电动汽车调度和电池交换站充电时间表问题的双决策模型
Yong Su1, Shishun Tian2, Hao Wu1
1Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, Guangdong, China.
Scientific reports
|July 8, 2025
概括
本研究介绍了电动汽车 (EV) 电池交换站 (BSS) 的新型双决策模型,优化了EV驾驶员的时间和BSS运营成本. 该模型使用先进的算法来有效地管理电动汽车发送和充电时间表.
科学领域:
- 运营研究 运营研究
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 电动汽车 (EV) 的日益普及需要高效的电池交换站 (BSS) 管理.
- 现有的BSS模型往往孤立地处理调度或充电时间表,无法捕捉现实世界的复杂性并为电动汽车驾驶员和运营商优化结果.
研究的目的:
- 开发一个集成的双决策模型,以优化电动汽车发送和BSS充电时间表.
- 为了最大限度地减少电动汽车司机的平均额外时间,同时优化电力成本,电池退化和BSS运营商的功率负载变异.
主要方法:
- 提出了一个双决策模型,其中电动汽车调度解决方案通知BSS充电时间表.
- 开发了一个适应式禁忌搜索 (ATS) 算法用于EV调度问题,考虑额外的时间,电池可用性和队列长度.
- 为复杂的BSS充电时间表问题实施了多目标粒子群优化 (MOPSO) 算法,以找到帕雷托最佳解决方案.
主要成果:
- 拟议的双决策模型在与传统的基于规则的策略 (如近在范围) 相比,表现优越.
- 通过甘特图表可视化的实验结果突出显示了电动汽车等待时间的减少和有效的调度.
- 一个全面的比较证实了ATS和MOPSO算法的有效性和竞争力.
结论:
- 集成的双决策模型为管理电动汽车电池交换站提供了更现实的和最佳的方法.
- 开发的进化算法 (ATS和MOPSO) 为发送电动汽车和BSS充电调度的NP难题提供了有效的解决方案.
- 这项研究有助于提高电动汽车充电基础设施的效率和经济可行性.
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