A CNN-LSTM Method Based on Voltage Deviation for Predicting the State of Health of Lithium-Ion Batteries

Fen Xiao, Wei Yang, Yanhuai Ding, Xiang Li, Kehang Zhang, Jiaxiong Liu
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Abstract

Ensuring the accurate estimation of the state of health (SOH) of lithium-ion batteries (LIBs) is essential for the reliability and safe operation of battery management systems. The prediction of SOH has witnessed significant advancements recently, largely propelled by the powerful nonlinear modeling capabilities of deep learning. Despite these advancements, the intricate nature of the battery degradation process poses a challenge in accurately simulating it using measurement data. In this paper, we introduce a novel approach by focusing on the charging voltage deviation, which is defined as the discrepancy between the charging voltage and its average value over each charge/discharge cycle. This deviation is rooted in the electrochemical reactions that lead to capacity decay and voltage fluctuations. We propose a convolutional neural network-long short-term memory (CNN-LSTM) hybrid framework aimed at estimating the SOH of the battery. For each charge/discharge cycle, a conventional CNN is employed to extract key capacity features from sequential charging data, encompassing voltage deviation, current, and charging duration. Following this, an LSTM network is leveraged to build the long-term dependencies of battery capacities, facilitating the SOH prediction process. The experimental results indicate that our model not only simplifies the computational complexity but also significantly enhances the precision of SOH predictions. This innovative approach holds promise for the advancement of battery management systems, ensuring their continued reliability and safety.

Abstract Image

基于电压偏差的CNN-LSTM方法预测锂离子电池健康状态
准确估计锂离子电池的健康状态(SOH)对电池管理系统的可靠性和安全运行至关重要。在深度学习强大的非线性建模能力的推动下,SOH的预测最近取得了重大进展。尽管取得了这些进步,但电池退化过程的复杂性对使用测量数据准确模拟它提出了挑战。在本文中,我们通过关注充电电压偏差引入了一种新的方法,充电电压偏差被定义为每个充放电周期中充电电压与其平均值之间的差异。这种偏差源于导致容量衰减和电压波动的电化学反应。我们提出了一种卷积神经网络-长短期记忆(CNN-LSTM)混合框架,旨在估计电池的SOH。对于每个充放电周期,采用传统的CNN从连续充电数据中提取关键容量特征,包括电压偏差、电流和充电持续时间。在此之后,利用LSTM网络建立电池容量的长期依赖关系,促进SOH预测过程。实验结果表明,该模型不仅简化了计算复杂度,而且显著提高了SOH预测的精度。这种创新的方法为电池管理系统的进步带来了希望,确保了它们持续的可靠性和安全性。
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CiteScore
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