A statistical combustion phase control approach of SI engines. (15th February 2017)

• Record Type: Journal Article
• Authors: Gao, Jinwu; Wu, Yuhu; Shen, Tielong
• Is Part Of: Mechanical systems and signal processing. Volume 85(2017)
• Page Start: 218
• Page End: 235
• Publication Date: 2017-02-15
• Subjects: Combustion phase indicator -- LPP -- Combustion phase control -- Hypothesis test criterion -- Spark advance adjustment; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.08.007
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: In order to maximize the performance of internal combustion engine, combustion phase is usually controlled to track its desired reference. However, suffering from the cyclic variability of combustion, it is difficulty but meaningful to control mean of combustion phase and constrain its variance. As a combustion phase indicator, the location of peak pressure (LPP) is utilized for real-time combustion phase control in this research. The purpose of the proposed method is to ensure the mean of LPP statistically tracks its reference and constrains the standard deviation of LPP distribution. To achieve this, LPP is first calculated based on the cylinder pressure sensor, and its characteristics are analyzed at the steady-state operating condition, then the distribution of LPP is examined online using hypothesis test criterion. On the basis of the presented statistical algorithm, current mean of LPP is applied in the feedback channel for designing spark advance adjustment law, and the stability of closed-loop system is theoretically ensured according to a steady statistical model. Finally, the proposed strategy is verified on a spark ignition gasoline engine. Abstract : Highlights: Using LPP as combustion phase indicator, a novel hypothesis test based controller is proposed to control combustion phase. The proposed approach can both control the mean of LPP and well constrain the standard deviation. The research has been validated and implemented on a 6-cylinder 3.5-l Toyota SI gasoline engine.