IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v261y2020ics0306261919321592.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919321592
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.114471?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. Wang, Jinghui & Rakha, Hesham A., 2016. "Fuel consumption model for conventional diesel buses," Applied Energy, Elsevier, vol. 170(C), pages 394-402.
    2. Cedric De Cauwer & Wouter Verbeke & Thierry Coosemans & Saphir Faid & Joeri Van Mierlo, 2017. "A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions," Energies, MDPI, vol. 10(5), pages 1-18, May.
    3. Li, Ji & Zhou, Quan & He, Yinglong & Shuai, Bin & Li, Ziyang & Williams, Huw & Xu, Hongming, 2019. "Dual-loop online intelligent programming for driver-oriented predict energy management of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Zhou, Quan & Li, Ji & Shuai, Bin & Williams, Huw & He, Yinglong & Li, Ziyang & Xu, Hongming & Yan, Fuwu, 2019. "Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle," Applied Energy, Elsevier, vol. 255(C).
    5. Siami-Irdemoosa, Elnaz & Dindarloo, Saeid R., 2015. "Prediction of fuel consumption of mining dump trucks: A neural networks approach," Applied Energy, Elsevier, vol. 151(C), pages 77-84.
    6. Khodabakhshian, Mohammad & Feng, Lei & Börjesson, Stefan & Lindgärde, Olof & Wikander, Jan, 2017. "Reducing auxiliary energy consumption of heavy trucks by onboard prediction and real-time optimization," Applied Energy, Elsevier, vol. 188(C), pages 652-671.
    7. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
    8. Sivak, Michael & Schoettle, Brandon, 2012. "Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy," Transport Policy, Elsevier, vol. 22(C), pages 96-99.
    9. Coifman, Benjamin & Li, Lizhe, 2017. "A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 362-377.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Quan & Li, Yanfei & Zhao, Dezong & Li, Ji & Williams, Huw & Xu, Hongming & Yan, Fuwu, 2022. "Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression," Applied Energy, Elsevier, vol. 305(C).
    2. 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).
    3. 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.
    4. 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).
    5. 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).
    6. 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).
    7. Nie, Zifei & Farzaneh, Hooman, 2023. "Energy-efficient lane-change motion planning for personalized autonomous driving," Applied Energy, Elsevier, vol. 338(C).

    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. 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).
    2. 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).
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    8. Muhammed Alhanouti & Frank Gauterin, 2024. "A Generic Model for Accurate Energy Estimation of Electric Vehicles," Energies, MDPI, vol. 17(2), pages 1-21, January.
    9. 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.
    10. 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.
    11. Li, Yangyang & Duan, Xiongbo & Fu, Jianqin & Liu, Jingping & Wang, Shuqian & Dong, Hao & Xie, Yunkun, 2019. "Development of a method for on-board measurement of instant engine torque and fuel consumption rate based on direct signal measurement and RGF modelling under vehicle transient operating conditions," Energy, Elsevier, vol. 189(C).
    12. Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2020. "A Method for the Optimization of Daily Activity Chains Including Electric Vehicles," Energies, MDPI, vol. 13(4), pages 1-21, February.
    13. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    14. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    15. Wang, Bin & Ma, Guangliang & Xu, Dan & Zhang, Le & Zhou, Jiahui, 2018. "Switching sliding-mode control strategy based on multi-type restrictive condition for voltage control of buck converter in auxiliary energy source," Applied Energy, Elsevier, vol. 228(C), pages 1373-1384.
    16. 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.
    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. 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).
    19. Huang, Yuhan & Ng, Elvin C.Y. & Zhou, John L. & Surawski, Nic C. & Chan, Edward F.C. & Hong, Guang, 2018. "Eco-driving technology for sustainable road transport: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 596-609.
    20. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.

    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:appene:v:261:y:2020:i:c:s0306261919321592. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.