Generation of Internal Combustion Engine Maps and Spark Timing Profiles Using Metamodels

With the growth of computing technologies, many leading automotive companies tend to use simulation tools to reduce the number of actual engine testing for evaluating the performance of Internal Combustion (IC) engines. However, a high-fidelity engine model which is very complex and computationally demanding, is needed. In this dissertation, we present efficient and accurate metamodels to predict an engine fuel map and to also obtain the spark timing profile to generate a specified torque curve. Time-dependent Kriging metamodels using Singular Value Decomposition (SVD) and Nonlinear Autoregressive metamodels with Exogenous inputs (NARX) in conjunction with Neural Networks (NN) are developed and used. A sequential process was first developed to generate steady-state engine fuel maps using Kriging accounting for different engine characteristics at different operating conditions. The generated map predicts engine output parameters such as Brake Mean Effective Pressure (BMEP) and fuel flow rate. The Kriging metamodels are created sequentially to ensure acceptable accuracy with a small number of expensive engine simulations. Two optimization problems are solved for full load and part load conditions, respectively. We demonstrate that the estimated fuel map is of high accuracy compared to the actual map. The internal combustion engine is a source of unwanted vehicle vibration produced by engine mount forces which depend on the engine torque profile during a transient tip-in or tip-out maneuver. A methodology was also developed to obtain the desired engine torque profile to minimize the unwanted vibration by controlling a set of engine calibration parameters. A set of design coefficients defining a spark timing profile and the corresponding engine torque profiles are used to construct time-dependent metamodels using SVD and Kriging. The accuracy of the approach is demonstrated using GT-Power engine simulations. In addition, we developed a time-dependent NARX-NN metamodel to predict engine spark timing and cylinder pressure profiles corresponding to a desired torque profile. The NARX-NN metamodel predicts the spark timing accurately using a very small number of engine simulations.