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Journal of Intelligent and Connected Vehicles 2022, 5(1): 8-16 https://doi.org/10.1108/JICV-07-2021-0009
Research paper | Open Access | Issue | Published: 24 January 2022

A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Show Author's Information Le Zhang1( ),  Ziling Zeng2,  Kun Gao2
School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden
Keywords:
Battery electric bus, Bi-level model, Charging station design, Vehicle scheduling, Heterogeneous charging modes
Cite this article:
Zhang L, Zeng Z, Gao K. A bi-level optimization framework for charging station design problem considering heterogeneous charging modes. Journal of Intelligent and Connected Vehicles, 2022, 5(1): 8-16. https://doi.org/10.1108/JICV-07-2021-0009
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Abstract Full text About this article
Purpose

The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes.

Design/methodology/approach

The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model.

Findings

This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically.

Originality/value

The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses.


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Abstract
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About this article

A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Show Author's information Le Zhang1( ), Ziling Zeng2, Kun Gao2
School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China
Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden

Abstract

Purpose

The purpose of this paper is to optimize the design of charging station deployed at the terminal station for electric transit, with explicit consideration of heterogenous charging modes.

Design/methodology/approach

The authors proposed a bi-level model to optimize the decision-making at both tactical and operational levels simultaneously. Specifically, at the operational level (i.e. lower level), the service schedule and recharging plan of electric buses are optimized under specific design of charging station. The objective of lower-level model is to minimize total daily operational cost. This model is solved by a tailored column generation-based heuristic algorithm. At the tactical level (i.e. upper level), the design of charging station is optimized based upon the results obtained at the lower level. A tabu search algorithm is proposed subsequently to solve the upper-level model.

Findings

This study conducted numerical cases to validate the applicability of the proposed model. Some managerial insights stemmed from numerical case studies are revealed and discussed, which can help transit agencies design charging station scientifically.

Originality/value

The joint consideration of heterogeneous charging modes in charging station would further lower the operational cost of electric transit and speed up the market penetration of battery electric buses.

Keywords: Battery electric bus, Bi-level model, Charging station design, Vehicle scheduling, Heterogeneous charging modes

References(38)

Adler, J.D. (2014), “Routing and scheduling of electric and alternative-fuel vehicles”, Doctoral dissertation, Arizona State University.

An, K. (2020), “Battery electric bus infrastructure planning under demand uncertainty”, Transportation Research Part C: Emerging Technologies, Vol. 111, pp. 572-587.
DOI Google Scholar

Bie, Y., Xiong, X., Yan, Y. and Qu, X. (2020), “Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment”, Computer-Aided Civil and Infrastructure Engineering, Vol. 35 No. 1, pp. 4-25.
DOI Google Scholar

Bunte, S. and Kliewer, N. (2009), “An overview on vehicle scheduling models”, Public Transport, Vol. 1 No. 4, pp. 299-317.
DOI Google Scholar

Ceder, A.A. (2011), “Public-transport vehicle scheduling with multi vehicle type”, Transportation Research Part C: Emerging Technologies, Vol. 19 No. 3, pp. 485-497.
DOI Google Scholar

Dell'Amico, M., Fischetti, M. and Toth, P. (1993), “Heuristic algorithms for the multiple depot vehicle scheduling problem”, Management Science, Vol. 39 No. 1, pp. 115-125.
DOI Google Scholar

Freling, R., Wagelmans, A.P. and Paixão, J.M.P. (2001), “Models and algorithms for single-depot vehicle scheduling”, Transportation Science, Vol. 35 No. 2, pp. 165-180.
DOI Google Scholar

Gao, K., Yang, Y., Li, A., Li, J. and Yu, B. (2021), “Quantifying economic benefits from free-floating bike-sharing systems: a trip-level inference approach and city-scale analysis”, Transportation Research Part A: Policy and Practice, Vol. 144, pp. 89-103.
DOI Google Scholar

