Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles

Ningyuan Guo; Xudong Zhang; Yuan Zou; Tao Zhang; DIetmar Gohlich

doi:10.1109/VPPC46532.2019.8952217

Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles

Ningyuan Guo, Xudong Zhang, Yuan Zou, Tao Zhang, DIetmar Gohlich

School of Mechanical Engineering

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

2 Citations (Scopus)

Abstract

This paper proposes a computationally efficient energy management strategy of tracked hybrid electric vehicles (THEV) based on nonlinear model predictive control (NMPC). First, the powertrain of THEV is introduced in detailed. Then, the model predictive control problem is illustrated with series of constraints. To improve the computational efficiency in NMPC controller, a nonlinear programming method, continuation/ generalized minimum residual (C/GMRES) algorithm is adopted. Finally, numerical simulation validations are conducted and the in-depth analysis is also demonstrated, which yields the superior computational efficacy and control performance of the proposed strategy.

Original language	English
Title of host publication	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728112497
DOIs	https://doi.org/10.1109/VPPC46532.2019.8952217
Publication status	Published - Oct 2019
Event	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Hanoi, Viet Nam Duration: 14 Oct 2019 → 17 Oct 2019

Publication series

Name	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

Conference

Conference	2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019
Country/Territory	Viet Nam
City	Hanoi
Period	14/10/19 → 17/10/19

Keywords

Continuation/generalized minimum residual (C/GMRES)
Fuel economy
Nonlinear model predictive control (NMPC) algorithm
Tracked hybrid electric vehicle (THEV)

Access to Document

10.1109/VPPC46532.2019.8952217

Cite this

Guo, N., Zhang, X., Zou, Y., Zhang, T., & Gohlich, DI. (2019). Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles. In 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings Article 8952217 (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VPPC46532.2019.8952217

Guo, Ningyuan ; Zhang, Xudong ; Zou, Yuan et al. / Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles. 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings).

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title = "Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles",

abstract = "This paper proposes a computationally efficient energy management strategy of tracked hybrid electric vehicles (THEV) based on nonlinear model predictive control (NMPC). First, the powertrain of THEV is introduced in detailed. Then, the model predictive control problem is illustrated with series of constraints. To improve the computational efficiency in NMPC controller, a nonlinear programming method, continuation/ generalized minimum residual (C/GMRES) algorithm is adopted. Finally, numerical simulation validations are conducted and the in-depth analysis is also demonstrated, which yields the superior computational efficacy and control performance of the proposed strategy.",

keywords = "Continuation/generalized minimum residual (C/GMRES), Fuel economy, Nonlinear model predictive control (NMPC) algorithm, Tracked hybrid electric vehicle (THEV)",

author = "Ningyuan Guo and Xudong Zhang and Yuan Zou and Tao Zhang and DIetmar Gohlich",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 ; Conference date: 14-10-2019 Through 17-10-2019",

year = "2019",

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doi = "10.1109/VPPC46532.2019.8952217",

language = "English",

series = "2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings",

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

booktitle = "2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings",

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}

Guo, N, Zhang, X , Zou, Y, Zhang, T & Gohlich, DI 2019, Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles. in 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings., 8952217, 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019, Hanoi, Viet Nam, 14/10/19. https://doi.org/10.1109/VPPC46532.2019.8952217

Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles. / Guo, Ningyuan; Zhang, Xudong ; Zou, Yuan et al.
2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8952217 (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings).

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

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T1 - Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles

AU - Guo, Ningyuan

AU - Zhang, Xudong

AU - Zou, Yuan

AU - Zhang, Tao

AU - Gohlich, DIetmar

PY - 2019/10

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N2 - This paper proposes a computationally efficient energy management strategy of tracked hybrid electric vehicles (THEV) based on nonlinear model predictive control (NMPC). First, the powertrain of THEV is introduced in detailed. Then, the model predictive control problem is illustrated with series of constraints. To improve the computational efficiency in NMPC controller, a nonlinear programming method, continuation/ generalized minimum residual (C/GMRES) algorithm is adopted. Finally, numerical simulation validations are conducted and the in-depth analysis is also demonstrated, which yields the superior computational efficacy and control performance of the proposed strategy.

AB - This paper proposes a computationally efficient energy management strategy of tracked hybrid electric vehicles (THEV) based on nonlinear model predictive control (NMPC). First, the powertrain of THEV is introduced in detailed. Then, the model predictive control problem is illustrated with series of constraints. To improve the computational efficiency in NMPC controller, a nonlinear programming method, continuation/ generalized minimum residual (C/GMRES) algorithm is adopted. Finally, numerical simulation validations are conducted and the in-depth analysis is also demonstrated, which yields the superior computational efficacy and control performance of the proposed strategy.

KW - Continuation/generalized minimum residual (C/GMRES)

KW - Fuel economy

KW - Nonlinear model predictive control (NMPC) algorithm

KW - Tracked hybrid electric vehicle (THEV)

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DO - 10.1109/VPPC46532.2019.8952217

M3 - Conference contribution

AN - SCOPUS:85078735776

T3 - 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

BT - 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Guo N, Zhang X , Zou Y, Zhang T, Gohlich DI. Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles. In 2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8952217. (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings). doi: 10.1109/VPPC46532.2019.8952217

Computationally efficient nonlinear model predictive controller for energy management of tracked hybrid electric vehicles

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