A Control Oriented Soot Prediction Model for Diesel Engines Using an Integrated Approach

Abstract

Diesel engines have been used in many vehicles and power generation units since a long time due to their less fuel consumption and high trustworthiness. With reference to upcoming emission norms, various engine out emissions have proved to be causing adverse effect on human health and environment. Soot, or particulate matter is one of the major pollutants in diesel engine out emissions and causes various lung related issues. There have been efforts to reduce the amount of soot generated using after-treatment devices like diesel particulate filter (DPF) to filter out particles and get clean tailpipe emissions. These technologies increase load on the system and involves additional maintenance. Also, deposition-based soot sensors have been found to be inoperative in certain scenarios like cold start conditions. In this research work, an effort has been made to develop a phenomenological model that predicts soot mass generated in a Cummins 6.7L diesel engine. The model uses in-cylinder conditions such as pressure, bulk mean temperature, fuel mass flow rate and injector orifice diameter. The difference between soot mass formed and oxidized yields the net amount of soot generated at engine out end. Furthermore, the generated soot mass is compared with benchmark results for specific load conditions and appropriate controller is designed to minimize this tradeoff. The control parameter being used here is fuel rail pressure, which controls the lift-off length, and ultimately equivalence ratio, which predicts mass of soot, generated in formation phase. The presented method shows a prediction error ranging from 5–20%, which is significantly reduced to 2% using a PID controller. The approach presented in this research work is generic and can be operated as stand-alone system or an integrated subsystem in a higher order control architecture.