Minkwang Lee

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.

Model-based control of a diesel engine variable geometry turbine and exhaust gas recirculation system using a linear parameter varying methodology

Diesel engines are widely used for passenger cars these days because of their high fuel efficiency. Although diesel engines have emission problems (oxides of nitrogen, particulate matter) originated from the compression ignition combustion characteristics, both of these types of emissions have been reduced by after-treatment systems such as selective catalytic reduction, diesel particulate filter and so on. In addition, due to the stringent carbon dioxide regulations, the fuel-efficient diesel engine as a powerplant for passenger cars occupies a major position in the passenger car market in Asia and Europe. In order to make diesel engines clean and efficient, appropriate control systems for air-path, combustion, and after-treatment are required. Particularly, an air-path control system is the most important part of the control system of diesel engines because the air-path system determines the dynamic behaviour of the engines. Control strategies for combustion and after-treatment systems are generated based on the air-path behaviour. In this paper, modelling and control of an air-path system for a turbocharged common-rail direct injection diesel engine are proposed. A linear parameter varying methodology is adopted for deriving an air-path system model. The linear parameter varying technique has the advantage of converting a complex nonlinear system in modelling into a simplified linear system. The air-path system model is derived with a linear three-state model, and the model accuracy is verified by driving cycle tests. The framework of a robust H∞ controller design is applied to the simplified linear parameter varying air-path model, and this synthesized H∞ controller is validated with engine experiments. The experimental results show that linear parameter varying model-based diesel engine air-path control allows better performance than a production engine management system, which requires massive calibration work.