改进了深度学习模型,用于精确预测和节约电动汽车的能源需求,并与认知无线电网络集成
V Niranjani1, Anandakumar Haldorai2
1Sri Eshwar College of Engineering, Coimbatore, India.
Scientific reports
|April 4, 2025
概括
精确的电动汽车 (EV) 能源需求预测对于智能运输至关重要. 拟议的实证模式分解与CNN和海优化算法 (EMD-CNN-SOA) 模型增强了节能并降低了电网负担.
科学领域:
- 智能运输系统 智能运输系统
- 电动汽车技术 电动汽车技术
- 无线通信无线通信
背景情况:
- 电动汽车 (EV) 的快速增长在预测充电需求和管理电网负载方面带来了挑战.
- 有限的电动汽车电池容量和距离焦虑需要有效的节能策略.
- 现有的节能方法往往缺乏足够的准确性.
研究的目的:
- 开发一个准确的模型来预测电动汽车的能源需求,并优化节能.
- 为了减少电网的压力,并最大限度地降低电动汽车充电成本.
- 探索认知无线电 (CR) 的集成,以增强智能运输功能.
主要方法:
- 提出了一个新型模型:用卷积神经网络 (EMD-CNN) 进行实证模式分解,使用海优化算法 (SOA) 进行优化.
- 集成认知无线电 (CR) 技术用于交通中的智能无线通信.
- 对LSTM,深度CNN和RNN等现有方法进行模型性能评估,以节能和CRN精度.
主要成果:
- 在电动汽车节能方面,EMD-CNN-SOA模型实现了88.23%的准确率,超过了TWC,LSTM,Deep CNN和RNN.
- 对于认知无线电网络 (CRN),EMD-CNN-SOA模型显示了92.59%的准确率,超过了LSTM,深度CNN和RNN.
- 拟议的模型有效地减少了电网的负担,并最大限度地降低了充电成本.
结论:
- 在预测电动汽车能源需求和改善节能方面,EMD-CNN-SOA模型提供了显著的进步.
- 集成CR技术通过实现无通信和提供实时交通信息来增强智能运输.
- 这项研究有助于更高效,更具成本效益和更可持续的智能运输系统.
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