离子电池的健康状况基于可解释的特征碎片,通过图表注意网络和双向门式反复单元
Wenpeng Luan1, Hanju Cai1, Xiaohui Wang1
1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了一种新方法,可以使用可解释的特性和高级深度学习来准确估计离子电池的健康状况. 该方法提高了电动汽车应用的安全性和可靠性.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 准确的健康状况 (SoH) 估计对于离子电池的安全性和性能至关重要.
- 目前的方法与可解释的特征提取和建模空间相关性作斗争.
- 解决这些局限性是可靠电池管理系统的关键.
研究的目的:
- 为可解释的离子电池SoH估计开发一个新的框架.
- 为了提高电池退化建模的准确性和可靠性.
- 为了在嵌入式系统上实现实际部署.
主要方法:
- 利用增量容量 (IC) 分析从充电数据中提取具有物理意义的电压和容量片段.
- 将特征转化为图形结构数据进行分析.
- 采用图表注意力网络 (GAT) 和双向封闭的循环单元 (Bi-GRU),用于空间时间学习的剩余连接.
主要成果:
- 在两个基准数据集上实现了卓越的性能,平均绝对平均误差 (MAE) 为0.561%和根平均平方误差 (RMSE) 为0.783%.
- 演示了较低的计算足迹 (1.68MFLOPs每推理).
- 已确认快速推断时间 (17.55ms) 适合嵌入式平台.
结论:
- 拟议的框架为离子电池SoH估计提供了一个高度准确和可解释的方法.
- 协同的时空学习有效地捕捉了退化趋势.
- 该模型的效率和速度使其适合于现实应用,提高了电池的安全性和寿命.
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