IDEAS home Printed from https://ideas.repec.org/p/cdl/itsdav/qt77t9p8sf.html
   My bibliography  Save this paper

To Charge or Not to Charge: Enhancing Electric Vehicle Charging Management with LSTM-based Prediction of Non-Critical Charging Sessions and Renewable Energy Integration

Author

Listed:
  • Tayarani, Hanif
  • Nitta, Christopher J.
  • Tal, Gil

Abstract

To maximize the greenhouse gas (GHG) emission reduction potential of Battery Electric Vehicles (BEVs), it is critical to develop EV dynamic charging management strategies. These strategies leverage the temporal variability in emissions associated with generated electricity to align EV charging with periods of low-carbon power generation. This study introduces a deep neural network tool to enable BEV drivers to make charging sessions align with the availability of cleaner energy resources. This study leverages a Long Short-Term Memory network to forecast individual BEV vehicle miles traveled (VMT) up to two days ahead, using a year-long dataset of driving and charging patterns from 66 California-based BEVs. Based on the predicted VMT, the model then estimates the vehicle's energy needs and the necessity of a charging session. This allows drivers to charge theirvehicles strategically, prioritizing low-carbon electricity periods without risking incomplete journeys. This framework empowers drivers to actively contribute to cleaner electricity consumption with minimal disruption to their daily routines. The tool developed in this project outperforms benchmark models such as recurrent neural networks and autoregressive integrated moving averages, demonstrating its predictive capabilities. To enhance the reliability of predictions, confidence intervals are integrated into the model, ensuring that the model does not disrupt drivers' daily routine trips when skipping non-critical charging events. The potential benefits of the tool are demonstrated by applying it to real-world EV data, finding that if drivers follow the tool’s predictive suggestion, they can reduce overall GHG emissions by 41% without changing their driving patterns. This study also found that even charging in regions with higher carbon-intensity electricity than California can achieve Californian emission levels for EV charging in the short term through strategic management of non-critical charging events. This findingreveals new possibilities for further emissions reduction from EV charging, even before the full transition to a carbon-neutral grid. View the NCST Project Webpage

Suggested Citation

  • Tayarani, Hanif & Nitta, Christopher J. & Tal, Gil, 2024. "To Charge or Not to Charge: Enhancing Electric Vehicle Charging Management with LSTM-based Prediction of Non-Critical Charging Sessions and Renewable Energy Integration," Institute of Transportation Studies, Working Paper Series qt77t9p8sf, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt77t9p8sf
    as

    Download full text from publisher

    File URL: https://www.escholarship.org/uc/item/77t9p8sf.pdf;origin=repeccitec
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hoehne, Christopher G. & Chester, Mikhail V., 2016. "Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions," Energy, Elsevier, vol. 115(P1), pages 646-657.
    Full references (including those not matched with items on IDEAS)

    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. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    2. Brinkel, N.B.G. & Schram, W.L. & AlSkaif, T.A. & Lampropoulos, I. & van Sark, W.G.J.H.M., 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits," Applied Energy, Elsevier, vol. 276(C).
    3. Maxwell Woody & Michael T. Craig & Parth T. Vaishnav & Geoffrey M. Lewis & Gregory A. Keoleian, 2022. "Optimizing future cost and emissions of electric delivery vehicles," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 1108-1122, June.
    4. Powell, Siobhan & Martin, Sonia & Rajagopal, Ram & Azevedo, Inês M.L. & de Chalendar, Jacques, 2024. "Future-proof rates for controlled electric vehicle charging: Comparing multi-year impacts of different emission factor signals," Energy Policy, Elsevier, vol. 190(C).
    5. Zhang, Zhe & Yu, Qing & Gao, Kun & He, Hong-Di & Liu, Yang & Huang, Haichao, 2025. "Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure," Applied Energy, Elsevier, vol. 377(PA).
    6. Alicia Triviño & José M. González-González & José A. Aguado, 2021. "Wireless Power Transfer Technologies Applied to Electric Vehicles: A Review," Energies, MDPI, vol. 14(6), pages 1-21, March.
    7. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    8. Li, Shuangqi & Gu, Chenghong & Zeng, Xianwu & Zhao, Pengfei & Pei, Xiaoze & Cheng, Shuang, 2021. "Vehicle-to-grid management for multi-time scale grid power balancing," Energy, Elsevier, vol. 234(C).
    9. Al-Bahrani, Loau Tawfak & Horan, Ben & Seyedmahmoudian, Mehdi & Stojcevski, Alex, 2020. "Dynamic economic emission dispatch with load dema nd management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment," Energy, Elsevier, vol. 195(C).
    10. Zhou, Yuekuan & Zheng, Siqian & Hensen, Jan L.M., 2024. "Machine learning-based digital district heating/cooling with renewable integrations and advanced low-carbon transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    11. Gough, Rebecca & Dickerson, Charles & Rowley, Paul & Walsh, Chris, 2017. "Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage," Applied Energy, Elsevier, vol. 192(C), pages 12-23.
    12. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    13. Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).
    14. Badrinarayanan, Rajagopalan & Tseng, King Jet & Soong, Boon Hee & Wei, Zhongbao, 2017. "Modelling and control of vanadium redox flow battery for profile based charging applications," Energy, Elsevier, vol. 141(C), pages 1479-1488.
    15. Tan, Ruipeng & Lin, Boqiang, 2020. "Are people willing to support the construction of charging facilities in China?," Energy Policy, Elsevier, vol. 143(C).
    16. Cao, Kaiying & Guo, Qiang & Xu, Yuqiu, 2023. "Information sharing and carbon reduction strategies with extreme weather in the platform economy," International Journal of Production Economics, Elsevier, vol. 255(C).
    17. Yu, Zhenyu & Lu, Fei & Zou, Yu & Yang, Xudong, 2022. "Quantifying the real-time energy flexibility of commuter plug-in electric vehicles in an office building considering photovoltaic and load uncertainty," Applied Energy, Elsevier, vol. 321(C).
    18. Sharma, S. & Jain, Prerna, 2023. "Risk-averse integrated DR and dynamic V2G scheduling of parking lot operator for enhanced market efficiency," Energy, Elsevier, vol. 275(C).
    19. Zheng, Yanchong & Niu, Songyan & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2019. "Integrating plug-in electric vehicles into power grids: A comprehensive review on power interaction mode, scheduling methodology and mathematical foundation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 424-439.
    20. Chung, Cheng-Ta & Wu, Chien-Hsun & Hung, Yi-Hsuan, 2021. "A design methodology for selecting energy-efficient compound split e-CVT hybrid systems with planetary gearsets based on electric circulation," Energy, Elsevier, vol. 230(C).

    More about this item

    Keywords

    Engineering; Social and Behavioral Sciences; Electric vehicle charging; Energy consumption; Forecasting; Neural networks; Pollutants; Renewable energy sources;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:cdl:itsdav:qt77t9p8sf. 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: Lisa Schiff (email available below). General contact details of provider: https://edirc.repec.org/data/itucdus.html .

    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.