对于退役的电源电池,表面温度辅助的充电状态估计
Liangyu Xu1, Wenxuan Han1,2, Jiawei Dong1,2
1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究引入了一种新方法,通过使用表面温度数据以及电信号来估计退役电池的电荷状态 (SOC). 与传统方法相比,这种方法显著提高了准确性,增强了电池管理系统.
科学领域:
- 电池技术 电池技术
- 电化学 电化学 电化学
- 热管理 热管理
背景情况:
- 退役电池的充电状态 (SOC) 估计是由于降解和老化而具有挑战性的.
- 传统的仅电气参数的方法对于老旧的电池是不准确的,因为改变了内部电阻和热量产生.
- 退役电池中的异质老化机制使SOC预测复杂化.
研究的目的:
- 通过使用表面温度数据,开发用于退役电池的精确SOC估计方法.
- 利用红外热成像来识别与SOC相关的关键温度特征.
- 整合热和电数据,以改善老电池中的SOC估计.
主要方法:
- 红外热成像用于从正/负标签和中心区域捕获温度特征.
- 开发一个Gated Recurrent Unit (GRU) 神经网络来处理多区域的温度和电气数据.
- 在恒流放电条件下进行实验验证 (0.5C-2C).
主要成果:
- 与仅使用电参数的方法相比,拟议的表面温度辅助方法可显著降低根平均平方误差 (RMSE) 64.3-68.1%.
- 在所有测试的放电率中,实现了低于1.04的RMSE值,优于传统基于电压的模型.
- 该GRU模型有效地捕捉了老电池的时空热电相互作用和歇斯底里效应.
结论:
- 表面温度辅助的SOC估计是退役电池的更准确的方法.
- 集热和电数据的GRU模型提高了二级电池应用的可靠性.
- 这种方法可以提高退役电池管理系统在储能应用中的性能.
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