Development of an ‘Online’ Combustion System Calibration Method using a Non-Dominated Sorting Genetic Algorithm

Calibration is a major resource bottleneck and source of risk in powertrain technology development. A promising alternative to the typical design-of-experiments (DoE) approach is the use of a ‘Non-Dominated Sorting Genetic Algorithm’ (NSGA) calibration method, where an iterative process is used to directly identify the Pareto Fronts between performance metrics, for example, net mean effective pressure (NMEP) and NOx emission. The goal of the present work was to develop and demonstrate a fully ‘online’ combustion system calibration method based on an NSGA, where the algorithm operates directly on experimental data rather than empirical models as is typical in the literature. This was completed by first designing an optimal NSGA for combustion system calibration and then demonstrating its use for an experimental combustion system calibration on a single cylinder gasoline engine at one operating condition.

Results from the design process here indicate that ‘online’ NSGAs have a strong potential to outperform traditional DoEs in the development of Pareto-optimal engine calibrations; however, NSGA performance is highly sensitive to the specific parameters used in the algorithm logic. The highest sensitivity was to the mutation logic within the genetic reproduction process, and second was the number of genes (calibrations) included in the overall process. Inclusion of both the Primary and Secondary non-dominated Pareto fronts in the set of Pareto-optimal calibrations was found to be critical to the success of the NSGA. When demonstrated for an experimental combustion system calibration the NSGA operated effectively and as expected, continually providing ‘upward’ pressure to generate calibrations that maximize NMEP but also ensure the breadth of the Pareto front (NOx) is scanned with high fidelity. In comparison to a traditional DoE approach, the NSGA was nearly twice as accurate in identifying calibrations along the Pareto front for the same number of total experiments. The Pareto-optimal calibrations developed by the NSGA are reasonable for these operating conditions and in excellent agreement with the literature. The present work strongly motivates and supports further development of NSGA methods for use in more complex systems and situations including for electrified and hybrid powertrains.