Plug-In Hybrid Electric Bus Energy Management Based on Stochastic Model Predictive Control

Shanshan Xie; Jiankun Peng; Hongwen He

doi:10.1016/j.egypro.2017.03.773

Plug-In Hybrid Electric Bus Energy Management Based on Stochastic Model Predictive Control

Shanshan Xie, Jiankun Peng, Hongwen He^*

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

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

14 Citations (Scopus)

Abstract

Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges.

Original language	English
Pages (from-to)	2672-2677
Number of pages	6
Journal	Energy Procedia
Volume	105
DOIs	https://doi.org/10.1016/j.egypro.2017.03.773
Publication status	Published - 2017
Event	8th International Conference on Applied Energy, ICAE 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Keywords

Energy management strategy
Hybrid electric bus
Markov chain
Model Prediction Control

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Cite this

@article{a1685762ba354b0b955da54e179be8a2,

title = "Plug-In Hybrid Electric Bus Energy Management Based on Stochastic Model Predictive Control",

abstract = "Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges.",

keywords = "Energy management strategy, Hybrid electric bus, Markov chain, Model Prediction Control",

author = "Shanshan Xie and Jiankun Peng and Hongwen He",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors. Published by Elsevier Ltd.; 8th International Conference on Applied Energy, ICAE 2016 ; Conference date: 08-10-2016 Through 11-10-2016",

year = "2017",

doi = "10.1016/j.egypro.2017.03.773",

language = "English",

volume = "105",

pages = "2672--2677",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier Ltd.",

}

TY - JOUR

T1 - Plug-In Hybrid Electric Bus Energy Management Based on Stochastic Model Predictive Control

AU - Xie, Shanshan

AU - Peng, Jiankun

AU - He, Hongwen

PY - 2017

Y1 - 2017

N2 - Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges.

AB - Energy management strategy is vital for a plug-in hybrid electric vehicle and in this paper, a strategy based on stochastic model predictive control is proposed. Firstly, Markov Chain Monte Carlo Simulation is used to predict velocity sequences in the 10-second horizon followed by post-processing like average filtering, quadratic fitting, etc. which is meant to moderate fluctuations of the results. The RMSE is controlled around 2.4357 Km/h. Moreover, dynamic programming is adopted to construct a benchmark strategy and also to act as the rolling optimization part of SMPC-based strategy. The results show that the fuel economy of the strategy based on SMPC is around 13 percent worse than that on DP. However, with 14.7 L/100 km as fuel consumption, it is still within reasonable ranges.

KW - Energy management strategy

KW - Hybrid electric bus

KW - Markov chain

KW - Model Prediction Control

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U2 - 10.1016/j.egypro.2017.03.773

DO - 10.1016/j.egypro.2017.03.773

M3 - Conference article

AN - SCOPUS:85020743930

SN - 1876-6102

VL - 105

SP - 2672

EP - 2677

JO - Energy Procedia

JF - Energy Procedia

T2 - 8th International Conference on Applied Energy, ICAE 2016

Y2 - 8 October 2016 through 11 October 2016

ER -

Plug-In Hybrid Electric Bus Energy Management Based on Stochastic Model Predictive Control

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Keywords

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