Gao, K., Yang, Y., Sun, L. and Qu, X. (2020), “Revealing psychological inertia in mode shift behavior and its quantitative influences on commuting trips”, Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 71, pp. 272-287.
DOI Google Scholar

Haghani, A. and Banihashemi, M. (2002), “Heuristic approaches for solving large-scale bus transit vehicle scheduling problem with route time constraints”, Transportation Research Part A: Policy and Practice, Vol. 36 No. 4, pp. 309-333.
DOI Google Scholar

Huang, Y. and Zhou, Y. (2015), “An optimization framework for workplace charging strategies”, Transportation Research Part C: Emerging Technologies, Vol. 52, pp. 144-155.
DOI Google Scholar

Jin, S., Qu, X., Zhou, D., Xu, C., Ma, D. and Wang, D. (2015), “Estimating cycleway capacity and bicycle equivalent unit for electric bicycles”, Transportation Research Part A: Policy and Practice, Vol. 77, pp. 225-248.
DOI Google Scholar

Kang, L., Chen, S. and Meng, Q. (2019), “Bus and driver scheduling with mealtime windows for a single public bus route”, Transportation Research Part C: Emerging Technologies, Vol. 101, pp. 145-160.
DOI Google Scholar

Kliewer, N., Mellouli, T. and Suhl, L. (2006), “A time–space network based exact optimization model for multi-depot bus scheduling”, European Journal of Operational Research, Vol. 175 No. 3, pp. 1616-1627.
DOI Google Scholar

Lajunen, A. (2014), “Energy consumption and cost-benefit analysis of hybrid and electric city buses”, Transportation Research Part C: Emerging Technologies, Vol. 38, pp. 1-15.
DOI Google Scholar

Lam, L. and Bauer, P. (2012), “Practical capacity fading model for li-ion battery cells in electric vehicles”, IEEE Transactions on Power Electronics, Vol. 28 No. 12, pp. 5910-5918.
DOI Google Scholar

Lebeau, P., Macharis, C. and Van Mierlo, J. (2016), “Exploring the choice of battery electric vehicles in city logistics: a conjoint-based choice analysis”, Transportation Research Part E: Logistics and Transportation Review, Vol. 91, pp. 245-258.
DOI Google Scholar

Li, J.Q. (2013), “Transit bus scheduling with limited energy”, Transportation Science, Vol. 48 No. 4, pp. 521-539.
DOI Google Scholar

Li, L., Lo, H.K. and Xiao, F. (2019), “Mixed bus fleet scheduling under range and refueling constraints”, Transportation Research Part C: Emerging Technologies, Vol. 104, pp. 443-462.
DOI Google Scholar

Liu, T. and Ceder, A.A. (2020), “Battery-electric transit vehicle scheduling with optimal number of stationary chargers”, Transportation Research Part C: Emerging Technologies, Vol. 114, pp. 118-139.
DOI Google Scholar

Mahmoud, M., Garnett, R., Ferguson, M. and Kanaroglou, P. (2016), “Electric buses: a review of alternative powertrains”, Renewable and Sustainable Energy Reviews, Vol. 62, pp. 673-684.
DOI Google Scholar

Marković, N., Nair, R., Schonfeld, P., Miller-Hooks, E. and Mohebbi, M. (2015), “Optimizing dial-a-ride services in Maryland: benefits of computerized routing and scheduling”, Transportation Research Part C: Emerging Technologies, Vol. 55, pp. 156-165.
DOI Google Scholar

Masmoudi, M.A., Hosny, M., Demir, E., Genikomsakis, K.N. and Cheikhrouhou, N. (2018), “The dial-a-ride problem with electric vehicles and battery swapping stations”, Transportation Research Part E: Logistics and Transportation Review, Vol. 118, pp. 392-420.
DOI Google Scholar

Meng, Q. and Qu, X. (2013), “Bus dwell time estimation at a bus bay: a probabilistic approach”, Transportation Research Part C: Emerging Technologies, Vol. 36, pp. 61-71.
DOI Google Scholar

Merle, O.D., Villeneuve, D., Desrosiers, J. and Hansen, P. (1999), “Stabilized column generation”, Discrete Mathematics, Vol. 194 Nos 1/3, pp. 229-237.
DOI Google Scholar
Neff, J. and Dickens, M. (2016), 2016 Public Transportation Fact Book, American Public Transportation Association, Washington, DC.

