Yeongseop Park

Observer design for exhaust gas recirculation rate estimation in a variable-geometry turbocharger diesel engine using a model reference identification scheme

Exhaust gas recirculation systems are used in diesel engines to reduce nitrogen oxide emissions. Since excessive recirculation in the cylinders may lead to an increase in generation of particulate matter and to unstable combustion, the exhaust gas recirculation rate should be measured correctly and should be controlled precisely. Unfortunately, the harsh conditions of the exhaust gas recirculation path make it difficult to measure the exhaust gas recirculation mass flow rate directly by using the relevant sensors. Therefore, precise control of the exhaust gas recirculation system depends on accurate estimation of the exhaust gas recirculation rate. To estimate accurately the exhaust gas recirculation rate in a turbocharged diesel engine, we propose an observer based on a model reference identification scheme. A linear parameter-varying model of the intake manifold pressure was derived to serve as the observer’s reference model. An update rule of the observer was designed with the model reference identification scheme. The intake and exhaust temperature models were developed through an empirical approach. Convergence of the proposed observer was proven in terms of the Lyapunov stability criterion. The proposed observer was implemented on a real-time embedded system and validated successfully through experiments on the engine.

Nonlinear Compensators of Exhaust Gas Recirculation and Variable Geometry Turbocharger Systems using Air Path Models for a CRDI Diesel Engine

This paper investigates the design of model-based feedforward compensators for exhaust gas recirculation (EGR) and variable geometry turbocharger (VGT) systems using air path models for a common-rail direct injection (CRDI) diesel engine to cope with the nonlinear control problem. The model-based feedforward compensators generate set-positions of the EGR valve and the VGT vane to track the desired mass air flow (MAF) and manifold absolute pressure (MAP) with consideration of the current engine operating conditions. In the best case, the rising time to reach 90% of the MAF set-point was reduced by 69.8% compared with the look-up table based feedforward compensators.

Development of Turbine Mass Flow Rate Model for Variable Geometry Turbocharger Using Artificial Neural Network

지구온난화 문제와 에너지고갈 문제에 대응하기 위하여 세계 각국에서는 차량용 내연기관에 대한 연비규제를 점차 강화하고 있다. 디젤엔진은 가솔린엔진 대비 높은 열효율에 의한 우수한 연비, 낮은 이산화탄소 배출 등의 이유로 차세대 자동차의 Key Words: Turbine Model(터빈 모델), Artificial Neural Network(인공신경망), Diesel Engine(디젤엔진), Variable Geometry Turbocharger(가변 기구 터보차저), Mass Flow Rate(질량 유량) 초록: 이 논문에서는 인공신경망을 이용하여 가변 기구 터보차저(VGT)의 터빈 질량유량을 추정하는 모델을 제안하고자 한다. 터빈 질량유량을 추정하기 위한 모델의 입력변수는 VGT 베인 개도량, 엔진 회전속도, 배기매니폴드 압력, 배기매니폴드 온도, 터빈 출구 압력이 사용되었으며, 터빈 입구 유효 단면적을 추정하는 부분에 인공신경망을 적용하였다. 실험을 통하여 이 논문에서 제안한 모델의 터빈 질량유량 추정 성능을 검증하였으며, 터빈 맵을 이용하여 추정한 결과와 비교를 통하여 제안한 모델의 우수성을 확인하였다. Abstract: In this paper, we propose a turbine mass flow rate model for a variable geometry turbocharger (VGT) using an artificial neural network (ANN). The model predicts the turbine mass flow rate using the VGT vane position, engine rotational speed, exhaust manifold pressure, exhaust manifold temperature, and turbine outlet pressure. The ANN is used for the estimation of the effective flow area. In order to validate the results estimated by the proposed model, we have compared estimation results with engine experimental results. The results, in addition, represent improved estimation accuracy when compared with the performance using the turbine map.