Model-order reduction for nonlinear distributed parameter systems with application to internal combustion engine modeling and simulation - Stockar, Stephanie (ProQuest Dissertations & Theses, 2013)
In recent years, difficult challenges have been faced by the automotive industry to meet the increasingly ambitious fuel economy objectives without sacrificing the vehicle performance, hence remaining appealing to the market. In this sense, the automotive industry is moving toward the implementation of advanced powertrain technologies aiming at improving the efficiency of breathing and combustion in Internal Combustion Engines (ICEs). With the advancements in engine design, the air path systems have become more sophisticated and advanced control techniques are needed in order to fully exploit the potential of existing technologies. This has pushed the automotive industry to adopt more efficient procedures for modeling, system simulation and model-based control. Such procedures require accurate and computationally efficient engine system simulation models that are able to predict the cylinder charge composition and the thermodynamic conditions with limited calibration effort. To this extent, reduced-order models for unsteady compressible flow systems, namely models derived from the fundamental conservation laws without over-simplifying the topology of the engine air path, are receiving considerable interest for performance simulation and control design. These models have high predictive abilities and maintain a relatively simple structure, namely, implementable as low-order systems of linear or nonlinear Differential and Algebraic Equations (DAEs). In this scenario, a novel mathematical modeling methodology is presented in this dissertation for the solution of non-linear hyperbolic partial differential equation describing the wave propagation in compressible fluids. The proposed approach combines an integral representation of the conservation laws for compressible fluids with a projection onto a pre-determined basis, which capture and solve the spatially-dependent nature of the system separately from its time evolution. This allows one to describe propagatory-type phenomena (intrinsically represented by a distributed-parameter system) as a dynamical system expressed through a set of coupled non-linear Ordinary Differential Equations (ODEs), hence through a lumped-parameter model that is amenable for estimation and control applications. The proposed modeling methodology is validated on a number of case studies ranging from the propagation of an acoustic wave in a pipe to the modeling and simulation of a single-cylinder engine.