Online Spark Timing Optimization With Complex High-Fidelity Combustion Phasing, Knock, and Coefficient of Variation of IMEP Models

The combustion phasing of spark ignition (SI) engines is traditionally regulated with map-based spark timing (SPKT) control. The calibration of these maps is time-consuming for SI engines with a high number of control actuators. This paper proposes three online SPKT optimization algorithms that can utilize control-oriented semiphysics-based combustion models making the SPKT control algorithm more adaptive to different engine designs. These three SPKT optimizers do not require model inversion and derivative information. These methods also preserve the dependence between combustion phasing, knock, and coefficient of variation (COV) of indicated mean effective pressure (IMEP) models to avoid evaluating combustion models multiple times within one iteration. The two-phase and constraint relaxation methods are derived from direct search optimization theories. The recursive least square (RLS) polynomial fitting method can be considered as a virtual extreme seeking (ES) process that converts the original black box nonlinear constrained optimization into the solution of three low-order polynomial equations. Although these three online SPKT optimization approaches have unique properties making them preferable with certain types of combustion models, simulation and test results show that all of them can find the optimal SPKT with less than 10 evaluations of the combustion models. This fact makes it possible to implement the proposed model-based SPKT control strategy in future engine control units (ECUs).

Automated summary

The paper introduces three online SPKT optimization algorithms that can leverage control-oriented semiphysics-based combustion models to enhance the adaptability of the SPKT control algorithm. These methods do not require model inversion and derivative information, and can effectively find the optimal SPKT. Simulation and test results demonstrate that these approaches can find the optimal SPKT with less than 10 evaluations of the combustion models, laying the foundation for implementing the proposed model-based SPKT control strategy in future engine control units (ECUs).