Accurate Capacity Prediction and Evaluation with Advanced SSA-CNN-BiLSTM Framework for Lithium-Ion Batteries

IF 5.4 3区材料科学 Q2 CHEMISTRY, PHYSICAL

ACS Applied Energy Materials Pub Date : 2024-02-21 DOI:10.3390/batteries10030071

Chunsong Lin, Xianguo Tuo, Longxing Wu, Guiyu Zhang, Xiangling Zeng

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引用次数: 0

Abstract

Lithium-ion batteries (LIBs) have been widely used for electric vehicles owing to their high energy density, light weight, and no memory effect. However, their health management problems remain unsolved in actual application. Therefore, this paper focuses on battery capacity as the key health indicator and proposes a data-driven method for capacity prediction. Specifically, this method mainly utilizes Convolutional Neural Network (CNN) for automatic feature extraction from raw data and combines it with the Bidirectional Long Short-Term Memory (BiLSTM) algorithm to realize the capacity prediction of LIBs. In addition, the sparrow search algorithm (SSA) is used to optimize the hyper-parameters of the neural network to further improve the prediction performance of original network structures. Ultimately, experiments with a public dataset of batteries are carried out to verify and evaluate the effectiveness of capacity prediction under two temperature conditions. The results show that the SSA-CNN-BiLSTM framework for capacity prediction of LIBs has higher accuracy compared with other original network structures during the multi-battery cycle experiments.

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利用先进的 SSA-CNN-BiLSTM 框架准确预测和评估锂离子电池的容量

锂离子电池（LIB）具有能量密度高、重量轻、无记忆效应等优点，已被广泛应用于电动汽车。然而，在实际应用中，其健康管理问题仍未得到解决。因此，本文将电池容量作为关键的健康指标，并提出了一种数据驱动的容量预测方法。具体来说，该方法主要利用卷积神经网络（CNN）从原始数据中自动提取特征，并结合双向长短期记忆（BiLSTM）算法实现 LIB 的容量预测。此外，还采用了麻雀搜索算法（SSA）来优化神经网络的超参数，以进一步提高原始网络结构的预测性能。最后，利用公开的电池数据集进行了实验，以验证和评估两种温度条件下容量预测的有效性。结果表明，在多电池循环实验中，用于锂电池容量预测的 SSA-CNN-BiLSTM 框架与其他原始网络结构相比具有更高的准确性。

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来源期刊

ACS Applied Energy Materials Materials Science-Materials Chemistry

CiteScore

10.30

自引率

6.20%

发文量

1368

期刊介绍： ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.