Model Predictive Control of the Air-conditioning System for Electric Bus

Mei Yan; Hongwen He; Hui Jia; Menglin Li; Xue Xue

doi:10.1016/j.egypro.2017.03.694

Model Predictive Control of the Air-conditioning System for Electric Bus

Mei Yan, Hongwen He^*, Hui Jia, Menglin Li, Xue Xue

^*Corresponding author for this work

School of Mechanical Engineering

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

13 Citations (Scopus)

Abstract

The varying amount of passengers that were carried on the electric bus is an obsessional disturbance for the cabin temperature control and energy management. This paper targets at evaluating the role of passenger number forecasting in the energy management of an electric bus based on model predictive control (MPC). Markov-chain model is used to forecast the future number of passengers, and thus provides references for the controller to achieve better energy economy. Dynamic Programming (DP) is used to optimize the power behaviors of the AC system within a MPC structure. Simulation results show that MPC used in the AC system can achieve a feasible and reliable temperature performance compared with the DP-solved benchmark. The energy consumption is improved by 8% compared with that of DP with a fixed number of passengers.

Original language	English
Pages (from-to)	2415-2421
Number of pages	7
Journal	Energy Procedia
Volume	105
DOIs	https://doi.org/10.1016/j.egypro.2017.03.694
Publication status	Published - 2017
Event	8th International Conference on Applied Energy, ICAE 2016 - Beijing, China Duration: 8 Oct 2016 → 11 Oct 2016

Keywords

AC system
Dynamic Programming
Electric bus
Passengers Variation
Stochastic

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.egypro.2017.03.694

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@article{a04866c032cf4fc4ac6ca78ca1a45a0b,

title = "Model Predictive Control of the Air-conditioning System for Electric Bus",

abstract = "The varying amount of passengers that were carried on the electric bus is an obsessional disturbance for the cabin temperature control and energy management. This paper targets at evaluating the role of passenger number forecasting in the energy management of an electric bus based on model predictive control (MPC). Markov-chain model is used to forecast the future number of passengers, and thus provides references for the controller to achieve better energy economy. Dynamic Programming (DP) is used to optimize the power behaviors of the AC system within a MPC structure. Simulation results show that MPC used in the AC system can achieve a feasible and reliable temperature performance compared with the DP-solved benchmark. The energy consumption is improved by 8% compared with that of DP with a fixed number of passengers.",

keywords = "AC system, Dynamic Programming, Electric bus, Passengers Variation, Stochastic",

author = "Mei Yan and Hongwen He and Hui Jia and Menglin Li and Xue Xue",

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

year = "2017",

doi = "10.1016/j.egypro.2017.03.694",

language = "English",

volume = "105",

pages = "2415--2421",

journal = "Energy Procedia",

issn = "1876-6102",

publisher = "Elsevier Ltd.",

}

TY - JOUR

T1 - Model Predictive Control of the Air-conditioning System for Electric Bus

AU - Yan, Mei

AU - He, Hongwen

AU - Jia, Hui

AU - Li, Menglin

AU - Xue, Xue

PY - 2017

Y1 - 2017

N2 - The varying amount of passengers that were carried on the electric bus is an obsessional disturbance for the cabin temperature control and energy management. This paper targets at evaluating the role of passenger number forecasting in the energy management of an electric bus based on model predictive control (MPC). Markov-chain model is used to forecast the future number of passengers, and thus provides references for the controller to achieve better energy economy. Dynamic Programming (DP) is used to optimize the power behaviors of the AC system within a MPC structure. Simulation results show that MPC used in the AC system can achieve a feasible and reliable temperature performance compared with the DP-solved benchmark. The energy consumption is improved by 8% compared with that of DP with a fixed number of passengers.

AB - The varying amount of passengers that were carried on the electric bus is an obsessional disturbance for the cabin temperature control and energy management. This paper targets at evaluating the role of passenger number forecasting in the energy management of an electric bus based on model predictive control (MPC). Markov-chain model is used to forecast the future number of passengers, and thus provides references for the controller to achieve better energy economy. Dynamic Programming (DP) is used to optimize the power behaviors of the AC system within a MPC structure. Simulation results show that MPC used in the AC system can achieve a feasible and reliable temperature performance compared with the DP-solved benchmark. The energy consumption is improved by 8% compared with that of DP with a fixed number of passengers.

KW - AC system

KW - Dynamic Programming

KW - Electric bus

KW - Passengers Variation

KW - Stochastic

UR - http://www.scopus.com/inward/record.url?scp=85020697947&partnerID=8YFLogxK

U2 - 10.1016/j.egypro.2017.03.694

DO - 10.1016/j.egypro.2017.03.694

M3 - Conference article

AN - SCOPUS:85020697947

SN - 1876-6102

VL - 105

SP - 2415

EP - 2421

JO - Energy Procedia

JF - Energy Procedia

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

Y2 - 8 October 2016 through 11 October 2016

ER -

Model Predictive Control of the Air-conditioning System for Electric Bus

Abstract

Keywords

UN SDGs

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