IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v197y2020ics0360544220300529.html
   My bibliography  Save this article

Optimal battery electric vehicles range: A study considering heterogeneous travel patterns, charging behaviors, and access to charging infrastructure

Author

Listed:
  • Zhou, Yue
  • Wen, Ruoxi
  • Wang, Hewu
  • Cai, Hua

Abstract

The choice of battery range (all-electric driving range) for battery electric vehicles (BEVs) is an important issue for both BEV adopters and BEV makers. This paper proposes a model to identify the minimum BEV battery range that can satisfy given travel demands, considering the opportunities to charge at existing public charging stations and the uncertainties in charging decision making. We conducted a stated preference survey to study the charging decision making and analyzed the data using the Latent Class model to generate the model coefficients for charging decisions making. The proposed approach can better identify the needed battery range than the often-used simplified charging rules. We applied the model to a case study of Beijing to evaluate the needed battery range for taxis and private vehicles. For taxis, BEVs with 220-mile battery range are able to satisfy the travel demands for about 90% of the drivers. For private vehicles, a 300-mile range is needed to cover the travel demands of 90% of the drivers, while a 100-mile range battery is able to satisfy the need for 80% of the private drivers. Simplified charging rules tend to underestimate the range needs for taxis but overestimate the range needs for private vehicles.

