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<p>With the rapid adoption of new energy vehicles (NEVs), efficient charging infrastructure has become critical for sustainable mobility. However, high costs, uneven distribution, and low operator willingness to share resources hinder charging infrastructure utilization. This paper presents an incentive mechanism to encourage the sharing of high-quality charging resources within simulated scenarios. A hybrid cloud-based framework integrates utility modeling with game-theoretic user selection and particle swarm optimization (PSO) for resource allocation. The model considers charging demand, resource costs, and user experience under idealized and complete information assumptions, while the game mechanism simulates user decision-making under resource constraints. Experimental results under controlled simulation settings demonstrate that the proposed approach significantly improves operator sharing willingness, resource utilization, and user satisfaction compared to traditional allocation methods, ultimately enhancing comprehensive benefits (considering the combined benefits of operators, users, and society).</p>

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Research on resource sharing and allocation incentive mechanism of new energy vehicle charging cloud platform

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月22日

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  • Junying Hu,
  • Qinjin Wei,
  • Huan Xu,
  • Yue Wu

摘要

With the rapid adoption of new energy vehicles (NEVs), efficient charging infrastructure has become critical for sustainable mobility. However, high costs, uneven distribution, and low operator willingness to share resources hinder charging infrastructure utilization. This paper presents an incentive mechanism to encourage the sharing of high-quality charging resources within simulated scenarios. A hybrid cloud-based framework integrates utility modeling with game-theoretic user selection and particle swarm optimization (PSO) for resource allocation. The model considers charging demand, resource costs, and user experience under idealized and complete information assumptions, while the game mechanism simulates user decision-making under resource constraints. Experimental results under controlled simulation settings demonstrate that the proposed approach significantly improves operator sharing willingness, resource utilization, and user satisfaction compared to traditional allocation methods, ultimately enhancing comprehensive benefits (considering the combined benefits of operators, users, and society).