{"title":"Online Spark Timing Optimization With Complex High-Fidelity Combustion Phasing, Knock, and Coefficient of Variation of IMEP Models","authors":"Qilun Zhu, R. Prucka, Shu Wang, Michael J Prucka","doi":"10.1115/1.4049733","DOIUrl":null,"url":null,"abstract":"\n 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).","PeriodicalId":54846,"journal":{"name":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","volume":"5 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Dynamic Systems Measurement and Control-Transactions of the Asme","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1115/1.4049733","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
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).
期刊介绍:
The Journal of Dynamic Systems, Measurement, and Control publishes theoretical and applied original papers in the traditional areas implied by its name, as well as papers in interdisciplinary areas. Theoretical papers should present new theoretical developments and knowledge for controls of dynamical systems together with clear engineering motivation for the new theory. New theory or results that are only of mathematical interest without a clear engineering motivation or have a cursory relevance only are discouraged. "Application" is understood to include modeling, simulation of realistic systems, and corroboration of theory with emphasis on demonstrated practicality.