Paixão, J.P. and Branco, I.M. (1987), “A quasi‐assignment algorithm for bus scheduling”, Networks, Vol. 17 No. 3, pp. 249-269.
DOI Google Scholar

Qin, N., Gusrialdi, A., Brooker, R.P. and Ali, T. (2016), “Numerical analysis of electric bus fast charging strategies for demand charge reduction”, Transportation Research Part A: Policy and Practice, Vol. 94, pp. 386-396.
DOI Google Scholar

Qu, X., Yu, Y., Zhou, M., Lin, C.T. and Wang, X. (2020), “Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: a reinforcement learning based approach”, Applied Energy, Vol. 257, p. 114030.
DOI Google Scholar

Rinaldi, M., Picarelli, E., D'Ariano, A. and Viti, F. (2020), “Mixed-fleet single-terminal bus scheduling problem: modelling, solution scheme and potential applications”, Omega, Vol. 96, p. 102070.
DOI Google Scholar

Schöbel, A. (2017), “An eigenmodel for iterative line planning, timetabling and vehicle scheduling in public transportation”, Transportation Research Part C: Emerging Technologies, Vol. 74, pp. 348-365.
DOI Google Scholar

Tang, X., Lin, X. and He, F. (2019), “Robust scheduling strategies of electric buses under stochastic traffic conditions”, Transportation Research Part C: Emerging Technologies, Vol. 105, pp. 163-182.
DOI Google Scholar

Wang, Y., Huang, Y., Xu, J. and Barclay, N. (2017), “Optimal recharging scheduling for urban electric buses: a case study in Davis”, Transportation Research Part E: Logistics and Transportation Review, Vol. 100, pp. 115-132.
DOI Google Scholar

Wang, S., Zhang, W. and Qu, X. (2018), “Trial-and-error train fare design scheme for addressing boarding/alighting congestion at CBD stations”, Transportation Research Part B: Methodological, Vol. 118, pp. 318-335.
DOI Google Scholar

Wen, M., Linde, E., Ropke, S., Mirchandani, P. and Larsen, A. (2016), “An adaptive large neighborhood search heuristic for the electric vehicle scheduling problem”, Computers & Operations Research, Vol. 76, pp. 73-83.
DOI Google Scholar

Xu, Y., Zheng, Y. and Yang, Y. (2021), “On the movement simulations of electric vehicles: a behavioral model-based approach”, Applied Energy, Vol. 283, p. 116356.
DOI Google Scholar

Zhang, L., Wang, S. and Qu, X. (2021), “Optimal electric bus fleet scheduling considering battery degradation and nonlinear charging profile”, Transportation Research Part E: Logistics and Transportation Review, Vol. 154, p. 102445.
DOI Google Scholar

Zhang, L., Zeng, Z. and Qu, X. (2020), “On the role of battery capacity fading mechanism in the lifecycle cost of electric bus fleet”, IEEE Transactions on Intelligent Transportation Systems, Vol. 22 No. 4, doi: 10.1109/TITS.2020.3014097.
DOI Google Scholar
Publication history
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Publication history

Received: 08 July 2021
Revised: 05 December 2021
Accepted: 16 December 2021
Published: 24 January 2022
Issue date: February 2022

Copyright

© 2022 Le Zhang, Ziling Zeng and Kun Gao. Published in Journal of Intelligent and Connected Vehicles. Published by Emerald Publishing Limited.

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This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

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