电动汽车充电路线规划,以最短的行程时间,基于改进的殖民地优化
Aiping Tan1, Chang Wang1, Yan Wang1
1School of Cyber Science and Engineering, Liaoning University, Shenyang 110036, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究介绍了电动汽车充电路线规划 (EVCRP-UR),通过个性化路线和充电来解决距离焦虑. 一个改进的群优化 (IACO) 算法优化了路径和充电时间,以实现高效的电动汽车行驶.
科学领域:
- 运输工程 运输工程
- 计算机科学 计算机科学
- 可持续能源 可持续能源
背景情况:
- 电动汽车 (EV) 提供了环境效益,但由于范围有限和充电缓慢而面临采用障碍.
- 距离焦虑仍然是阻碍广泛采用电动汽车的重大挑战.
- 目前的EV路线优化缺乏针对个人用户需求和约束的个性化.
研究的目的:
- 提出基于用户要求 (EVCRP-UR) 的新型电动汽车充电路线规划问题.
- 为个性化电动汽车路由和充电优化制定综合方法.
- 提高电动汽车导航系统的效率和以用户为中心.
主要方法:
- 利用拓优化来减少路径规划中的计算复杂性.
- 引入了一种改进的群优化 (IACO) 算法,采用了新的启发式和概率模型.
- 开发了一种离散电力动态编程 (DE-DP) 算法,以确定最佳的充电时间.
主要成果:
- 拟议的IACO算法有效地整合了用户偏好和个性化EV路由的多个约束.
- 拓优化显著提高了路径规划效率.
- 结合的IACO和DE-DP方法在EV路由和充电优化方面表现出卓越的性能.
结论:
- EVCRP-UR框架通过提供个性化的电动汽车路线和充电解决方案,成功解决了距离焦虑问题.
- IACO算法为优化电动汽车行驶提供了一种高效有效的方法.
- 这项研究有助于通过先进的规划策略克服电动汽车的采用障碍.
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