Cylinder-Specific Model-Based Control of Combustion Phasing for Multiple-Cylinder Diesel Engines Operating with High Dilution and Boost Levels

TL;DR

This paper develops and validates a cylinder-specific, model-based control system for precise combustion phasing in multi-cylinder diesel engines operating with high dilution and boost, improving efficiency and stability.

Contribution

It introduces a novel non-linear combustion model combined with intake gas property prediction for cylinder-specific control, and explores adaptive and feedforward control strategies validated through simulations.

Findings

CA50 reaches steady state within 10 cycles

Steady state errors are less than +/-0.1 CAD with adaptive control

Errors less than +/-1.3 CAD with feedforward control

Abstract

Accurate control of combustion phasing is indispensable for diesel engines due to the strong impact of combustion timing on efficiency. In this work, a non-linear combustion phasing model is developed and integrated with a cylinder-specific model of intake gas. The combustion phasing model uses a knock integral model, a burn duration model and a Wiebe function to predict CA50 (the crank angle at which 50% of the mass of fuel has burned). Meanwhile, the intake gas property model predicts the EGR fraction and the in-cylinder pressure and temperature at intake valve closing (IVC) for different cylinders. As such, cylinder-to-cylinder variation of the pressure and temperature at intake valves closing is also considered in this model. This combined model is simplified for controller design and validated. Based on these models, two combustion phasing control strategies are explored. The first is an adaptive controller that is designed for closed-loop control and the second is a feedforward model-based control strategy for open-loop control. These two control approaches were tested in simulations for all six cylinders and the results demonstrate that the CA50 can reach steady state conditions within 10 cycles. In addition, the steady state errors are less than +/-0.1 crank angle degree (CAD) with the adaptive control approach, and less than +/-1.3 CAD with feedforward model-based control. The impact of errors on the control algorithms is also discussed in the paper.