一个混合优化和图形网络,用于可持续的电动汽车充电,使用双重活性桥梁转换器和可再生能源
Priya Narayanan1, Prabaakaran Kandasamy2, Nehru Kandasamy3
1Department of Electrical & Electronics Engineering, Easwari Engineering College, Chennai, Tamil Nadu, 600089, India. priya13eee@gmail.com.
Scientific reports
|February 25, 2026
概括
本研究介绍了用于混合可再生能源电动汽车充电系统的Pelican优化算法归因多顺序图卷积网络 (POA-AMOGCN) 技术. 它显著降低了能源的水平化成本 (LCOE),提高了经济绩效和电网稳定性.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 优化算法 优化算法
背景情况:
- 电动汽车充电系统 (EVCS) 需要集成混合可再生能源 (HRES) 的高效电力管理.
- 挑战包括电力转换效率和电网稳定性,以实现可靠的电动汽车充电.
- 使用双主动桥 (DAB) 转换器和HRES的EVCS的经济评估需要先进的混合方法.
研究的目的:
- 开发和评估一种新的混合优化技术,用于经济评估与HRES集成的EVCS.
- 主要目标是尽量降低水平化能源成本 (LCOE),以提高经济绩效.
- 加强资源分配,促进电动汽车与可再生能源的整合.
主要方法:
- 集成优化算法 (POA) 用于电源管理和控制信号的生成.
- 利用赋值多顺序图卷积网络 (AMOGCN) 来预测最佳控制信号.
- 在MATLAB中实现并与已建立的优化算法进行比较,如GTO,HCSA-PSO,NN-IPSO,FBIAOA和PSO.
主要成果:
- 拟议的POA-AMOGCN技术实现了0.0549美元/千瓦时的最低LCOE.
- 与传统和其他先进的优化方法相比,证明了实质性的成本效益改善.
- 成功优化了EVCS的电源管理和增强资源分配.
结论:
- POA-AMOGCN方法为混合可再生能源电动汽车充电系统的经济评估和优化提供了卓越的解决方案.
- 这种方法有效平衡发电,电池存储和电动汽车需求,同时提高电网稳定性.
- 这些发现突出了结合先进的优化算法和图形卷积网络的潜力,以高效和成本效益地将可再生能源集成到运输中.
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