Model predictive control for energy management of a plug-in hybrid electric bus

Hongwen He; Jieli Zhang; Gaopeng Li

doi:10.1016/j.egypro.2016.06.109

Model predictive control for energy management of a plug-in hybrid electric bus

Hongwen He^*, Jieli Zhang, Gaopeng Li

^*Corresponding author for this work

School of Mechanical Engineering

Research output: Contribution to journal › Conference article › peer-review

29 Citations (Scopus)

Abstract

In this paper, the model predictive control (MPC) method is researched for energy management problem of a plug-in hybrid electric vehicle (HEV). The multi-step Markov prediction method is used to predict the driving condition. Dynamic programming (DP) is used to solve the optimization problem within the prediction horizon. Through the comparison of MPC result with the results of DP strategy and a rule-based strategy, it is certified that the MPC strategy can be implemented to solve the optimization problem for PHEV with good fuel economy.

Original language	English
Pages (from-to)	901-907
Number of pages	7
Journal	Energy Procedia
Volume	88
DOIs	https://doi.org/10.1016/j.egypro.2016.06.109
Publication status	Published - 1 Jun 2016
Event	Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015 - Fuzhou, China Duration: 15 Nov 2015 → 17 Nov 2015

Keywords

Energy management strategy
Markov
Model predictive control
Plug-in hybrid electric vehicles

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10.1016/j.egypro.2016.06.109

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title = "Model predictive control for energy management of a plug-in hybrid electric bus",

abstract = "In this paper, the model predictive control (MPC) method is researched for energy management problem of a plug-in hybrid electric vehicle (HEV). The multi-step Markov prediction method is used to predict the driving condition. Dynamic programming (DP) is used to solve the optimization problem within the prediction horizon. Through the comparison of MPC result with the results of DP strategy and a rule-based strategy, it is certified that the MPC strategy can be implemented to solve the optimization problem for PHEV with good fuel economy.",

keywords = "Energy management strategy, Markov, Model predictive control, Plug-in hybrid electric vehicles",

author = "Hongwen He and Jieli Zhang and Gaopeng Li",

note = "Publisher Copyright: {\textcopyright} 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.; Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015 ; Conference date: 15-11-2015 Through 17-11-2015",

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T1 - Model predictive control for energy management of a plug-in hybrid electric bus

AU - He, Hongwen

AU - Zhang, Jieli

AU - Li, Gaopeng

PY - 2016/6/1

Y1 - 2016/6/1

N2 - In this paper, the model predictive control (MPC) method is researched for energy management problem of a plug-in hybrid electric vehicle (HEV). The multi-step Markov prediction method is used to predict the driving condition. Dynamic programming (DP) is used to solve the optimization problem within the prediction horizon. Through the comparison of MPC result with the results of DP strategy and a rule-based strategy, it is certified that the MPC strategy can be implemented to solve the optimization problem for PHEV with good fuel economy.

AB - In this paper, the model predictive control (MPC) method is researched for energy management problem of a plug-in hybrid electric vehicle (HEV). The multi-step Markov prediction method is used to predict the driving condition. Dynamic programming (DP) is used to solve the optimization problem within the prediction horizon. Through the comparison of MPC result with the results of DP strategy and a rule-based strategy, it is certified that the MPC strategy can be implemented to solve the optimization problem for PHEV with good fuel economy.

KW - Energy management strategy

KW - Markov

KW - Model predictive control

KW - Plug-in hybrid electric vehicles

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U2 - 10.1016/j.egypro.2016.06.109

DO - 10.1016/j.egypro.2016.06.109

M3 - Conference article

AN - SCOPUS:85007574078

SN - 1876-6102

VL - 88

SP - 901

EP - 907

JO - Energy Procedia

JF - Energy Procedia

T2 - Applied Energy Symposium and Summit on Low-Carbon Cities and Urban Energy Systems, CUE 2015

Y2 - 15 November 2015 through 17 November 2015

ER -

Model predictive control for energy management of a plug-in hybrid electric bus

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Keywords

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