Dynamic modeling of a free-piston engine based on combustion parameters prediction

Leiming Chen, Zhaoping Xu, Shuangshuang Liu and Liang Liu

Energy, 2022, vol. 249, issue C

Abstract: A dynamic combustion model (DCM) is proposed for model-based control synthesis design in an opposed-piston free-piston engine (FPE). The DCM combines a modified triple Wiebe function with an artificial neural network (ANN), and considers the effects of engine variables such as cylinder wall temperature, fuel injection quantity and ignition timing. Firstly, it was proved by algebraic analysis that the triple Wiebe function can well describe the three combustion stages of the FPE. The ANN was then trained using the data obtained through CFD simulations at different operating points, with the aim of predicting combustion parameters based on engine variables. After the model was calibrated, it was verified by the test results from a prototype. Furthermore, this model was used to investigate combustion control strategies for cold start and stable operation. The results showed that the DCM has high precision and can simulate the complex combustion process under a wide range of FPE conditions, and is a favorable tool to make control strategies without experiments, especially in the cold start phase. Unlike general combustion modeling methods, this study provides a cost-effective way to build such a model for controller design.

Keywords: Free-piston engine; Dynamic combustion model; Artificial neural network; Wiebe function (search for similar items in EconPapers)
Date: 2022
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/S0360544222006958
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:energy:v:249:y:2022:i:c:s0360544222006958

DOI: 10.1016/j.energy.2022.123792

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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