Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration

He MA, Ziyang LI, Mohamad TAYARANI, Guoxiang LU, Hongming XU, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

10 Citations (Scopus)

Abstract

For modern engines, the number of adjustable variables is increasing considerably. With an increase in the number of degrees of freedom and the consequent increase in the complexity of the calibration process, traditional design of experiments–based engine calibration methods are reaching their limits. As a result, an automated engine calibration approach is desired. In this paper, a model-based computational intelligence multi-objective optimization approach for gasoline direct injection engine calibration is developed, which can optimize the engine’s indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number simultaneously, by intelligently adjusting the engine actuators’ settings through Strength Pareto Evolutionary Algorithm 2. A mean-value model of gasoline direct injection engine is developed in the author’s earlier work and used to predict the performance of indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number with given value of intake valves opening timing, exhaust valves closing timing, spark timing, injection timing, and rail pressure. Then a co-simulation platform is established for the introduced intelligence engine calibration approach in the given engine operating condition. The co-simulation study and experimental validation results suggest that the developed intelligence calibration approach can find the optimal gasoline direct injection engine actuators’ settings with acceptable accuracy in much less time, compared to the traditional approach. © IMechE 2018.
Original languageEnglish
Pages (from-to)1391-1402
Number of pages12
JournalProceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Volume233
Issue number6
Early online date5 Jun 2018
DOIs
Publication statusPublished - May 2019
Externally publishedYes

Bibliographical note

The authors wish to acknowledge the financial support of EPSRC under EP/J00930X/1 ‘‘New Control Methodology for the Next Generation of Engine Management Systems’’ and technical support of Jaguar Land Rover by providing the test engine and relevant data. Shell provided all the testing fuels. They also wish to thank Peter Thornton and Carl Hingley in the Future Engines and Fuels Lab for providing technical support to the engine testing rig.

Keywords

  • Computational intelligence
  • gasoline direct injection engine calibration
  • multi-objective evolutionary algorithm

Fingerprint

Dive into the research topics of 'Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration'. Together they form a unique fingerprint.

Cite this