深度学习图像识别在锂电池健康状态评估中的应用

Yanli Liu, Yu Su, Shaofan Zhang, Vladimir Terzija, Ze Cheng
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引用次数: 0

摘要

准确估算锂离子电池的健康状态(SOH)是保证锂离子电池可靠运行的关键。不同老化水平下的电池恒流充电电压曲线存在显著偏差。基于一维时间序列数据的传统方法在捕获和表征这些复杂模式方面存在局限性。为了解决这一问题,本文利用锂电池恒流充电电压段的一维(1D)时间序列数据,通过增量容量分析选择。然后使用Gramian角和场算法将该数据转换为二维表示。利用ResNet出色的图像识别能力,该方法实现了高精度的SOH估计。使用来自牛津大学和马里兰大学的公开数据集进行的验证表明,与直接将电压段输入网络的传统技术相比,该技术在电池SOH估计精度方面有了显着提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Application of deep learning image recognition for lithium battery State of Health assessment

Application of deep learning image recognition for lithium battery State of Health assessment

Application of deep learning image recognition for lithium battery State of Health assessment

Accurately estimating the State of Health (SOH) of lithium-ion batteries is essential for ensuring their reliable operation. The constant-current charging voltage curves of batteries at different aging levels show significant deviations. Traditional methods based on one-dimensional time-series data face limitations in capturing and characterizing these complex patterns. To address this issue, this paper leverages the one-dimensional (1D) time series data of the lithium battery constant-current charging voltage segment, selected using incremental capacity analysis. This data is then transformed into a two-dimensional representation using the Gramian angular summation field algorithm. Utilizing the exceptional image-recognition capabilities of ResNet, this approach achieves high-accuracy SOH estimation. Validation using publicly available datasets from the University of Oxford and the University of Maryland demonstrates a significant improvement in battery SOH estimation accuracy compared to traditional techniques, which directly input voltage segments into the network.

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