Model Based Control Method for Diesel Engine Combustion

Abstract

With the increase of information processing speed, more and more engine optimization work can be processed automatically. The quick-response closed-loop control method is becoming an urgent demand for the combustion control of modern internal combustion engines. In this paper, artificial neural network (ANN) and polynomial functions are used to predict the emission and engine performance based on seven parameters extracted from the in-cylinder pressure trace information of over 3000 cases. Based on the prediction model, the optimal combustion parameters are found with two different intelligent algorithms, including genetical algorithm and fish swarm algorithm. The results show that combination of quadratic function with genetical algorithm is able to obtain the appropriate combustion control parameters. Both engine emissions and thermal efficiency can be virtually predicted in a much faster way, such that enables a promising way to achieve fast and reliable closed-loop combustion control.

Calculation of Intake Oxygen Concentration through Intake CO2 Measurement and Evaluation of Its Effect on Nitrogen Oxide Prediction Accuracy in a Heavy-Duty Diesel Engine

“…This interest can be confirmed by several studies that have recently been reported in the literature. Some examples are provided in [1][2][3][4][5][6], in which the authors show the advantages of model-based control for several applications, including vehicle speed management, hybrid powertrain energy management and engine management. In [1], the authors proposed a dynamic programming-based optimal speed-planning algorithm for heavy-duty vehicles based on V2X (vehicle-to-everything) communication and look-ahead function.…”

“…In [3], the authors described a hierarchical-model predictive-control framework that can be used to coordinate the power split and the thermal management of the exhaust in diesel hybrid electric vehicles, with the aim of reducing fuel consumption and optimizing the exhaust temperature. In [4], the authors applied a model-based technique to identify the optimal combustion parameters for an 8.42 L diesel engine by exploiting artificial neural networks and polynomial functions. In [5], the authors proposed a real-time physics-based combustion model for diesel combustion to predict the heat release rate, for model-based control purposes.…”

A Hybrid Physics-Based and Stochastic Neural Network Model Structure for Diesel Engine Combustion Events

“…A standard deviation of 0.67 CAD, 1.19 CAD, and 0.223 bar was achieved for the predictions of the start of combustion (SOC), CA50, and IMEP, respectively. Similarly, Wang et al [20] combined polynomial fitting functions and ANNs to predict diesel engine emissions and performance. A quadratic function was utilized to parameterize CA50, and the resulting model was used to optimize engine emissions and thermal efficiency.…”

“…The literature demonstrates that leveraging physics to inform ANN models could enhance predictions of engine metrics and, as such, improve real-time combustion phasing control methods without the strict need for in-cylinder sensors. While prior work using either direct ANNs or integrated ANN and physics-based approaches to predict critical engine metrics has shown promise [1,[5][6][7]13,15,16,[18][19][20][21][22][23][24], some challenges with uncertainty and complexity still need to be addressed. Some existing methods assume linear relationships and, as such, are simpler to use for control, but have a more limited ability to extrapolate, and encounter greater inaccuracy due to the nonlinearity of engine processes [7,13,18,22].…”