Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process

Zhilin Li; Yue Ma; Zhengchao Wei; Shumin Ruan

doi:10.1109/DDCLS55054.2022.9858507

Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process

Zhilin Li, Yue Ma^*, Zhengchao Wei, Shumin Ruan

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The modeling of the startup process of a turboshaft engine has always been a difficult task, which is further aggravated on a turboshaft engine in a hybrid-electric system. With sufficient data from bench experiments, this article establishes a data-driven numerical model to simulate the startup process of a hybrid-electric turboshaft engine. Neural networks are organized hierarchically to estimate two key parameters of the engine system: engine speed and exhaust temperature. The proposed model is built, trained, and tested in sections, and then tested as a whole. According to the running tests, it can give accurate results using a rather simple framework, showing great potential as a reference model on real-time control units.

Original language	English
Title of host publication	Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022
Editors	Mingxuan Sun, Zengqiang Chen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	959-963
Number of pages	5
ISBN (Electronic)	9781665496759
DOIs	https://doi.org/10.1109/DDCLS55054.2022.9858507
Publication status	Published - 2022
Event	11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022 - Emeishan, China Duration: 3 Aug 2022 → 5 Aug 2022

Publication series

Name	Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022

Conference

Conference	11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022
Country/Territory	China
City	Emeishan
Period	3/08/22 → 5/08/22

Keywords

Hybrid system
Neural network
Numerical model
Startup Process
Turboshaft engine

Access to Document

10.1109/DDCLS55054.2022.9858507

Cite this

Li, Z., Ma, Y., Wei, Z., & Ruan, S. (2022). Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process. In M. Sun, & Z. Chen (Eds.), Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022 (pp. 959-963). (Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS55054.2022.9858507

Li, Zhilin ; Ma, Yue ; Wei, Zhengchao et al. / Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process. Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022. editor / Mingxuan Sun ; Zengqiang Chen. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 959-963 (Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022).

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title = "Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process",

abstract = "The modeling of the startup process of a turboshaft engine has always been a difficult task, which is further aggravated on a turboshaft engine in a hybrid-electric system. With sufficient data from bench experiments, this article establishes a data-driven numerical model to simulate the startup process of a hybrid-electric turboshaft engine. Neural networks are organized hierarchically to estimate two key parameters of the engine system: engine speed and exhaust temperature. The proposed model is built, trained, and tested in sections, and then tested as a whole. According to the running tests, it can give accurate results using a rather simple framework, showing great potential as a reference model on real-time control units.",

keywords = "Hybrid system, Neural network, Numerical model, Startup Process, Turboshaft engine",

author = "Zhilin Li and Yue Ma and Zhengchao Wei and Shumin Ruan",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022 ; Conference date: 03-08-2022 Through 05-08-2022",

year = "2022",

doi = "10.1109/DDCLS55054.2022.9858507",

language = "English",

series = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "959--963",

editor = "Mingxuan Sun and Zengqiang Chen",

booktitle = "Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022",

address = "United States",

}

Li, Z, Ma, Y, Wei, Z & Ruan, S 2022, Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process. in M Sun & Z Chen (eds), Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022. Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022, Institute of Electrical and Electronics Engineers Inc., pp. 959-963, 11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022, Emeishan, China, 3/08/22. https://doi.org/10.1109/DDCLS55054.2022.9858507

Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process. / Li, Zhilin; Ma, Yue; Wei, Zhengchao et al.
Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022. ed. / Mingxuan Sun; Zengqiang Chen. Institute of Electrical and Electronics Engineers Inc., 2022. p. 959-963 (Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Ruan, Shumin

PY - 2022

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N2 - The modeling of the startup process of a turboshaft engine has always been a difficult task, which is further aggravated on a turboshaft engine in a hybrid-electric system. With sufficient data from bench experiments, this article establishes a data-driven numerical model to simulate the startup process of a hybrid-electric turboshaft engine. Neural networks are organized hierarchically to estimate two key parameters of the engine system: engine speed and exhaust temperature. The proposed model is built, trained, and tested in sections, and then tested as a whole. According to the running tests, it can give accurate results using a rather simple framework, showing great potential as a reference model on real-time control units.

AB - The modeling of the startup process of a turboshaft engine has always been a difficult task, which is further aggravated on a turboshaft engine in a hybrid-electric system. With sufficient data from bench experiments, this article establishes a data-driven numerical model to simulate the startup process of a hybrid-electric turboshaft engine. Neural networks are organized hierarchically to estimate two key parameters of the engine system: engine speed and exhaust temperature. The proposed model is built, trained, and tested in sections, and then tested as a whole. According to the running tests, it can give accurate results using a rather simple framework, showing great potential as a reference model on real-time control units.

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Li Z, Ma Y, Wei Z, Ruan S. Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process. In Sun M, Chen Z, editors, Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 959-963. (Proceedings of 2022 IEEE 11th Data Driven Control and Learning Systems Conference, DDCLS 2022). doi: 10.1109/DDCLS55054.2022.9858507

Open-loop NARX Based Modeling of a Hybrid-electric Turboshaft Engine's Startup Process

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