(438g) Fundamentals Based Low-Dimensional Combustion Models for Control and Optimization of IC Engines

The current trend towards simultaneously increasing fuel-to-wheels efficiency while reducing emissions from transportation system powertrains require system level optimization realized through multivariable control. Such an optimization can only be accomplished using fundamentals/first-principles based models for each of the engine sub-systems, i.e. in-cylinder combustion processes, exhaust after-treatment systems, mechanical and electrical systems (for hybrid vehicles) and sensor systems. The combustion process and catalyst systems can be described by the fundamental conservation laws (species, momentum and energy) of diffusion-convection-reaction type, such a description (consisting of many partial differential equations and complex chemistry) is extremely demanding computationally and is not useful for system level optimization studies. Moreover for online optimization and real-time control, these physics based models must be low-dimensional. At present, the bottleneck for attaining real-time onboard system level optimization is the lack of accurate low-dimensional models for the internal combustion (IC) engine.

In the present work, a five-mode (two concentration and three temperature modes) low-dimensional model for in-cylinder combustion process that includes all the relevant physics and chemistry occurring at different time and length scales is developed. The lumped parameter ordinary differential equation model is based on two mixing times tmix,1 and tmix,2 to capture the mixing limitations. The time tmix,1 captures the diffusion limitations inside the cylinder, while tmix,2 captures the mixing limitations caused by reactant entrance and product exit distribution. For example, the fueling dynamics and the dynamics of the air entrainment, valve angle, and fuel atomization are captured by tmix,2. For a given fuel inlet conditions, the model predicts exhaust composition of practically relevant regulated gases (total unburnt HCs, CO, and NOx) as well as computes the in-cylinder pressure and temperature of the combustion chamber. The model is also able to capture the trends observed with change in fuel composition (gasoline and ethanol blending), air/fuel ratio, and spark timing. The preliminary results show qualitative agreement with the experimental results published in the literature [J. B. Heywood, Internal Combustion Engine Fundamentals, McGraw-Hill, New York, 1988]. Further improvements to the model will be discussed.