Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion

Feifeng Zheng; Zhixin Wang; Yinfeng Xu; Ming Liu

doi:10.1007/978-3-030-85906-0_32

Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion

Feifeng Zheng
, Zhixin Wang
, Yinfeng Xu
, Ming Liu

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

2 Scopus citations

Abstract

In order to reduce carbon emissions, various government organizations have taken measures to promote the development of the electric vehicle industry. Motivated by scheduling practices, this work proposes a battery electric bus (BEB) charging scheduling problem. Different from the existing research on scheduling, the completion level of each task in this problem is controllable, because the charging level of BEB can be controlled. Besides, for saving charging cost, bus companies usually charge their battery electric buses (BEBs) at night. Because of the concentration of charging time, the decision-maker must make charging scheduling decision under a departure schedule to ensure charging with no delay. For solving this problem, we first establish a mixed integer linear programming model with the aim of minimising the total electricity cost and battery loss cost. Numerical experiments demonstrate the electiveness of the proposed model.

Original language	English
Title of host publication	Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings
Editors	Alexandre Dolgui, Alain Bernard, David Lemoine, Gregor von Cieminski, David Romero
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	282-289
Number of pages	8
ISBN (Print)	9783030859053
DOIs	https://doi.org/10.1007/978-3-030-85906-0_32
State	Published - 2021
Externally published	Yes
Event	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021 - Nantes, France Duration: 5 Sep 2021 → 9 Sep 2021

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	632 IFIP
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021
Country/Territory	France
City	Nantes
Period	5/09/21 → 9/09/21

Keywords

Battery loss cost
Bus charging
Optimization
Scheduling
State of charge

Access to Document

10.1007/978-3-030-85906-0_32

Cite this

Zheng, F., Wang, Z., Xu, Y., & Liu, M. (2021). Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion. In A. Dolgui, A. Bernard, D. Lemoine, G. von Cieminski, & D. Romero (Eds.), Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings (pp. 282-289). (IFIP Advances in Information and Communication Technology; Vol. 632 IFIP). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85906-0_32

Zheng, Feifeng ; Wang, Zhixin ; Xu, Yinfeng et al. / Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion. Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. editor / Alexandre Dolgui ; Alain Bernard ; David Lemoine ; Gregor von Cieminski ; David Romero. Springer Science and Business Media Deutschland GmbH, 2021. pp. 282-289 (IFIP Advances in Information and Communication Technology).

@inproceedings{2990b26841214c8985625d7fe69a8d08,

title = "Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion",

abstract = "In order to reduce carbon emissions, various government organizations have taken measures to promote the development of the electric vehicle industry. Motivated by scheduling practices, this work proposes a battery electric bus (BEB) charging scheduling problem. Different from the existing research on scheduling, the completion level of each task in this problem is controllable, because the charging level of BEB can be controlled. Besides, for saving charging cost, bus companies usually charge their battery electric buses (BEBs) at night. Because of the concentration of charging time, the decision-maker must make charging scheduling decision under a departure schedule to ensure charging with no delay. For solving this problem, we first establish a mixed integer linear programming model with the aim of minimising the total electricity cost and battery loss cost. Numerical experiments demonstrate the electiveness of the proposed model.",

keywords = "Battery loss cost, Bus charging, Optimization, Scheduling, State of charge",

author = "Feifeng Zheng and Zhixin Wang and Yinfeng Xu and Ming Liu",

note = "Publisher Copyright: {\textcopyright} 2021, IFIP International Federation for Information Processing.; IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021 ; Conference date: 05-09-2021 Through 09-09-2021",

year = "2021",

doi = "10.1007/978-3-030-85906-0\_32",

language = "英语",

isbn = "9783030859053",

series = "IFIP Advances in Information and Communication Technology",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "282--289",

editor = "Alexandre Dolgui and Alain Bernard and David Lemoine and \{von Cieminski\}, Gregor and David Romero",

booktitle = "Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings",

}

Zheng, F, Wang, Z, Xu, Y & Liu, M 2021, Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion. in A Dolgui, A Bernard, D Lemoine, G von Cieminski & D Romero (eds), Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. IFIP Advances in Information and Communication Technology, vol. 632 IFIP, Springer Science and Business Media Deutschland GmbH, pp. 282-289, IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021, Nantes, France, 5/09/21. https://doi.org/10.1007/978-3-030-85906-0_32

Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion. / Zheng, Feifeng; Wang, Zhixin; Xu, Yinfeng et al.
Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. ed. / Alexandre Dolgui; Alain Bernard; David Lemoine; Gregor von Cieminski; David Romero. Springer Science and Business Media Deutschland GmbH, 2021. p. 282-289 (IFIP Advances in Information and Communication Technology; Vol. 632 IFIP).

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

TY - GEN

T1 - Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion

AU - Zheng, Feifeng

AU - Wang, Zhixin

AU - Xu, Yinfeng

AU - Liu, Ming

PY - 2021

Y1 - 2021

N2 - In order to reduce carbon emissions, various government organizations have taken measures to promote the development of the electric vehicle industry. Motivated by scheduling practices, this work proposes a battery electric bus (BEB) charging scheduling problem. Different from the existing research on scheduling, the completion level of each task in this problem is controllable, because the charging level of BEB can be controlled. Besides, for saving charging cost, bus companies usually charge their battery electric buses (BEBs) at night. Because of the concentration of charging time, the decision-maker must make charging scheduling decision under a departure schedule to ensure charging with no delay. For solving this problem, we first establish a mixed integer linear programming model with the aim of minimising the total electricity cost and battery loss cost. Numerical experiments demonstrate the electiveness of the proposed model.

AB - In order to reduce carbon emissions, various government organizations have taken measures to promote the development of the electric vehicle industry. Motivated by scheduling practices, this work proposes a battery electric bus (BEB) charging scheduling problem. Different from the existing research on scheduling, the completion level of each task in this problem is controllable, because the charging level of BEB can be controlled. Besides, for saving charging cost, bus companies usually charge their battery electric buses (BEBs) at night. Because of the concentration of charging time, the decision-maker must make charging scheduling decision under a departure schedule to ensure charging with no delay. For solving this problem, we first establish a mixed integer linear programming model with the aim of minimising the total electricity cost and battery loss cost. Numerical experiments demonstrate the electiveness of the proposed model.

KW - Battery loss cost

KW - Bus charging

KW - Optimization

KW - Scheduling

KW - State of charge

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U2 - 10.1007/978-3-030-85906-0_32

DO - 10.1007/978-3-030-85906-0_32

M3 - 会议稿件

AN - SCOPUS:85115297123

SN - 9783030859053

T3 - IFIP Advances in Information and Communication Technology

SP - 282

EP - 289

BT - Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings

A2 - Dolgui, Alexandre

A2 - Bernard, Alain

A2 - Lemoine, David

A2 - von Cieminski, Gregor

A2 - Romero, David

PB - Springer Science and Business Media Deutschland GmbH

T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021

Y2 - 5 September 2021 through 9 September 2021

ER -

Zheng F, Wang Z, Xu Y, Liu M. Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion. In Dolgui A, Bernard A, Lemoine D, von Cieminski G, Romero D, editors, Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems - IFIP WG 5.7 International Conference, APMS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 282-289. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-030-85906-0_32

Charging Scheduling Optimization of Battery Electric Bus with Controllable Task Completion

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