IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i16p7349-d1724428.html
   My bibliography  Save this article

Co-Optimization of Charging Strategies and Route Planning for Variable-Ambient-Temperature Long-Haul Electric Vehicles Based on an Electrochemical–Vehicle Dynamics Model

Author

Listed:
  • Libin Zhang

    (School of Transportation, Jilin University, Changchun 130022, China)

  • Minghang Zhang

    (School of Transportation, Jilin University, Changchun 130022, China)

  • Hongying Shan

    (School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China)

  • Guan Xu

    (School of Transportation, Jilin University, Changchun 130022, China)

  • Jingsheng Dong

    (School of Transportation, Jilin University, Changchun 130022, China)

  • Xuemeng Bai

    (School of Transportation, Jilin University, Changchun 130022, China)

Abstract

Vehicle electrification is one of the main development directions within the automobile industry. However, due to the range limit of electric vehicles, electric vehicle users generally have range anxiety, especially toward long-haul driving. Therefore, there is an urgent need to effectively coordinate route planning and charging during long-haul driving, especially considering factors such as insufficient charging facilities, long charging times, battery aging, and changes in energy consumption under variable-temperature environments. In this study, the goal is to collaboratively optimize route planning and charging strategies. To achieve this goal, a mixed-integer nonlinear model is developed to minimize the total system cost, an electrochemical model is applied to accurately track the battery state, and a two-layer IACO-SA is proposed. Finally, the highway network in five provinces of China is adopted as an example to compare the optimal scheme results of our model with those of three other models. The comparison results prove the effectiveness of the proposed model and solution algorithm for the collaborative optimization of route planning and charging strategies of electric vehicles during long-haul driving.

Suggested Citation

  • Libin Zhang & Minghang Zhang & Hongying Shan & Guan Xu & Jingsheng Dong & Xuemeng Bai, 2025. "Co-Optimization of Charging Strategies and Route Planning for Variable-Ambient-Temperature Long-Haul Electric Vehicles Based on an Electrochemical–Vehicle Dynamics Model," Sustainability, MDPI, vol. 17(16), pages 1-27, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7349-:d:1724428
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/16/7349/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/16/7349/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Subramanian, Vignesh & Feijoo, Felipe & Sankaranarayanan, Sriram & Melendez, Kevin & Das, Tapas K., 2022. "A bilevel conic optimization model for routing and charging of EV fleets serving long distance delivery networks," Energy, Elsevier, vol. 251(C).
    3. Shi, Junzhe & Zeng, Teng & Moura, Scott, 2023. "Electric fleet charging management considering battery degradation and nonlinear charging profile," Energy, Elsevier, vol. 283(C).
    4. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
    5. Feifeng Zheng & Zhixin Wang & Zhaojie Wang & Ming Liu, 2023. "Daytime and Overnight Joint Charging Scheduling for Battery Electric Buses Considering Time-Varying Charging Power," Sustainability, MDPI, vol. 15(13), pages 1-19, July.
    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. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    2. Basso, Franco & Feijoo, Felipe & Pezoa, Raúl & Varas, Mauricio & Vidal, Brian, 2024. "The impact of electromobility in public transport: An estimation of energy consumption using disaggregated data in Santiago, Chile," Energy, Elsevier, vol. 286(C).
    3. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    4. Wang, Fu-Kwun & Kebede, Getnet Awoke & Lo, Shih-Che & Woldegiorgis, Bereket Haile, 2024. "An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction," Energy, Elsevier, vol. 308(C).
    5. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    6. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    7. Guangsen Wei & Weidong Chen & Nima Dongzhou, 2025. "RETRACTED ARTICLE: Enhancing Sustainable Development Through Sentiment Analysis of Public Digital Resources: A PSO-LSTM Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 581-600, March.
    8. Liu, Zixi & Ruan, Guanqiang & Tian, Yupeng & Hu, Xing & Yan, Rong & Yang, Kuo, 2024. "A real-world battery state of charge prediction method based on a lightweight mixer architecture," Energy, Elsevier, vol. 311(C).
    9. Wan, Sicheng & Yang, Haojing & Lin, Jinwen & Li, Junhui & Wang, Yibo & Chen, Xinman, 2024. "Improved whale optimization algorithm towards precise state-of-charge estimation of lithium-ion batteries via optimizing LSTM," Energy, Elsevier, vol. 310(C).
    10. Li, Zongxiang & Li, Liwei & Chen, Jing & Wang, Dongqing, 2024. "A multi-head attention mechanism aided hybrid network for identifying batteries’ state of charge," Energy, Elsevier, vol. 286(C).
    11. Ruan, Guanqiang & Liu, Zixi & Cheng, Jinrun & Hu, Xing & Chen, Song & Liu, Shiwen & Guo, Yong & Yang, Kuo, 2024. "A deep learning model for predicting the state of energy in lithium-ion batteries based on magnetic field effects," Energy, Elsevier, vol. 304(C).
    12. Simone Barcellona & Lorenzo Codecasa & Silvia Colnago & Luigi Piegari, 2023. "Calendar Aging Effect on the Open Circuit Voltage of Lithium-Ion Battery," Energies, MDPI, vol. 16(13), pages 1-16, June.
    13. Hu, Jingwei & Li, Xiaodong & Fang, Zheng & Cheng, Jun & Yi, Longqiang & Zhang, Zhihong, 2025. "Estimate state of charge in lithium-ion batteries with unknown data," Applied Energy, Elsevier, vol. 389(C).
    14. Li, Qianwen & Leng, Yunhan & Yao, Handong & Pei, Mingyang, 2024. "Assessment of transit bus electricity consumption using a random parameters approach," Energy, Elsevier, vol. 307(C).
    15. Dong, Changyin & Xiong, Zhuozhi & Li, Ni & Yu, Xinlian & Liang, Mingzhang & Zhang, Chu & Li, Ye & Wang, Hao, 2025. "A real-time prediction framework for energy consumption of electric buses using integrated Machine learning algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    16. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    17. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    18. Jiahui Zhao & Yong Zhu & Bin Zhang & Mingyi Liu & Jianxing Wang & Chenghao Liu & Xiaowei Hao, 2023. "Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    19. Zeng, Qingshun & Shi, Changfeng & Zhu, Wenjun & Zhi, Jiaqi & Na, Xiaohong, 2023. "Sequential data-driven carbon peaking path simulation research of the Yangtze River Delta urban agglomeration based on semantic mining and heuristic algorithm optimization," Energy, Elsevier, vol. 285(C).
    20. Li, Yichao & Ma, Chen & Liu, Kailong & Chang, Long & Zhang, Chenghui & Duan, Bin, 2024. "A novel joint estimation for core temperature and state of charge of lithium-ion battery based on classification approach and convolutional neural network," Energy, Elsevier, vol. 308(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:16:p:7349-:d:1724428. 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.