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Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression

Author

Listed:
  • Jiang, Junyu
  • Yu, Yuanbin
  • Min, Haitao
  • Cao, Qiming
  • Sun, Weiyi
  • Zhang, Zhaopu
  • Luo, Chunqi

Abstract

Electric bus energy consumption prediction is conducive to bus route scheduling and charging optimization management. However, existing prediction models do not comprehensively consider vehicle parameters, auxiliary power, speed profile and external factors. In particular, there is a lack of in-depth research on generating speed profile based on low-resolution data for electric bus. The aim of this study involves predicting bus trip-level energy consumption. Thus, the temperature effect on powertrain efficiency and auxiliary power was first analyzed, and then a Markov-based speed profile generation method considering the speed state transition spatiotemporal characteristics and driving style was developed. Reliable process-related variables extracted from the aforementioned model development in conjunction with external-related variables are used as the input of Markov-based Gaussian processing regression model (M-GPR). Finally, 7919 regular bus driving samples on the same route were extracted from the raw bus operation dataset to verify the model effectiveness in improving the prediction accuracy of trip-level energy consumption. The results indicate that M-GPR exhibits a better prediction performance with an average prediction error of 8.69%, which decreases by 0.62%–5.41% compared with other models. Furthermore, M-GPR presents wide applicability, with average errors between 7.01% and 9.90% in different operating scenarios.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222027529
    DOI: 10.1016/j.energy.2022.125866
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    References listed on IDEAS

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    1. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    2. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2022. "Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle," Energy, Elsevier, vol. 238(PC).
    3. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    4. Wei Qin & Linhong Wang & Yuhan Liu & Cheng Xu, 2021. "Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    5. Yuan, Xinmei & Zhang, Chuanpu & Hong, Guokai & Huang, Xueqi & Li, Lili, 2017. "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, Elsevier, vol. 141(C), pages 1955-1968.
    6. Li, Pengshun & Zhang, Yi & Zhang, Yi & Zhang, Kai & Jiang, Mengyan, 2021. "The effects of dynamic traffic conditions, route characteristics and environmental conditions on trip-based electricity consumption prediction of electric bus," Energy, Elsevier, vol. 218(C).
    7. Zhou, Boya & Wu, Ye & Zhou, Bin & Wang, Renjie & Ke, Wenwei & Zhang, Shaojun & Hao, Jiming, 2016. "Real-world performance of battery electric buses and their life-cycle benefits with respect to energy consumption and carbon dioxide emissions," Energy, Elsevier, vol. 96(C), pages 603-613.
    8. Ma, Xiaolei & Miao, Ran & Wu, Xinkai & Liu, Xianglong, 2021. "Examining influential factors on the energy consumption of electric and diesel buses: A data-driven analysis of large-scale public transit network in Beijing," Energy, Elsevier, vol. 216(C).
    9. Li, Weihan & Fan, Yue & Ringbeck, Florian & Jöst, Dominik & Sauer, Dirk Uwe, 2022. "Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression," Applied Energy, Elsevier, vol. 306(PB).
    10. Vepsäläinen, Jari & Otto, Kevin & Lajunen, Antti & Tammi, Kari, 2019. "Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions," Energy, Elsevier, vol. 169(C), pages 433-443.
    11. 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.
    12. 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.
    13. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2021. "Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving," Applied Energy, Elsevier, vol. 297(C).
    14. Mahmoud, Moataz & Garnett, Ryan & Ferguson, Mark & Kanaroglou, Pavlos, 2016. "Electric buses: A review of alternative powertrains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 673-684.
    15. 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.
    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. 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).
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