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Model improvement and scheduling optimization for multi-vehicle charging planning in IoV

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  • Qian, Jun-Hao
  • Zhao, Yi-Xin
  • Huang, Wei

Abstract

Intelligent electric vehicles (EV) can transmit their location, driving status and other information to Intelligent Transportation Systems (ITS) through the Internet of vehicles (IoV) communication. Among them, the optimization of EV charging planning has great significance in finding a suitable charging station (CS) for users. However, the constraints of State-of-Charge (SOC) and driving direction are incomplete in current planning models. Meanwhile, the existing scheduling policy based on Deep Reinforcement Learning (DRL) suffers from slow convergence due to the fixed average change rate of the reward function. This paper establishes a comprehensive EV charging planning model (CCPM) and presents an efficient multi-vehicle scheduling algorithm (EMVSA). Firstly, CCPM calculates the travel time under the SOC constraints to ensure that CS is reachable and takes into account the direction constraint by minimizing the distance of the selected CS to the user’s destination. Secondly, a novel reward shaping method, which gradually increases the average change rate of the reward function, is presented and proved theoretically to accelerate the convergence of EMVSA. On the real city road network data, experimental results show that CCPM can guarantee the reasonability of CS selection and direction, and that the convergence speed of EMVSA is significantly increased to get the optimal scheduling result.

Suggested Citation

  • Qian, Jun-Hao & Zhao, Yi-Xin & Huang, Wei, 2023. "Model improvement and scheduling optimization for multi-vehicle charging planning in IoV," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    • Handle: RePEc:eee:phsmap:v:621:y:2023:i:c:s0378437123003813
    DOI: 10.1016/j.physa.2023.128826
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    References listed on IDEAS

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    1. Zeng, Jie & Xiong, Yong & Liu, Feiyang & Ye, Junqing & Tang, Jinjun, 2022. "Uncovering the spatiotemporal patterns of traffic congestion from large-scale trajectory data: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Li, Haijian & Zhang, Junjie & Sun, Xiaoliang & Niu, Jun & Zhao, Xiaohua, 2022. "A survey of vehicle group behaviors simulation under a connected vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    3. Wang, Bowen & Wang, Jingsheng, 2022. "ST-MGAT:Spatio-temporal multi-head graph attention network for Traffic prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. Chen, Jianzhong & Li, Jing & Xu, Zhaoxin & Wu, Xiaobao, 2022. "Cooperative optimal control for connected and automated vehicles platoon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
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