Volume 24 Issue 6
Dec.  2024
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MA Jun-chi, ZHANG Yuan, DUAN Zong-tao, TANG Lei. Research review on behavior strategies of electric vehicles considering charging demands[J]. Journal of Traffic and Transportation Engineering, 2024, 24(6): 66-79. doi: 10.19818/j.cnki.1671-1637.2024.06.004
Citation: MA Jun-chi, ZHANG Yuan, DUAN Zong-tao, TANG Lei. Research review on behavior strategies of electric vehicles considering charging demands[J]. Journal of Traffic and Transportation Engineering, 2024, 24(6): 66-79. doi: 10.19818/j.cnki.1671-1637.2024.06.004
shu

Research review on behavior strategies of electric vehicles considering charging demands

doi: 10.19818/j.cnki.1671-1637.2024.06.004

  • School of Information Engineering, Chang'an University, Xi'an 710018, Shaanxi, China

Funds:

National Natural Science Foundation of China 62002030

Key Research and Development Program of Shaanxi Province 2020GY-013

Key Research and Development Program of Shaanxi Province 2019GY-006

Key Research and Development Program of Shaanxi Province 2019ZDLGY17-08

Key Research and Development Program of Shaanxi Province 2019ZDLGY03-09-01

National Key Research and Development Program of China 2021YFB2501203

Fundamental Research Funds for the Central Universities 300102414608

More Information
  • Author Bio:

    MA Jun-chi(1988-), male, associate professor, PhD, majunchi@chd.edu.cn

  • Received Date: 2024-07-13
  • Publish Date: 2024-12-25
    Abstract
  • In order to improve the usability and operational efficiency of electric vehicles, the research progress of related work was described from three perspectives: charging behavior strategies (including charging station recommendation and charging path planning), behavior strategies for passenger service (including ride-sharing and car rental scenarios), and behavior strategies under vehicle-to-grid (V2G) interactions. The principles and applications of artificial intelligence technology were summarized, and the future research directions were explored. Research results show that the research on charging station recommendation focuses on two optimization objectives: time overhead and charging fee. Heuristic algorithm or reinforcement learning algorithm is often applied to obtain the optimal charging station. Charging path planning needs to construct path energy constraints and energy recovery mechanisms according to the characteristics of electric vehicles. In general, the Pareto optimal method or reinforcement learning algorithm is used to optimize the path with time, energy, and other objectives. In ride-sharing scenarios, the research on behavior strategies mainly uses the temporal and spatial distribution features, and coordinate order dispatch, charging, and repositioning operation to maximize fleet profit. In car rental scenarios, the research on behavior strategies uses charging and repositioning operation to provide abundant available electric vehicles to satisfy users' needs at service stations. Research on behavior strategy in V2G scenarios focuses on the three optimization objectives of charging/discharging cost effectiveness, power grid stability, and energy utilization efficiency. Mathematical programming method or reinforcement learning algorithm is often used to optimize the charging/discharging behavior of electric vehicles. Future research on behavior strategies of electric vehicles should focus on the changes in charging behavior after the introduction of autonomous driving technology, with attention to the interpretability and scalability of the model. From the system perspective, battery degradation and integrated scheduling should be further considered.

     

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