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基于释放部分的LIB健康状况估计,使用多模型组合

Peng Xu¹, Yuan Huang¹, Wenwen Ran¹

¹School of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, China.

|February 22, 2024

概括

这项研究结合了增量容量 (IC),差异热电压测量 (DTV) 和差异温度 (DT) 分析,以准确预测电池健康状况 (SOH). 开发的模型实现了高精度,验证了其可行性,用于早期SOH估计.

科学领域:

电化学电化学电化学
材料科学材料科学材料科学
数据科学数据科学数据科学

背景情况:

准确的电池健康状况 (SOH) 估计对于可靠的电池管理系统 (BMS) 来说至关重要.
现有的方法可能缺乏准确性或需要复杂的内部测量.
排放阶段分析为改善SOH预测提供了一个潜在的途径.

研究的目的:

通过整合多种电化学分析技术,开发和验证一种新的SOH预测模型.
使用外部可测量的数据量化内部电池退化.
为了实现早期和准确的SOH预测,以改善电池管理.

主要方法:

从电压,电流和温度数据中推导和平滑增量容量 (IC),微分热电压测量 (DTV) 和微分温度 (DT) 曲线.
在具有显著相变的曲线域中识别关键特征.
皮尔森相关性分析,以选择最能表明产能恶化的特征.
使用双向长期短期记忆 (BILSTM) 神经网络构建健康状况 (SOH) 预测模型.

主要成果:

综合方法成功地确定了与电池容量退化相关的突出特征.
在两个独立的数据集上,BILSTM模型表现出高的预测准确性.
在SOH预测中实现了根平均平方误差 (RMSE) 低于1.2%和平均绝对误差 (MAE) 低于1%.

结论:

结合IC,DTV和DT分析,提供了一种可靠的方法,用于在电池放电期间预测SOH.
开发的BILSTM模型从外部测量中准确量化了内部电化学反应.
这种方法为BMS中的早期电池SOH估计提供了可行和准确的解决方案.

关键词:

双向长期短期内存网络双向长期短期内存网络不同的温度分析差异.不同的热电量测量分析.增量能力分析增量能力分析离子电池是一种离子电池.健康状况身体健康状况

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