Internal combustion engine calibration using optimization algorithms. (1st January 2022)

• Record Type: Journal Article
• Authors: Yu, Xunzhao; Zhu, Ling; Wang, Yan; Filev, Dimitar; Yao, Xin
• Is Part Of: Applied energy. Volume 305(2022)
• Publication Date: 2022-01-01
• Subjects: Engine calibration -- Fuel consumption -- Expensive optimization -- Multi-objective optimization -- Dynamic optimization -- Meta-heuristic algorithms -- Gradient-based algorithms; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117894
• Languages: English
• ISSNs: 0306-2619

Abstract

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. Highlights: We review studies of engine calibration problems from the perspective of optimization. Calibration problems are formulated mathematically with detailed descriptions. Model-based calibration is efficient for both off-board and on-board calibration. Meta-heuristic algorithms outperform gradient-based algorithms in most cases. Surrogate management strategies are desirable for surrogate-assisted calibration.