IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i9p4235-d1650914.html
   My bibliography  Save this article

Lane Change Trajectory Planning for Intelligent Electric Vehicles in Dynamic Traffic Environments: Aiming at Optimal Energy Consumption

Author

Listed:
  • Lin Hu

    (College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
    Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China)

  • Jie Wang

    (Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China)

  • Jing Huang

    (College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China)

  • Pak Kin Wong

    (Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau)

  • Jing Zhao

    (Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau)

Abstract

With the reduction in battery costs and the widespread application of artificial intelligence, the adoption of new-energy vehicles is accelerating. Integrating energy consumption optimization into the process of intelligent development is of great significance for sustainable development. This paper, considering the regenerative braking characteristics of electric vehicles and the time-varying nature of surrounding obstacle vehicles during lane changes, proposes a segmented real-time trajectory-planning method combining optimal control and quintic polynomials. At the beginning of the lane change, a safe intermediate position is calculated based on the speed and position information of the ego vehicle and the leading obstacle vehicle in the current lane. The trajectory optimization problem from the starting point to the intermediate position is formulated as an optimal control problem, resulting in the first segment of the trajectory. Upon reaching the intermediate position, the endpoint range is determined based on the speed and position information of the leading and trailing obstacle vehicles in the target lane. Multiple trajectories are then generated using quintic polynomials, and the optimal trajectory is selected as the second segment of the lane-changing trajectory. Experimental results from a driving simulator show that the proposed method can reduce energy consumption by approximately 40%.

Suggested Citation

  • Lin Hu & Jie Wang & Jing Huang & Pak Kin Wong & Jing Zhao, 2025. "Lane Change Trajectory Planning for Intelligent Electric Vehicles in Dynamic Traffic Environments: Aiming at Optimal Energy Consumption," Sustainability, MDPI, vol. 17(9), pages 1-28, May.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4235-:d:1650914
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/9/4235/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/9/4235/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    2. Nie, Zifei & Farzaneh, Hooman, 2023. "Energy-efficient lane-change motion planning for personalized autonomous driving," Applied Energy, Elsevier, vol. 338(C).
    3. 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.
    4. 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).
    5. Hu, Lin & Tian, Qingtao & Zou, Changfu & Huang, Jing & Ye, Yao & Wu, Xianhui, 2022. "A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    Full references (including those not matched with items on IDEAS)

    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. Torkey, Alaa & Abdelgawad, Hossam, 2022. "Framework for planning of EV charging infrastructure: Where should cities start?," Transport Policy, Elsevier, vol. 128(C), pages 193-208.
    2. 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.
    3. Zhang, Xinfang & Zhang, Zhe & Liu, Yang & Xu, Zhigang & Qu, Xiaobo, 2024. "A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation," Renewable Energy, Elsevier, vol. 234(C).
    4. 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).
    5. David Watling & Patrícia Baptista & Gonçalo Duarte & Jianbing Gao & Haibo Chen, 2022. "Systematic Method for Developing Reference Driving Cycles Appropriate to Electric L-Category Vehicles," Energies, MDPI, vol. 15(9), pages 1-28, May.
    6. Xue Lei & Hongyu Lu & Pengfei Fan & Rui Liu & Songsong Li & Yizheng Wu & Guohua Song, 2025. "Comparative Sensitivity Analyses of Energy Consumption in Response to Average Speed Between Electric Vehicles and Conventional Vehicles: Case Study in Beijing, China," Energies, MDPI, vol. 18(9), pages 1-23, April.
    7. Maksymilian Mądziel, 2025. "Impact of Weather Conditions on Energy Consumption Modeling for Electric Vehicles," Energies, MDPI, vol. 18(8), pages 1-21, April.
    8. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    9. Yashraj Tripathy & Andrew McGordon & Anup Barai, 2020. "Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-18, April.
    10. K. S. Reddy & S. Aravindhan & Tapas K. Mallick, 2017. "Techno-Economic Investigation of Solar Powered Electric Auto-Rickshaw for a Sustainable Transport System," Energies, MDPI, vol. 10(6), pages 1-15, May.
    11. Stefano De Pinto & Pablo Camocardi & Christoforos Chatzikomis & Aldo Sorniotti & Francesco Bottiglione & Giacomo Mantriota & Pietro Perlo, 2020. "On the Comparison of 2- and 4-Wheel-Drive Electric Vehicle Layouts with Central Motors and Single- and 2-Speed Transmission Systems," Energies, MDPI, vol. 13(13), pages 1-24, June.
    12. 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).
    13. Jin, Rui & Li, Lei & Liang, Xiaoling & Zou, Xiang & Yang, Zeyuan & Ge, Shuzhi Sam & Huang, Haihong, 2024. "Energy-efficient design of the powertrain for mechanical-electro-hydraulic equipment via configuring multidimensional controllable variables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 201(C).
    14. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    15. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    16. Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
    17. Parker, Nathan C. & Kuby, Michael & Liu, Jingteng & Stechel, Ellen B., 2025. "Extreme heat effects on electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 380(C).
    18. Soulios, V. & Loonen, R.C.G.M. & Metavitsiadis, V. & Hensen, J.L.M., 2018. "Computational performance analysis of overheating mitigation measures in parked vehicles," Applied Energy, Elsevier, vol. 231(C), pages 635-644.
    19. Li, Hai & Zheng, Peng & Zhang, Tingsheng & Zou, Yingquan & Pan, Yajia & Zhang, Zutao & Azam, Ali, 2021. "A high-efficiency energy regenerative shock absorber for powering auxiliary devices of new energy driverless buses," Applied Energy, Elsevier, vol. 295(C).
    20. Kapetanović, Marko & Núñez, Alfredo & van Oort, Niels & Goverde, Rob M.P., 2021. "Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains," Applied Energy, Elsevier, vol. 294(C).

    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:gam:jsusta:v:17:y:2025:i:9:p:4235-:d:1650914. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.