Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control

Yusuf Lestanto; Rui Vressel Sugarcia; Aries Subiantoro; Abdul Halim

doi:10.1109/IoTaIS60147.2023.10346070

Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control

Yusuf Lestanto, Rui Vressel Sugarcia, Aries Subiantoro, Abdul Halim

Department of Electrical Engineering

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

Abstract

The energy management strategy (EMS) is a critical component of the design of hybrid electric vehicles. In this paper, a predictive EMS based on Nonlinear Model Predictive Control (NMPC) and power demand estimation approaches is proposed for the EMS of battery and diesel engine in a hybrid train. The Artificial Neural Network (ANN) method is introduced to realize model prediction of power demand. On this basis, the NMPC-based control framework is constructed, and a nonlinear programming approach with excellent global search ability and faster convergence is used to find the optimal control sequence in the receding horizon. Theoretical simulations are performed and the findings show that the total cost of EMS with implemented PWM as an AC/DC converter reduces diesel engine power consumption rather than using a conventional rectifier at 34.1%.

Original language	English
Title of host publication	Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	85-90
Number of pages	6
ISBN (Electronic)	9798350319040
DOIs	https://doi.org/10.1109/IoTaIS60147.2023.10346070
Publication status	Published - 2023
Event	2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023 - Hybrid, Bali, Indonesia Duration: 28 Nov 2023 → 30 Nov 2023

Publication series

Name	Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023

Conference

Conference	2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023
Country/Territory	Indonesia
City	Hybrid, Bali
Period	28/11/23 → 30/11/23

Keywords

DC link voltage
EMS
hybrid train
predictive control of nonlinear model
PWM rectifier

Access to Document

10.1109/IoTaIS60147.2023.10346070

Cite this

Lestanto, Y., Sugarcia, R. V., Subiantoro, A., & Halim, A. (2023). Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control. In Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023 (pp. 85-90). (Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IoTaIS60147.2023.10346070

Lestanto, Yusuf ; Sugarcia, Rui Vressel ; Subiantoro, Aries et al. / Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control. Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 85-90 (Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023).

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title = "Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control",

abstract = "The energy management strategy (EMS) is a critical component of the design of hybrid electric vehicles. In this paper, a predictive EMS based on Nonlinear Model Predictive Control (NMPC) and power demand estimation approaches is proposed for the EMS of battery and diesel engine in a hybrid train. The Artificial Neural Network (ANN) method is introduced to realize model prediction of power demand. On this basis, the NMPC-based control framework is constructed, and a nonlinear programming approach with excellent global search ability and faster convergence is used to find the optimal control sequence in the receding horizon. Theoretical simulations are performed and the findings show that the total cost of EMS with implemented PWM as an AC/DC converter reduces diesel engine power consumption rather than using a conventional rectifier at 34.1%.",

keywords = "DC link voltage, EMS, hybrid train, predictive control of nonlinear model, PWM rectifier",

author = "Yusuf Lestanto and Sugarcia, {Rui Vressel} and Aries Subiantoro and Abdul Halim",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023 ; Conference date: 28-11-2023 Through 30-11-2023",

year = "2023",

doi = "10.1109/IoTaIS60147.2023.10346070",

language = "English",

series = "Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023",

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

pages = "85--90",

booktitle = "Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023",

address = "United States",

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Lestanto, Y, Sugarcia, RV, Subiantoro, A & Halim, A 2023, Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control. in Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023. Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023, Institute of Electrical and Electronics Engineers Inc., pp. 85-90, 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023, Hybrid, Bali, Indonesia, 28/11/23. https://doi.org/10.1109/IoTaIS60147.2023.10346070

Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control. / Lestanto, Yusuf; Sugarcia, Rui Vressel; Subiantoro, Aries et al.
Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 85-90 (Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023).

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

TY - GEN

T1 - Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control

AU - Lestanto, Yusuf

AU - Sugarcia, Rui Vressel

AU - Subiantoro, Aries

AU - Halim, Abdul

PY - 2023

Y1 - 2023

N2 - The energy management strategy (EMS) is a critical component of the design of hybrid electric vehicles. In this paper, a predictive EMS based on Nonlinear Model Predictive Control (NMPC) and power demand estimation approaches is proposed for the EMS of battery and diesel engine in a hybrid train. The Artificial Neural Network (ANN) method is introduced to realize model prediction of power demand. On this basis, the NMPC-based control framework is constructed, and a nonlinear programming approach with excellent global search ability and faster convergence is used to find the optimal control sequence in the receding horizon. Theoretical simulations are performed and the findings show that the total cost of EMS with implemented PWM as an AC/DC converter reduces diesel engine power consumption rather than using a conventional rectifier at 34.1%.

AB - The energy management strategy (EMS) is a critical component of the design of hybrid electric vehicles. In this paper, a predictive EMS based on Nonlinear Model Predictive Control (NMPC) and power demand estimation approaches is proposed for the EMS of battery and diesel engine in a hybrid train. The Artificial Neural Network (ANN) method is introduced to realize model prediction of power demand. On this basis, the NMPC-based control framework is constructed, and a nonlinear programming approach with excellent global search ability and faster convergence is used to find the optimal control sequence in the receding horizon. Theoretical simulations are performed and the findings show that the total cost of EMS with implemented PWM as an AC/DC converter reduces diesel engine power consumption rather than using a conventional rectifier at 34.1%.

KW - DC link voltage

KW - EMS

KW - hybrid train

KW - predictive control of nonlinear model

KW - PWM rectifier

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M3 - Conference contribution

AN - SCOPUS:85182403072

T3 - Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023

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BT - Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023

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Lestanto Y, Sugarcia RV, Subiantoro A , Halim A. Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control. In Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 85-90. (Proceedings of 2023 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2023). doi: 10.1109/IoTaIS60147.2023.10346070

Energy Management Strategy of Hybrid Train Based on Nonlinear Model Predictive Control

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