在FPGA平台上使用强化学习进行智能电动汽车充电管理.
Udhaya Mugil Damodarin1, Gian Carlo Cardarilli1, Luca Di Nunzio1
1Department of Electronic Engineering, Tor Vergata University of Rome, Via del Politecnico 1, 00133 Rome, Italy.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究介绍了一个智能电动汽车 (EV) 充电系统,使用FPGA上的强化学习 (RL). 该系统优化电动汽车充电,以减少电网压力和优先考虑需求,提高电力效率和可靠性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 对电动汽车 (EV) 的需求不断增长,需要智能充电解决方案来管理电网负载.
- 现有的充电系统往往缺乏适应能力,无法应对波动的电网条件和不同的充电优先级.
研究的目的:
- 开发一个智能电动汽车充电管理系统,将强化学习 (RL) 集成到现场可编程门阵列 (FPGA) 平台上.
- 优化电动汽车充电决策,以减轻电网压力和优先考虑充电需求.
- 为现代智能电网环境创建一个节能和可扩展的解决方案.
主要方法:
- 在FPGA上实现RL代理的Q学习算法.
- 硬件传感器的集成 (电流,电压,优先级指标) 用于环境感知.
- 动态电流分配和任务优先级为多个电动充电器.
- 硬件设计策略,以实现高效,实时运行和低能耗.
主要成果:
- 该系统有效地管理多个电动汽车充电器,动态分配电流和优先级充电.
- 证明了在变化的需求下保持稳定的运行能力,提高电力效率,安全性和服务可靠性.
- 基于FPGA的设计确保了实时响应和低能耗,适合嵌入式应用和潜在的能量收集.
结论:
- 拟议的基于FPGA的RL系统为高效的电动汽车充电基础设施提供了实际框架.
- 智能,传感器驱动的方法使其能够适应电网波动,并优化能源分配.
- 可扩展的设计支持大型安装的扩展,为强大的智能电网环境做出贡献.
关键词:
在FPGA中,FPGA是指FPGA.这就是Q-learning.电动汽车电动汽车是什么嵌入式系统 嵌入式系统绿色流动性的绿色流动性硬件加速加速器 硬件加速器强化学习是一种强化学习.传感器数据处理数据处理可持续性 可持续性的可持续性更多相关视频
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