Dynamic Modeling and Predictive Control of a Multi-Mode Combustion Engine

Author

Sadaf Batool, Michigan Technological UniversityFollow

Date of Award

2023

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Mahdi Shahbakhti

Advisor 2

Jeffrey D. Naber

Committee Member 1

Jeremy Worm

Committee Member 2

Nathir Rawashdeh

Abstract

Low temperature combustion (LTC) offers high thermal efficiency and low engine-out nitrogen oxides (NOx) and particulate matter (PM) emissions. Homogeneous charge compression ignition (HCCI), partially premixed charge compression ignition (PPCI) and reactivity-controlled compression ignition (RCCI) are the common LTC modes studied in this research. The primary barrier to implementing the LTC modes in on-road vehicles is their limited operating range due to high cyclic variability and excessive pressure rise rates. The feasible operating range of the LTC modes is only a subset of the speed-load range of the conventional spark ignition (SI) engine. Therefore, a multi-mode engine concept operating in one or more LTC modes and SI mode is a viable option to improve engine performance in terms of efficiency and emissions. The goal of this dissertation is to develop model-based closed loop control of an SI-RCCI-SI multi-mode engine.

Control-oriented models and predictive controllers for HCCI, PPCI and RCCI modes are developed to simultaneously control combustion phasing and engine load for an optimal operation of a multi-mode engine. Cyclic variability in HCCI and RCCI modes are modeled using machine learning classification algorithms. Nonlinear model predictive controllers are developed for HCCI and RCCI modes to control combustion phasing and engine load while constraining cyclic variability below 3%. Furthermore, LTC engine operation faces challenges of excessive pressure rise rates that can damage the hardware. To this end, supervised machine learning classification algorithms are developed to model the heat release type which is used as a scheduling variable to develop data-driven model for an LTC engine. Model predictive controller is then developed to control combustion phasing and engine load while constraining maximum pressure rise rate below 8 bar/CAD.

RCCI mode offers good control over the combustion event by modulating the start of injection timing of high reactivity fuel and adjusting the premixed ratio of the dual fuels. Therefore, this research focuses on SI-RCCI-SI multi-mode engine concept. The aim of this research is to achieve smooth SI-RCCI-SI mode switching operation at different engine loads and speed. A dynamic model for SI-RCCI-SI multi-mode engine is developed and validated for different transient conditions. The model includes the mode switching dynamics as well as actuator dynamics. A model-based predictive controller framework is developed for SI-RCCI-SI mode switching. The mode switching controller showed good performance during mode transitions and steady state engine operation. The controller is capable of tracking the desired combustion phasing and engine load during mode switching while maintaining $\lambda$ near stoichiometry in SI mode and constraining maximum pressure rise rate below 8 bar/CAD in RCCI mode.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Batool, Sadaf, "Dynamic Modeling and Predictive Control of a Multi-Mode Combustion Engine", Campus Access Dissertation, Michigan Technological University, 2023.

https://doi.org/10.37099/mtu.dc.etdr/1555