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Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling

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  • Xing, Yang
  • Lv, Chen
  • Cao, Dongpu
  • Lu, Chao

Abstract

Analyzing the energy consumption for road entities and the corresponding driving behaviors are critical tasks for the realization of public traffic with a low energy cost and high efficiency. In this study, a personalized energy consumption analysis and prediction framework are proposed to estimate future energy consumption and the speed of a vehicle. An accumulation energy consumption index is predicted based on the features of the driving behavior. This approach is independent of the vehicle style, and it can play a critical role in the estimation of energy consumption as well as energy management for both petrol and electric vehicles. Three different energy-oriented driving behaviors are first identified and compared. It is shown that the vehicles with heavy energy usage have the characteristics of a higher speed, larger acceleration, larger headway space, and smaller headway time. The relationship between the energy consumptions and acceleration-deceleration characteristics are analyzed, and it is noted that the heavy energy users tend to perform acceleration maneuvers more frequently and with a longer period. Finally, a personalized joint time series modeling system based on the long short-term memory and a recurrent neural network is designed to jointly estimate the future energy consumption index considering different driving styles. It is found that the proposed personalized sequence prediction framework can generate more accurate results than the models that do not consider the energy cost levels and driving behaviors. The next-generation simulation data for free highway driving behaviors are used for the analysis and model evaluation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:261:y:2020:i:c:s0306261919321592
    DOI: 10.1016/j.apenergy.2019.114471
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    References listed on IDEAS

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    Cited by:

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    3. Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.
    4. Zepeng Gao & Jianbo Feng & Chao Wang & Yu Cao & Bonan Qin & Tao Zhang & Senqi Tan & Riya Zeng & Hongbin Ren & Tongxin Ma & Youshan Hou & Jie Xiao, 2022. "Research on Vehicle Active Steering Stability Control Based on Variable Time Domain Input and State Information Prediction," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    5. Amirgholy, Mahyar & Gao, H. Oliver, 2023. "Optimal traffic operation for maximum energy efficiency in signal-free urban networks: A macroscopic analytical approach," Applied Energy, Elsevier, vol. 329(C).
    6. Zhang, Yu & He, Yingying & Zhang, Likai, 2023. "Recognition method of abnormal driving behavior using the bidirectional gated recurrent unit and convolutional neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    7. Hou, Zhuoran & Guo, Jianhua & Li, Jihao & Hu, Jinchen & Sun, Wen & Zhang, Yuanjian, 2023. "Exploration the pathways of connected electric vehicle design: A vehicle-environment cooperation energy management strategy," Energy, Elsevier, vol. 271(C).
    8. Nie, Zifei & Farzaneh, Hooman, 2023. "Energy-efficient lane-change motion planning for personalized autonomous driving," Applied Energy, Elsevier, vol. 338(C).

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