It is a common intuition from battery experts that many shape features in the voltage profile image contain abundant information related to battery performance. However, such features are often too subtle for a human to extract by eye inspection and further correlate with battery performance. Using long cycling data from hundreds of large-format pouch cells and a total of 2 million cycles tested over 1000 days, we demonstrate here for the first time that it is advantageous to accurately predict the capacity and remaining useful life in real time by learning battery voltage profile images rather than voltage values. A strategy of end-to-end performance prediction of large-format battery cells is thus demonstrated to be feasible using only a few of the previous cycles at any given time point during the cycling test. Our work paves the way toward the application of machine learning for real-time battery performance prediction and regulation for electric vehicle applications.

中文翻译：

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机器学习一百万次循环作为电动汽车应用实用电池的二维图像

电池专家的一个普遍直觉是，电压曲线图中的许多形状特征都包含与电池性能相关的丰富信息。然而，这些特征通常过于微妙，人类无法通过肉眼检查提取并进一步与电池性能相关联。使用来自数百个大型软包电池的长循环数据和超过 1000 天的总共 200 万次循环测试，我们在这里首次证明通过学习电池实时准确预测容量和剩余使用寿命是有利的电压曲线图像而不是电压值。因此，在循环测试期间的任何给定时间点，仅使用几个先前的循环，就证明了大型电池单元的端到端性能预测策略是可行的。