Kangyoon Lee

Integration of multiple vehicle models with an IMM filter for vehicle localization

A vehicle localization system can be extremely useful for intelligent transformation systems (ITS) such as advanced driver assistance systems (ADASs), emergency vehicle notification systems, and collision avoidance systems. To optimize the performance of vehicle localization systems, localization algorithms that analyze multi-sensor data processed using a Kalman filter have been developed. However, a Kalman filter with a single process model cannot guarantee the accuracy of localization under various driving conditions, because the single vehicle model does not cover all driving situations. Therefore, we present a position estimation algorithm based on an interacting multiple model (IMM) filter that uses two kinds of vehicle models: a kinematic vehicle model and a dynamic vehicle model. While the kinematic vehicle model is suitable for low-speed and low-slip driving conditions, the dynamic vehicle model is more appropriate for high-speed and high-slip situations. The IMM filter integrates the estimates from a kinematic vehicle model based on an extended Kalman filter (EKF) and estimates from a dynamic vehicle model based on EKF to improve localization accuracy. The developed estimation algorithm was verified by simulation using a commercial vehicle model. The simulation results show that the estimates of vehicle position by the algorithm presented in this study are accurate under a wide range of driving conditions.

Closed-loop control of start of combustion using difference pressure management

Abstract A great deal of attention is being paid to the common-rail direct injection (CRDI) diesel engine as a promising technology for enhancing engine performance and satisfying stringent emission regulations. In a conventional CRDI diesel engine, the start of combustion (SOC) is controlled in an open-loop manner by adjusting the start of energizing (SOE) of an injector. The open-loop SOC control cannot compensate for unexpected variations in the injection delay and ignition delay resulting from cycle-by-cycle variation, cylinder-to-cylinder variation, production variation, and ageing. In this study, cylinder pressure was investigated as a means for controlling the SOC of a CRDI diesel engine. Various pressure variables were compared for the purpose of detecting the SOC of a CRDI diesel engine. The crank angle position at which the difference pressure becomes 10 bar (CADP10) was selected as the pressure variable for the detection of the SOC. The control performance was evaluated with engine-dynamometer experiments in steady and transient operating conditions. The experimental results showed that difference pressure managing can be effectively used for real-time detection of the SOC. Furthermore, the SOC detection technique enables the fuel control strategy to be transformed from an open-loop scheme to a closed-loop scheme.

Real-Time IMEP Estimation and Control Using an In-Cylinder Pressure Sensor for a Common-Rail Direct Injection Diesel Engine

An in-cylinder pressure-based control method is capable of improving engine performance, as well as reducing harmful emissions. However, this method is difficult to be implemented in a conventional engine management system due to the excessive data acquisition and long computation time. In this study, we propose a real-time indicated mean effective pressure (IMEP) estimation method using cylinder pressure in a common-rail direct injection diesel engine. In this method, difference pressure integral (DPI) was applied to the estimation. The DPI requires only 180 pressure data points during one engine cycle from top dead center to bottom dead center when pressure data are captured at every crank angle. Therefore, the IMEP can be estimated in real time. To further reduce the computational load, the IMEP was also estimated using DPI at 2 deg, 3 deg, and 4 deg crank angle resolutions. Furthermore, based on the estimated IMEP, we controlled IMEP using a radial basis function network and linear feedback controller. As a result of the study, successful estimation and control were demonstrated through engine experiments.

A study on the injection characteristics of a liquid-phase liquefied petroleum gas injector for air-fuel ratio control

Abstract Liquefied petroleum gas (LPG) is widely used as a gaseous fuel in spark ignition engines because of its considerable advantages over gasoline. However, the LPG engine suffers a torque loss because the vapour-phase LPG displaces a larger volume of air than do gasoline droplets. In order to improve engine power as well as fuel consumption and air-fuel ratio control, considerable research has been devoted to improving the LPG injection system. In the liquid-phase LPG injection systems, the injection rate of an injector is affected by the fuel temperature, injection pressure, and driving voltage. When injection conditions change, the air-fuel ratio should be accurately controlled in order to reduce exhaust emissions. In this study, correction factors for the fuel injection rate are developed on the basis of fuel temperature, injection pressure, and injector driving voltage. A compensation method to control the amount of injected fuel is proposed for a liquid-phase LPG injection control system. The experimental results show that the liquid-phase LPG injection system works well over the entire range of engine speeds and load conditions, and the air-fuel ratio can be accurately controlled by using the proposed compensation algorithm.

Experimental analysis of a liquid-phase liquefied petroleum gas injector for a heavy-duty engine:

Abstract Liquefied petroleum gas (LPG) is a well-known clean alternative fuel for vehicles. In a heavy-duty, liquid-phase LPG injection (LPLi) system, several factors influence the amount of fuel injected. These factors are fuel temperature, fuel injection pressure, battery voltage, and so on. In order to maintain the stoichiometric air-fuel ratio in the cylinder during engine operation, the injected fuel quantity should be controlled accurately. In this study, the characteristics of the injector used in the heavy-duty LPLi engine are investigated experimentally. The fuel injection algorithm, which compensates for the variations in the injection pressure, the fuel temperature and the battery voltage, is developed and verified through engine tests.

Parameter Estimation and Modeling of HSDI Common-Rail Injector Using Feedforward Neural Network

This study presents the process of the solenoid parameter estimation of an common-rail injector fer HSDI(High Speed Direct Injection) diesel engines. The EMF(Electromotive Force) and solenoid inductance are the major parameters for presenting the injector dynamics, and also these parameters are estimated by using a multi-layer feedforward artificial neural networks(ANN). The performances of parameter estimators are verified by the simulation with injector model. The feasibility of this methodology is closely examined through the simulation in the various operating points of injector. The simulation results have revealed that estimated parameters show favorable agreements with the common-rail injector model.