The invention discloses a lithium battery SOH prediction method and device, and relates to the technical field of lithium battery health management, and the method comprises the steps: obtaining the sample data of a lithium battery, and carrying out the screening of the sample data through a gray correlation analysis method, and obtaining the related data; the method comprises the following steps: acquiring original data of a lithium battery SOH, and decomposing the original data through ensemble empirical mode decomposition to generate label data; constructing a deep learning model, wherein the deep learning model is a CNN-bolstm model based on an attention mechanism; training the deep learning model through the associated data and the label data to generate an optimization model, predicting the measured data through the optimization model, and generating a lithium battery SOH prediction result to realize lithium battery SOH prediction; through the technical scheme, the problems of poor stability, poor generalization ability and low prediction precision of traditional battery SOH prediction can be solved.
本发明公开一种锂电池SOH预测方法及装置,涉及锂电池健康管理技术领域,该方法包括:获取锂电池的样本数据,通过灰色关联度分析方法对样本数据进行筛选,得到相关联数据;获取锂电池SOH的原始数据,通过集合经验模态分解对原始数据进行分解,生成标签数据;构建深度学习模型,其中所述深度学习模型为基于注意力机制的CNN‑bilstm模型;通过相关联数据及标签数据对深度学习模型进行训练,生成优化模型,通过优化模型对实测数据进行预测,生成锂电池SOH预测结果以实现锂电池SOH预测;通过上述技术方案,本发明能够解决传统电池SOH预测稳定性差、泛化能力差、预测精度低的问题。
Lithium battery SOH prediction method and device
一种锂电池SOH预测方法及装置
2023-03-17
Patent
Electronic Resource
Chinese
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