基于双重注意力混合数据驱动方法的离子电池的增强剩余使用寿命预测
Zhenglu Shi1, Al-Wesabi Ibrahim1, Jiazhu Xu2
1College of Electrical and Information Engineering, Hunan University, Changsha, 410083, China.
Scientific reports
|January 12, 2026
概括
对电动汽车 (EV) 和轮椅离子电池 (LIB) 的准确剩余使用寿命 (RUL) 预测至关重要. 本研究引入了一种先进的编码器解码器模型,具有双重注意力机制,以提高RUL预测的准确性和可靠性.
科学领域:
- 电池技术 电池技术
- 人工智能的人工智能
- 机器学习用于工程.
背景情况:
- 电动汽车 (EV) 和轮椅依赖离子电池 (LIB) 进行移动.
- 预测LIB的剩余使用寿命 (RUL) 对安全和维护至关重要.
- 使用双向封闭反复单位 (BiGRU) 的现有方法由于依赖顺序输入,对预测精度有局限性.
研究的目的:
- 为电动汽车和轮椅中的LIB开发一个更准确的RUL预测模型.
- 通过整合注意力机制来克服传统序列模型的局限性.
- 改进电池管理系统,以提高操作可靠性和用户安全.
主要方法:
- 一种编码器-解码器架构,将双重注意力机制与BIGRU网络集成在一起.
- 一个注意力增强的BIGRU编码器,以强调关键电池衰老特征.
- 一个BiGRU解码器用于精确预测容量降解轨迹.
主要成果:
- 拟议的模型在NASA和CALCE数据集上实现了高精度,绝对误差很低 (例如,在特定的NASA单元中0-4).
- 与现有基准相比,表现出优越的预测准确性,稳定性和通用性.
- 在预测LIBs容量退化轨迹方面的验证有效性.
结论:
- 新的双重注意力编码器-解码器BiGRU模型显著提高了EV和轮椅LIB的RUL预测.
- 这种高保真度模型为推进电池管理系统提供了巨大的潜力.
- 改进的RUL预测可以提高操作可靠性,安全性和最终用户体验.
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