Detection of transient low-temperature combustion characteristics by ion current – The missing link for homogeneous charge compression ignition control?

Jinqiu Wang, Julian Bedei, Jun Deng, Jakob Andert, Denghao Zhu and Liguang Li

Applied Energy, 2021, vol. 283, issue C, No S0306261920316858

Abstract: Homogeneous charge compression ignition is a promising low-temperature combustion mode because of its high efficiency and low emissions; however, its strong cycle-to-cycle coupling effect, which caused by the recirculation of exhaust gases, may entail problems with low combustion stability. In this study, a new concept that extracts more comprehensive combustion information in homogeneous charge compression ignition is proposed through the integration of ion current and in-cylinder pressure sensing. To analyze the correlations of combustion parameters and their relationships with the ion current parameters, steady-state measurements were conducted. Dynamic measurements were implemented to form a comprehensive database for artificial neural network training. To investigate the hypothesis that the ion current gives additional information beyond the pressure trace, black-box models based on experimental data are trained. The results show that the baseline model trained purely with the manipulated variables has the worst performance, while the model including both in-cylinder pressure and ion current derived parameters has the best predictability, with the overall root-mean-square error reduced by 2.5% in predicting combustion phasing, compared with in-cylinder pressure based model. It demonstrates that a significant improvement in model quality can be achieved by the combination of ion current and in-cylinder pressure sensing, which indicates that the ion current signal contains information that goes beyond a sole analysis of the pressure trace. By complementing the in-cylinder pressure, the use of the ion current as a “chemical sensor” for low-temperature combustion thus appears very promising for the stable control of homogeneous charge compression ignition combustion.

Keywords: Homogeneous charge compression ignition; Ion current; Artificial neural network; In-cylinder pressure analysis; In-cycle control; Black-box modeling (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920316858
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316858

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.116299

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().