle factors and environmental factors. Recently, human factors have been drawing particular attention as efforts are being made to enhance the safety performance of vehicles and improve road conditions. Driving distraction caused by an increased driving workload is a representative human factor. Various studies in the past have attempted to quantify the driving workload by using EEG activities. However, they have failed to consider vibration properties generated from vehicle engines. A number of noise signals were included in brainwave signal processing, which resulted in a failure to obtain reliable outcomes. Thus, this study suggests driver EEG activities free of vehicle engine secondary vibration in order to develop a method that analyzes the driving workload with high statistical reliability. By using the analytical method developed in this study, standard values of driving workload for straight and left-turn driving that has statistical significance could be calculated. The analytical method for driving workload created by this study can be applied to HVI and road design.