研究增量范围电动汽车的能源管理策略,整合速度预测
Wei Wang1, Kun Zhang1, Qingming Zhang1
1School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, China.
Science progress
|January 17, 2025
概括
本研究介绍了一种优化的能源管理策略,用于扩展范围的电动汽车,使用一只算法长的短期内存模型来预测速度. 这种方法在各种驾驶条件下显著降低了燃料消耗.
科学领域:
- 汽车工程 汽车工程
- 人工智能的人工智能
- 可持续能源 可持续能源
背景情况:
- 目前用于长距离电动汽车 (EREV) 的能源管理策略难以平衡电力需求和能源使用,以在各种驾驶条件下实现最佳燃油经济.
- 为了提高整体燃油效率,有效地管理EREV中的双重动力源至关重要.
研究的目的:
- 提出和验证EREV的先进能源管理策略,其中包括速度预测,以提高燃油经济性.
- 为了优化电动汽车电动机和内燃机之间的功率分配.
主要方法:
- 研究了长期短期记忆 (LSTM) 神经网络,用于在不同驾驶周期下预测车辆速度.
- 利用算法 (SA) 来优化LSTM模型的超参数,提高预测准确度.
- 开发了一个新的能源管理策略,集成SA-LSTM模型进行预测控制.
主要成果:
- 在SA优化的LSTM模型中,预测误差显著减少,平均平方偏差和平均绝对误差分别减少了46.46%和54.46%.
- 根据新欧洲驾驶周期 (NEDC) 提出的基于速度预测的能源管理策略,在新欧洲驾驶周期 (NEDC) 中实现了6.05%的燃料消耗降低.
- 与传统的基于规则的混合控制策略相比,在世界轻型车辆测试周期 (WLTC) 中观察到燃油消耗减少了3.50%.
结论:
- 整合速度预测,特别是使用SA-LSTM模型,为EREVs提供了能源管理的重大改进.
- 开发的战略通过优化基于预测的驾驶条件的动力分配,有效地提高了燃油经济性.
- 这种预测方法代表了对EREVs传统控制策略的有希望的进步.
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