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无线传感器网络的电池的早期健康状况预测使用LSTM和单个指数退化模型

Lorenzo Ciani¹, Cristian Garzon-Alfonso¹, Francesco Grasso¹

¹Department of Information Engineering, University of Florence, Via di Santa Marta 3, 50139 Florence, Italy.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

智能电网中的电池的精确健康状况 (SOH) 预测可以通过长短期内存网络 (LSTM) 实现. 即使数据有限 (30%的使用率),LSTM也可以提供可靠的SOH估计,提高电网可靠性和电池寿命.

科学领域:

电气工程电气工程
计算机科学计算机科学
材料科学材料科学材料科学

背景情况:

电池对于无线传感器网络和智能电网至关重要.
健康状况 (SOH) 预测对于电网可靠性,能源管理和电池寿命至关重要.
准确的SOH估计支持可再生能源的整合,并降低维护成本.

研究的目的:

为预测电池的SOH和剩余使用寿命 (RUL) 提供解决方案.
评估长短期内存 (LSTM) 网络对电池SOH预测的有效性.
通过使用LSTMs来确定精确SOH估计的最佳数据要求.

主要方法:

利用了两个数据集:NASA的原始电池数据和使用单个指数模型的曲线拟合数据.
训练有素的LSTM网络对数据表示30%,50%和65%的电池周期消耗.
探索各种LSTM架构和超参数以优化性能.

主要成果:

一个用仅50条记录 (30%使用率) 训练的LSTM模型实现了1.68×10-4的平均平方误差 (MSE) 和1.30×10-2的根平均平方误差 (RMSE).
使用110个记录进行训练的表现最好的LSTM模型,产生了2.51×10-5的MSE和5.01×10-3的RMSE.
证明即使使用有限数量的电池使用数据,也可以准确地预测SOH.

结论:

在智能电网中,LSTM 网络为电池 SOH 和 RUL 预测提供了强大的解决方案.
有效的SOH估计可以用比以前假设的要少得多的数据来实现.
这些发现支持改进的电池管理策略,提高智能电网运营的可靠性和效率.

关键词:

这是LSTM的LSTM.美国国家航空航天局NASA NASA 电池的健康状况机器学习是机器学习.预后和健康管理.经常性的神经网络.剩余的使用寿命.单个指数模型是一个指数模型.智能电网是一个智能电网.

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