Suggested Citation

  • Zhou, Yue & Wen, Ruoxi & Wang, Hewu & Cai, Hua, 2020. "Optimal battery electric vehicles range: A study considering heterogeneous travel patterns, charging behaviors, and access to charging infrastructure," Energy, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220300529
    DOI: 10.1016/j.energy.2020.116945
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220300529
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.116945?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gao, Zhiming & Lin, Zhenhong & LaClair, Tim J. & Liu, Changzheng & Li, Jan-Mou & Birky, Alicia K. & Ward, Jacob, 2017. "Battery capacity and recharging needs for electric buses in city transit service," Energy, Elsevier, vol. 122(C), pages 588-600.
    2. Brett Williams & Elliot Martin & Timothy Lipman & Daniel Kammen, 2011. "Plug-in-Hybrid Vehicle Use, Energy Consumption, and Greenhouse Emissions: An Analysis of Household Vehicle Placements in Northern California," Energies, MDPI, vol. 4(3), pages 1-23, March.
    3. Xu, Min & Meng, Qiang & Liu, Kai & Yamamoto, Toshiyuki, 2017. "Joint charging mode and location choice model for battery electric vehicle users," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 68-86.
    4. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    5. Khan, Mobashwir & Kockelman, Kara M., 2012. "Predicting the market potential of plug-in electric vehicles using multiday GPS data," Energy Policy, Elsevier, vol. 46(C), pages 225-233.
    6. Björnsson, Lars-Henrik & Karlsson, Sten, 2015. "Plug-in hybrid electric vehicles: How individual movement patterns affect battery requirements, the potential to replace conventional fuels, and economic viability," Applied Energy, Elsevier, vol. 143(C), pages 336-347.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mandev, Ahmet & Plötz, Patrick & Sprei, Frances & Tal, Gil, 2022. "Empirical charging behavior of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    2. Bassem Haidar & Pascal da Costa & Jan Lepoutre & Fabrice Vidal, 2020. "Which combination of battery capacity and charging power for battery electric vehicles: urban versus rural French case studies," Post-Print hal-03071656, HAL.
    3. Ren, Yilong & Lan, Zhengxing & Yu, Haiyang & Jiao, Gangxin, 2022. "Analysis and prediction of charging behaviors for private battery electric vehicles with regular commuting: A case study in Beijing," Energy, Elsevier, vol. 253(C).
    4. Li, Jiapei & Xie, Chi, 2024. "Identifying and minimizing critical driving range thresholds for electric vehicles in intercity networks," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    5. Iwona Krzyżewska & Katarzyna Chruzik, 2023. "Maintenance and Exploitation of Electric, Hybrid, and Internal Combustion Vehicles," Energies, MDPI, vol. 16(23), pages 1-22, November.
    6. Zou, Pengyu & Zhang, Bin & Yi, Yi & Wang, Zhaohua, 2024. "How does travel satisfaction affect preference for shared electric vehicles? An empirical study using large-scale monitoring data and online text mining," Transport Policy, Elsevier, vol. 146(C), pages 59-71.
    7. Pareschi, Giacomo & Küng, Lukas & Georges, Gil & Boulouchos, Konstantinos, 2020. "Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data," Applied Energy, Elsevier, vol. 275(C).
    8. Subhaditya Shom & Kevin James & Mahmoud Alahmad, 2022. "Understanding the Correlation of Demographic Features with BEV Uptake at the Local Level in the United States," Sustainability, MDPI, vol. 14(9), pages 1-21, April.
    9. Aritra Ghosh, 2020. "Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review," Energies, MDPI, vol. 13(10), pages 1-22, May.
    10. Sprei, Frances & Kempton, Willett, 2024. "Mental models guide electric vehicle charging," Energy, Elsevier, vol. 292(C).
    11. Karol Tucki & Olga Orynycz & Mateusz Mitoraj-Wojtanek, 2020. "Perspectives for Mitigation of CO 2 Emission due to Development of Electromobility in Several Countries," Energies, MDPI, vol. 13(16), pages 1-24, August.
    12. Xiangyu Luo & Rui Qiu, 2020. "Electric Vehicle Charging Station Location towards Sustainable Cities," IJERPH, MDPI, vol. 17(8), pages 1-22, April.
    13. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
    14. Du, Zhili & Zheng, Lirong & Lin, Boqiang, 2024. "Influence of charging stations accessibility on charging stations utilization," Energy, Elsevier, vol. 298(C).
    15. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    16. Zhu, Xiaoxi & Chiong, Raymond & Wang, Miaomiao & Liu, Kai & Ren, Minglun, 2021. "Is carbon regulation better than cash subsidy? The case of new energy vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 146(C), pages 170-192.
    17. Veronika Štekerová & Martin Kotek & Veronika Hartová, 2020. "Comparison of two electric vehicles in terms of real range in different types of operations," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 66(4), pages 140-145.
    18. Aixin Yang & Guiqing Zhang & Chenlu Tian & Wei Peng & Yechun Liu, 2024. "Charging Behavior Portrait of Electric Vehicle Users Based on Fuzzy C-Means Clustering Algorithm," Energies, MDPI, vol. 17(7), pages 1-26, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Xiao & Pan, Jian & Wang, Hewu & Cai, Hua, 2019. "Battery electric vehicles: What is the minimum range required?," Energy, Elsevier, vol. 166(C), pages 352-358.
    2. Li, Jiapei & Xie, Chi, 2024. "Identifying and minimizing critical driving range thresholds for electric vehicles in intercity networks," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    3. Sodenkamp, Mariya & Wenig, Jürgen & Thiesse, Frédéric & Staake, Thorsten, 2019. "Who can drive electric? Segmentation of car drivers based on longitudinal GPS travel data," Energy Policy, Elsevier, vol. 130(C), pages 111-129.
    4. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
    5. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    6. Watanabe, Hajime & Maruyama, Takuya, 2023. "A Bayesian instrumental variable model for multinomial choice with correlated alternatives," Journal of choice modelling, Elsevier, vol. 46(C).
    7. Hong Gao & Kai Liu & Xinchao Peng & Cheng Li, 2020. "Optimal Location of Fast Charging Stations for Mixed Traffic of Electric Vehicles and Gasoline Vehicles Subject to Elastic Demands," Energies, MDPI, vol. 13(8), pages 1-16, April.
    8. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    9. Tamara L. Sheldon & J. R. DeShazo & Richard T. Carson, 2017. "Electric And Plug-In Hybrid Vehicle Demand: Lessons For An Emerging Market," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 695-713, April.
    10. Raka Jovanovic & Islam Safak Bayram & Sertac Bayhan & Stefan Voß, 2021. "A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems," Energies, MDPI, vol. 14(20), pages 1-23, October.
    11. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    12. Wang, Ying-Wei & Lin, Chuah-Chih, 2013. "Locating multiple types of recharging stations for battery-powered electric vehicle transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 58(C), pages 76-87.
    13. Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    14. Qi Liu & Jiahao Liu & Dunhu Liu, 2018. "Intelligent Multi-Objective Public Charging Station Location with Sustainable Objectives," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    15. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    16. Liu, Zhengxuan & Zhou, Yuekuan & Yan, Jun & Tostado-Véliz, Marcos, 2023. "Frontier ocean thermal/power and solar PV systems for transformation towards net-zero communities," Energy, Elsevier, vol. 284(C).
    17. Basma, Hussein & Haddad, Marc & Mansour, Charbel & Nemer, Maroun & Stabat, Pascal, 2022. "Evaluation of the techno-economic performance of battery electric buses: Case study of a bus line in paris," Research in Transportation Economics, Elsevier, vol. 95(C).
    18. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
    19. Zarazua de Rubens, Gerardo, 2019. "Who will buy electric vehicles after early adopters? Using machine learning to identify the electric vehicle mainstream market," Energy, Elsevier, vol. 172(C), pages 243-254.
    20. Yıldız, Barış & Olcaytu, Evren & Şen, Ahmet, 2019. "The urban recharging infrastructure design problem with stochastic demands and capacitated charging stations," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 22-44.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220300529. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.