IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v290y2024ics0360544223035995.html
   My bibliography  Save this article

A hybrid on-line approach for predicting the energy consumption of electric buses based on vehicle dynamics and system identification

Author

Listed:
  • Pan, Yingjiu
  • Fang, Wenpeng
  • Ge, Zhenzhen
  • Li, Cheng
  • Wang, Caifeng
  • Guo, Baochang

Abstract

Energy consumption modeling for electric vehicles is conducive to achieving eco-driving optimization and alleviating driver ‘range anxiety’. However, it remains challenging due to the complexity of influencing factors. In this study, we propose a novel hybrid cumulative energy consumption (CEC) prediction approach by a combination of vehicle dynamics and the forgetting factor recursive least squares (FFRLS) to achieve real-time online forecasting accurately. The approach is developed with real-world data collected from over 983,000 frames across six electric buses in Guangzhou, China. To enhance the accuracy and practical application of the approach, we divide the data into segments as a unit of calculation for CEC. Subsequently, we constructed the model in three working conditions based on the vehicle longitudinal dynamics, and determined the parameters to be estimated for the model. With the help of the online learning capabilities of FFRLS algorithm, the estimated parameters can be constantly adjusted and updated according to the state of the buses. The results demonstrate that the mean absolute percentage error (MAPE) of the proposed method is 7.05 %. This performance surpasses that of existing relevant studies, indicating that the model provides a more accurate method for eco-driving and city-scale electric bus operation systems.

Suggested Citation

  • Pan, Yingjiu & Fang, Wenpeng & Ge, Zhenzhen & Li, Cheng & Wang, Caifeng & Guo, Baochang, 2024. "A hybrid on-line approach for predicting the energy consumption of electric buses based on vehicle dynamics and system identification," Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035995
    DOI: 10.1016/j.energy.2023.130205
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223035995
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.130205?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. Li, Li & Dong, Mi & Song, Dongran & Yang, Jian & Wang, Qibing, 2022. "Distributed and real-time economic dispatch strategy for an islanded microgrid with fair participation of thermostatically controlled loads," Energy, Elsevier, vol. 261(PB).
    2. Jiang, Junyu & Yu, Yuanbin & Min, Haitao & Cao, Qiming & Sun, Weiyi & Zhang, Zhaopu & Luo, Chunqi, 2023. "Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression," Energy, Elsevier, vol. 263(PD).
    3. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    4. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    5. David Jiménez & Sara Hernández & Jesús Fraile-Ardanuy & Javier Serrano & Rubén Fernández & Federico Álvarez, 2018. "Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones," Energies, MDPI, vol. 11(2), pages 1-23, February.
    6. Gallet, Marc & Massier, Tobias & Hamacher, Thomas, 2018. "Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks," Applied Energy, Elsevier, vol. 230(C), pages 344-356.
    7. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).
    8. Hao Wang & Yanping Zheng & Yang Yu, 2021. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter," Mathematics, MDPI, vol. 9(15), pages 1-12, July.
    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. Viana-Fons, Joan Dídac & Payá, Jorge, 2024. "HVAC system operation, consumption and compressor size optimization in urban buses of Mediterranean cities," Energy, Elsevier, vol. 296(C).
    2. Jiang, Junyu & Yu, Yuanbin & Min, Haitao & Cao, Qiming & Sun, Weiyi & Zhang, Zhaopu & Luo, Chunqi, 2023. "Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression," Energy, Elsevier, vol. 263(PD).
    3. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    4. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    5. Roman Michael Sennefelder & Rubén Martín-Clemente & Ramón González-Carvajal, 2023. "Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression," Energies, MDPI, vol. 16(11), pages 1-14, May.
    6. Basso, Franco & Feijoo, Felipe & Pezoa, Raúl & Varas, Mauricio & Vidal, Brian, 2024. "The impact of electromobility in public transport: An estimation of energy consumption using disaggregated data in Santiago, Chile," Energy, Elsevier, vol. 286(C).
    7. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    8. 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).
    9. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2020. "Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 30-46.
    10. Muhammed Alhanouti & Frank Gauterin, 2024. "A Generic Model for Accurate Energy Estimation of Electric Vehicles," Energies, MDPI, vol. 17(2), pages 1-21, January.
    11. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    12. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    13. Karla Schröder & Gonzalo Farias & Sebastián Dormido-Canto & Ernesto Fabregas, 2024. "Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution," Energies, MDPI, vol. 17(11), pages 1-13, June.
    14. Teresa Pamuła & Wiesław Pamuła, 2020. "Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning," Energies, MDPI, vol. 13(9), pages 1-17, May.
    15. Hatem Abdelaty & Moataz Mohamed, 2021. "A Prediction Model for Battery Electric Bus Energy Consumption in Transit," Energies, MDPI, vol. 14(10), pages 1-26, May.
    16. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).
    17. Cong Zhang & Haitao Min & Yuanbin Yu & Dai Wang & Justin Luke & Daniel Opila & Samveg Saxena, 2016. "Using CPE Function to Size Capacitor Storage for Electric Vehicles and Quantifying Battery Degradation during Different Driving Cycles," Energies, MDPI, vol. 9(11), pages 1-23, November.
    18. Ibrahim, Amier & Jiang, Fangming, 2021. "The electric vehicle energy management: An overview of the energy system and related modeling and simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    19. Bogdan Ovidiu Varga & Arsen Sagoian & Florin Mariasiu, 2019. "Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges," Energies, MDPI, vol. 12(5), pages 1-19, March.
    20. Hamza Mediouni & Amal Ezzouhri & Zakaria Charouh & Khadija El Harouri & Soumia El Hani & Mounir Ghogho, 2022. "Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach," Energies, MDPI, vol. 15(17), pages 1-17, September.

    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:290:y:2024:i:c:s0360544223035995. 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.