State of health (SOH) prediction is always a research hotspot in the field of lithium‐ion batteries (LIBs). Machine learning (ML) methods have received widespread attention for their high prediction accuracy. However, the existing studies only focus on extracting features from simple constant current charge‐discharge curves or using features that require pre‐processing, while the actual discharge current is random and can affect battery aging. Besides, the online sequential extreme learning machine (OSELM) currently used in ML lacks a more efficient online learning and update mechanism in terms of prediction. Therefore, this paper firstly extracts effective features from the random discharge data and conducts a mechanism analysis to verify its rationality. Then, we propose a drift detection based on the Bernstein inequality (BI‐DD) algorithm and use it to guide the OSELM to save learning time. The experimental results show the OSELM based on the BI‐DD can perform good learning for SOH prediction in a shorter time. The learning time can be reduced by up to 88.87% and the mean absolute error (MAE) does not exceed 1%, which is a promising SOH prediction method.

中文翻译：

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新型在线顺序极限学习机方法预测锂离子电池的健康状态

健康状态（SOH）预测始终是锂离子电池（LIB）领域的研究热点。机器学习（ML）方法以其高预测精度而受到广泛关注。但是，现有研究仅集中于从简单的恒定电流充放电曲线中提取特征或使用需要预处理的特征，而实际放电电流是随机的，会影响电池的老化。此外，目前在机器学习中使用的在线顺序极限学习机（OSELM）在预测方面缺乏更有效的在线学习和更新机制。因此，本文首先从随机放电数据中提取有效特征，并进行机理分析以验证其合理性。然后，我们提出了一种基于伯恩斯坦不等式（BI‐DD）算法的漂移检测，并用它来指导OSELM节省学习时间。实验结果表明，基于BI‐DD的OSELM可以在较短的时间内很好地学习SOH。学习时间最多可以减少88.87％，平均绝对误差（MAE）不超过1％，这是一种很有前途的SOH预测方法。