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A computer aided test system, which is called MOde Survey System (MOSS), is newly developed to evaluate a driving pattern and traffic flow in Seoul, Korea. This system is designed for the people who work on vehicle emissions and energy more quantitatively with the aim of being cost-effective and easy-to-use. Compared to currently-existing Test and Measurement Systems, MOSS is designed and developed for a specific goal with a couple of unique features. These features are: 1) To be able to be used as either a stand alone system like a data logging system or a real-time processing system with a PC to easily visualize vehicle performances and traffic information during the test. 2) To provide a statistical analysis program for easy use to analyze of driving pattern and traffic situation with logged test data files.

An evidential theory is widely used for 2D grid-based localization in a robotics field because the theory has benefits to consider additional states such as 'unknown' and 'conflict'. However, there are some problems such as computational limitation and excessive resource share when the localization system is expanded from 2D grid to 3D voxel. In order to overcome the problems, this paper proposes the parallelized particle filter based localization system using 3D evidential voxel maps. A many-core processor based parallel computing framework with optimization techniques is applied to accelerate the computing power. Experiments were performed to evaluate the performance of the localization system in a complex environment, and to compare the computational time and resources between various types of processing units. The experimental results show that the proposed parallel particle filter is much more efficient than particle filter without parallel computing regarding computational cost.

Cylinder air charge is an important parameter to reduce generation of visible emissions by adjusting the amount of fuel injected into a diesel engine. In this study, we propose a cylinder air charge estimation algorithm for a diesel engine equipped with variable geometry turbocharger (VGT), exhaust gas recirculation (EGR), and swirl control valve (SCV). The estimation algorithm predicts the cylinder air charge using a mean value air path model and measurable signals available in mass produced engines. The estimation algorithm addresses effects of the VGT, EGR, and SCV on the cylinder air charge. The proposed estimation algorithm was validated with a 1-D engine model simulation.

In order to evaluate vehicle powertrain performances and driver behaviors, a computer aided in-vehicle data acquisition system named MOde Survey System (MOSS) is newly developed in conjunction with a motor manufacturer. MOSS is designed to be used by personnel related to vehicle emissions and energy with the aim of being cost-effective and easy-to-use. Since driving behaviors and patterns influence powertrain performance in terms of fuel economy and emissions, engineers can utilize the system for understanding the driving pattern and traffic situation quantitatively. MOSS mainly consists of an MCU-based hardware and PC-based software. MOSS logs and analyzes various data related to vehicle driving. Compared to currently-existing Test and Measurement Systems, MOSS is designed and developed for a specific goal with several unique features.

In CRDI diesel engines, the piezo injector is gradually replacing the solenoid injector due to the quick response of the actuator. Operating performance of the injectors in the CRDI diesel engine has an influence on engine emissions. Therefore, accurate injector control is one of the most important parts of the CRDI engine control. The objective of this paper is the development of a piezo injector driver for CRDI diesel engines. Electrical characteristics of the piezo injector were analyzed. A control strategy for charging and discharging the actuator are proposed. The developed injector driver is verified by experiments under various fuel pressures, injection durations and driving circuit voltages.

This paper presents an exhaust gas recirculation (EGR) rate estimation algorithm for a turbocharged diesel engine. Accurate estimation of the EGR rate is important for precise air system control of a diesel engine. In order to estimate the EGR rate accurately, we developed an adaptive observer using a model reference identification scheme (MRIS). A linear parameter varying model for the intake manifold pressure dynamics is derived as the reference model for the adaptive observer. The intake and exhaust temperature models are developed through an empirical approach. The MRIS is used to design an update rule for the adaptive observer. Convergence of the proposed observer is proven by using the Lyapunov stability criterion. The proposed observer is implemented in a real-time embedded system and validated via engine experiments.