Methodology for rapid static and dynamic model-based engine calibration and optimization.

摘要

Over the past decade, the government regulations and legislations on pollutant emissions produced by transportations, especially by automotive vehicles, have become increasingly stringent. Moreover, the rise of gasoline price in recent years and increasing demand for large Sport Utility Vehicles (SUV's), especially by customers in the North American market, have driven automotive industries to focus on developing advanced technologies for production engines to reduce fuel consumption and pollutant emissions. As a result of the advancements in automotive technologies, today's Internal Combustion (IC) engines are equipped with advanced actuators and sensors, which provide more control authority than ever before, while changing how engines are operated. These additional actuator inputs (for example, direct fuel injection, variable intake system, intake port throttling, variable valve actuation, etc.) can lead to a sizable improvement in fuel economy and emissions. However, these additional degrees of control freedom can also lead to a significant increase in engine mapping and calibration requirements during the engine design and control system development processes.; For this reason, this research work proposes a comprehensive methodology that provides a systematic approach to assist an engine control system development and calibration processes in the model-based framework. This alternative approach for developing an engine control system could in principle relieve some of the experimental burdens while accelerating the development and calibration processes. The proposed methodology has been demonstrated based on an advanced IC engine equipped with a Variable Valve Actuation (VVA) system. First, a new approach of obtaining reliable engine maps using a validated crank-angle resolved engine model has been introduced. Then, the utility and feasibility of the model-based engine map have been demonstrated by using it to develop a mean-value engine plant model and validate its outputs with the vehicle data acquired during transient driving cycle tests. This type of engine model is a computer-aided tool that can provide a rapid and accurate evaluation of a prototype control algorithm by using it as a virtual engine plant with which control engineers can quickly test and validate their control algorithm via, for example, hardware-in-the-loop simulations. It has been found that this new methodology provides a tightly coupled approach presenting significant advantages for reducing the complexity of an overall process of engine mapping, calibration, and control system design as well as providing a robust and rational framework in developing optimal control strategies.