电动汽车充电站的新型控制策略使用随机建模和排队分析
Shreekant Varshney1, Kaibalya Prasad Panda2, Manthan Shah3
1Department of Mathematics, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382426, Gujarat, India. skvarshney91@gmail.com.
Scientific reports
|July 2, 2025
概括
这项研究使用排队理论建模了电动汽车 (EV) 充电基础设施,并纳入了消费者不耐烦和拒绝等行为. 该框架优化了电动汽车充电网络设计和管理,以提高服务质量和效率.
科学领域:
- 运营研究 运营研究
- 运输工程 运输工程
- 排队理论 排队理论
背景情况:
- 电动汽车 (EV) 充电基础设施的部署面临着由于不可预测的用户行为和排队动态的挑战.
- 传统的模型往往忽视了关键的客户行为,如不耐烦和拒绝,导致网络设计不足最佳.
研究的目的:
- 开发一个全面的分析框架来建模电动汽车充电基础设施.
- 为了提高准确性,将现实的客户行为动态纳入排队模型.
- 为优化电动汽车充电网络容量和管理提供决策支持工具.
主要方法:
- 随机排队-理论方法,重点关注客户行为动态.
- 使用连续时间马尔科夫链 (CTMC) 构建系统动态.
- 矩阵分析解决方案技术,以获得稳定状态概率和性能指标.
主要成果:
- 该模型成功地结合了客户的不耐烦,拒绝,反和依赖国家的服务门.
- 计算了诸如系统占用率,服务器利用率,放弃率和吞吐量等关键性能指标.
- 数字模拟验证了模型,并揭示了客户容忍度,服务质量和运营性能之间的相互依赖.
结论:
- 开发的框架为电动汽车充电网络的容量规划,拥堵控制和服务优化提供了宝贵的见解.
- 该研究提供了一个严格的决策支持系统,用于在动态用户行为下管理电动汽车充电基础设施.
- 调查结果强调了考虑客户行为的重要性,以提高智能充电基础设施的效率和适应性.
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