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An online energy consumption estimation method for different types of battery electric buses based on incremental learning

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  • Pan, Yingjiu
  • Fang, Wenpeng
  • Yan, Huimin
  • Zhang, Wenshan
  • Li, Yun

Abstract

Accurately estimating the real-time energy consumption (EC) of battery electric buses (BEBs) is critical for optimizing eco-driving strategies and energy management. Such estimations can significantly enhance the energy efficiency and economic viability of urban public transportation systems. However, research on online estimation models remains relatively limited. This study proposes a model employing incremental learning for real-time EC estimation, enabling continuous parameter updates to adapt to dynamic environments. Natural driving data collected from eight BEBs was analyzed to evaluate the influence of dynamic parameters on EC and to investigate variations in EC characteristics among different buses. Based on these insights, personalized input features were identified for the model, tailored to specific bus types using vehicle dynamics and correlation analysis. An online EC estimation model based on incremental learning was subsequently developed, and experiments were performed to optimize its structure and fine-tuning strategy. The results demonstrated an average mean absolute percentage error (MAPE) of 5.992%, validating the model's robustness across diverse conditions. These findings highlight the practical applicability of the proposed model for real-time EC estimation in BEBs, contributing to enhanced energy efficiency in public transportation systems.

Suggested Citation

  • Pan, Yingjiu & Fang, Wenpeng & Yan, Huimin & Zhang, Wenshan & Li, Yun, 2025. "An online energy consumption estimation method for different types of battery electric buses based on incremental learning," Energy, Elsevier, vol. 336(C).
    • Handle: RePEc:eee:energy:v:336:y:2025:i:c:s036054422504157x
    DOI: 10.1016/j.energy.2025.138515
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    References listed on IDEAS

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    1. Zhao, Li & Ke, Hanchen & Huo, Weiwei, 2023. "A frequency item mining based energy consumption prediction method for electric bus," Energy, Elsevier, vol. 263(PD).
    2. 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).
    3. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    4. Krystian Pietrzak & Oliwia Pietrzak, 2020. "Environmental Effects of Electromobility in a Sustainable Urban Public Transport," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
    5. Saadon Al-Ogaili, Ali & Ramasamy, Agileswari & Juhana Tengku Hashim, Tengku & Al-Masri, Ahmed N. & Hoon, Yap & Neamah Jebur, Mustafa & Verayiah, Renuga & Marsadek, Marayati, 2020. "Estimation of the energy consumption of battery driven electric buses by integrating digital elevation and longitudinal dynamic models: Malaysia as a case study," Applied Energy, Elsevier, vol. 280(C).
    6. Tang, Tianli & Gu, Ziyuan & Yang, Yuanxuan & Sun, Haobo & Chen, Siyuan & Chen, Yuting, 2024. "A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).
    7. Orji, Ifeyinwa Juliet, 2024. "Adopting electric bus for improving efficiency in the local public transport sector: Analysis of facilitating conditions and their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 180(C).
    8. Zhang, Jian & Tang, Tie-Qiao & Yan, Yadan & Qu, Xiaobo, 2021. "Eco-driving control for connected and automated electric vehicles at signalized intersections with wireless charging," Applied Energy, Elsevier, vol. 282(PA).
    9. 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.
    10. Yang, Ningkang & Ruan, Shumin & Han, Lijin & Liu, Hui & Guo, Lingxiong & Xiang, Changle, 2023. "Reinforcement learning-based real-time intelligent energy management for hybrid electric vehicles in a model predictive control framework," Energy, Elsevier, vol. 270(C).
    11. 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).
    12. Zhang, Zhaosheng & Wang, Shuai & Ye, Baolin & Ma, Yucheng, 2025. "A feature prediction-based method for energy consumption prediction of electric buses," Energy, Elsevier, vol. 314(C).
    13. 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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