Model-Based Control of the Air Fuel Ratio for Gasoline Direct Injection Engines via Advanced Co-Simulation: An Approach to Reduce the Development Cycle of Engine Control Systems

Author(s):  
Alessandro di Gaeta ◽  
Umberto Montanaro ◽  
Veniero Giglio

Nowadays, the precise control of the air fuel ratio (AFR) in spark ignition (SI) engines plays a crucial role in meeting the more and more restrictive standard emissions for the passenger cars and the fuel economy required by the automotive market as well. To attain this demanding goal, the development of an advanced AFR control strategy embedding highly predictive models becomes mandatory for the next generation of electronic control unit (ECU). Conversely, the adoption of more complex control strategies affects the development time of the ECU increasing the time-to-market of new engine models. In this paper to solve the AFR control problem for gasoline direct injection (GDI) and to speed up the design of the entire control system, a gain scheduling PI model-based control strategy is proposed. To this aim, AFR dynamics are modeled via a first order time delay system whose parameters vary strongly with the fresh air mass entering the cylinders. Nonlinear relations have been found to describe the behavior of model parameters in function of air mass. Closed loop performances, when this novel controller is nested in the control loop, are compared to those provided by the classical PI Ziegler–Nichols control action with respect to different cost functions. Model validation as well as the effectiveness of the control design are carried out by means of ECU-1D engine co-simulation environment for a wide range of engine working conditions. The combination in one integrated designing environment of control systems and virtual engine, simulated through high predictive commercial one dimensional code, becomes a high predictive tool for automotive control engineers and enables fast prototyping.

2000 ◽  
Author(s):  
J. R. Wagner ◽  
D. M. Dawson ◽  
Z. Liu

Abstract The wide-range of operating conditions, inherent induction process nonlinearities, and gradual component degradations due to aging, have prompted research into model-based engine control algorithms. Consequentially, a variety of nonlinear and intelligent algorithms have been proposed and experimentally studied. Recent attention has focused on the simultaneous regulation of the air-to-fuel ratio and engine speed using a sliding mode control strategy. In this paper, a nonlinear model-based backstepping control strategy will be proposed for simultaneous air-to-fuel ratio control and speed tracking in passenger/light-duty automobile engines. For comparison purposes, a multi-surface sliding mode controller and an integrated speed-density air-to-fuel controller with attached engine speed regulation will be implemented. Representative numerical results will be presented and discussed.


Author(s):  
Shunki Nishii ◽  
Yudai Yamasaki

Abstract To achieve high thermal efficiency and low emission in automobile engines, advanced combustion technologies using compression autoignition of premixtures have been studied, and model-based control has attracted attention for their practical applications. Although simplified physical models have been developed for model-based control, appropriate values for their model parameters vary depending on the operating conditions, the engine driving environment, and the engine aging. Herein, we studied an onboard adaptation method of model parameters in a heat release rate (HRR) model. This method adapts the model parameters using neural networks (NNs) considering the operating conditions and can respond to the driving environment and the engine aging by training the NNs onboard. Detailed studies were conducted regarding the training methods. Furthermore, the effectiveness of this adaptation method was confirmed by evaluating the prediction accuracy of the HRR model and model-based control experiments.


Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2699 ◽  
Author(s):  
Giovanni Di Ilio ◽  
Vesselin K. Krastev ◽  
Giacomo Falcucci

The introduction of new emissions tests in real driving conditions (Real Driving Emissions—RDE) as well as of improved harmonized laboratory tests (World Harmonised Light Vehicle Test Procedure—WLTP) is going to dramatically cut down NOx and particulate matter emissions for new car models that are intended to be fully Euro 6d compliant from 2020 onwards. Due to the technical challenges related to exhaust gases’ aftertreatment in small-size diesel engines, the current powertrain development trend for light passenger cars is shifted towards the application of different degrees of electrification to highly optimized gasoline direct injection (GDI) engines. As such, the importance of reliable multidimensional computational tools for GDI engine optimization is rapidly increasing. In the present paper, we assess a hybrid scale-resolving turbulence modeling technique for GDI fuel spray simulation, based on the Engine Combustion Network “Spray G” standard test case. Aspects such as the comparison with Reynolds-averaged methods and the sensitivity to the spray model parameters are discussed, and strengths and uncertainties of the analyzed hybrid approach are pointed out. The outcomes of this study serve as a basis for the evaluation of scale-resolving turbulence modeling options for the development of next-generation directly injected thermal engines.


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