提高电动汽车电池性能和效率的智能策略
Swathi Tangi1, Ayush Vatsa1, Akshat Opam1
1Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
本研究介绍了一种机器学习框架,用于预测电动汽车 (EV) 的续航里程,并优化驾驶以减少续航范围的焦虑. 人工智能模型组合显著提高了预测准确性,提高了EV效率和用户体验.
科学领域:
- 人工智能的人工智能
- 汽车工程 汽车工程
- 数据科学数据科学数据科学
背景情况:
- 对电动汽车 (EV) 的日益增长的需求需要准确的续航里程预测,以减轻续航里程焦虑.
- 优化加速和速度等驾驶参数对于提高电动汽车用户体验和效率至关重要.
研究的目的:
- 开发和评估一个机器学习 (ML) 框架,用于预测EV范围,最佳加速和速度.
- 为了比较单个ML模型与整体组合的性能,以获得预测准确度.
主要方法:
- 一个合成数据集由2000个现实世界驾驶场景生成.
- 四个ML模型 (随机森林,额外树木,线性回归,LSTM) 被单独和组合训练和测试.
- 通过随机数据分区的十个独立运行,确保了统计可靠性.
主要成果:
- 整体模型在预测电动汽车续航里程和驾驶参数方面始终优于单个模型.
- 整体组合 (RF+ET+LSTM+LR) 在MAE,MSE和R2指标中表现最强.
- 开发了一个实时网络应用程序,用于动态估计驾驶参数.
结论:
- 人工智能驱动的预测建模为改善电动汽车能源管理和驾驶效率提供了巨大的潜力.
- 拟议的框架可以支持高效的驾驶行为,并减少电动汽车用户的距离焦虑.
- 将ML集成到电动汽车系统中是推动可持续运输的关键.
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