基于机器学习的方法来减少混合储能电动汽车的能源消耗
T Paulraj1, Yeddula Pedda Obulesu2
1School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Scientific reports
|August 12, 2025
概括
本研究提出了一种机器学习策略,使用长短期记忆 (LSTM) 网络来优化电动汽车中的超级电容器 (SC) 电流. 这种方法可以减少电池的压力,并通过管理电力需求来提高能源效率.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 汽车工程 汽车工程
背景情况:
- 电池电动汽车 (BEV) 和混合动力电池电动汽车 (HBEV) 面临着高暂时负载影响电池寿命和效率的挑战.
- 超级电容器 (SC) 提供了管理峰值功率需求的潜力,但需要有效的控制策略.
- 优化电池和SC之间的能量分配对于提高电动汽车性能至关重要.
研究的目的:
- 开发和验证一种基于机器学习的新策略,用于产生超级电容器参考电流.
- 优化电池电动汽车 (BEV) 和混合动力电池电动汽车 (HBEV) 的能源分配,使用自适应超级电容器电流预测.
- 降低电池压力,提高热性能,提高电动汽车的整体能源效率.
主要方法:
- 一个长短期记忆 (LSTM) 神经网络被训练使用现实世界的驱动周期数据.
- 训练好的LSTM模型以开放神经网络交换 (ONNX) 格式出口,以便实时部署.
- 一个全面的车辆模型,包括日产Sakura EV,在基于Simulink的控制环境中使用Simscape开发和模拟.
主要成果:
- 超级电容器支持在不同驱动周期 (EUDC,IM240) 中降低了电池峰值电流高达33.5%,峰值功率需求高达31.6%.
- 在超级电容器的帮助下,电池能耗降低了高达12.36%.
- 拟议的LSTM-ONNX框架在降低电池压力,提高热性能和优化功率传输方面取得了显著的改进,从而使引电机扭矩更平稳,电流波动减少.
结论:
- 数据驱动的LSTM-ONNX控制策略有效地优化了电动汽车的超级电容器电流.
- 该框架允许从电池中自适应地卸载高暂时负载,显著提高能源效率和电池寿命.
- 通过全面的模拟,拟议的控制策略的实时适用性和有效性得到了确认.
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