可持续的电动汽车路由使用光谱聚类和模糊增强学习,在移动指数和等待约束下,能源受限制A*
1School of Electrical Engineering, Vellore Institute of Technology, Chennai, 600127, Tamilnadu, India.
Scientific reports
|November 29, 2025
概括
本研究介绍了一种先进的电动汽车 (EV) 路由框架,通过整合光谱聚类,模糊增强学习和增强路径识别,显著减少了旅行时间并提高了能源效率. 这种新的系统优化了路线,考虑了交通,高度和充电站可用性等现实世界的因素,减少了电池违规和距离焦虑.
科学领域:
- 计算机科学, 人工智能, 交通工程.
背景情况:
- 现有的电动汽车 (EV) 路由解决方案往往独立优化指标,未能建模动态相互依存,并适应实时交通和充电条件.
- 当前的拓建模缺乏地理可扩展性,评估通常仅限于简化环境,限制了EV路由框架的实际应用.
研究的目的:
- 开发一个全面的EV路由框架,解决碎片化优化,有限的实时适应性,不足的拓建模和受限制的评估环境.
- 通过集成先进的算法来实现最佳路线计算,以增强EV导航,在现实世界的限制下,包括电池限制,交通动态,地形高度和充电站延迟.
主要方法:
- 一种拓自适应的集群机制,使用光谱集群与地理距离指标和圆区域建模.
- 一个依赖时间的到达模拟模型,用于准确地预测充电站的占用率,并结合时间需求和特定站点的动态.
- 一个基于模糊强化学习的充电站评估器,考虑空间密度,占用趋势和时间可用性.
- 一个增强的A*算法,集成高度感知能量分析,实时交通灵敏度和自适应性充电状态 (SOC) 约束建模.
主要成果:
- 与传统路线相比,实现了总行程时间减少22.8%,能源效率提高19.6%,充电站等待时间减少63.3%.
- 减少了90.0%的电池违规行为,通过改进的SOC意识计划,有效地减轻了距离焦虑.
- 在各种各样的地形 (城市,农村,高度密集) 中表现出卓越的性能,比传统算法提高了多达27.2%.
结论:
- 拟议的框架为电动汽车路由提供了一种统一有效的方法,其性能优于既有算法和最近的多目标解决方案.
- 该系统能够处理现实世界的约束和动态条件的能力提高了电动汽车导航的可靠性和效率.
- 通过实现节能,可靠的电动汽车导航,支持清洁交通和智能城市整合,为可持续发展目标7和11做出贡献.
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