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

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  • 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
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    References listed on IDEAS

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    1. 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).
    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. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
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