Internal combustion engine calibration using optimization algorithms

Xunzhao YU, Ling ZHU, Yan WANG, Dimitar FILEV, Xin YAO

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

22 Citations (Scopus)

Abstract

Engine calibration is a process of optimizing engine settings so that optimal engine performance, like minimum fuel consumption, minimum pollutant gas emissions, maximum power output can be achieved. With the development of vehicle engine technology, modern engines contain more adjustable parameters than before, making the engine calibration task quite complicated and difficult. Also, the environmental problem caused by pollutant emissions attracted worldwide attention, leading to a strict requirement of engine performance. Therefore, some studies have been conducted to solve modern engine calibration problems. In this survey, we review the state-of-the-art applications of different optimization approaches in diverse internal combustion motor engines. Background of engine calibration problems and the problem formulations are given at the beginning, followed by an introduction to the structure of an engine cylinder and explanations of specialized terminologies for engine performance. The research on the calibration of three different types of internal combustion engines is reviewed, including gasoline engines, diesel engines, and hybrid-powered engines. For each engine type, the review covers the research on off-board engine calibration and on-board engine calibration. In the end, we summarize the optimization methodology and discuss current gaps and future work. © 2021
Original languageEnglish
Article number117894
JournalApplied Energy
Volume305
Early online date29 Sept 2021
DOIs
Publication statusPublished - Jan 2022
Externally publishedYes

Funding

This work was supported by Ford USA through a research grant to the University of Birmingham.

Keywords

  • Dynamic optimization
  • Engine calibration
  • Expensive optimization
  • Fuel consumption
  • Gradient-based algorithms
  • Meta-heuristic algorithms
  • Multi-objective optimization

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