Li Zuxin, Shen Shengyu, Ye Yifu, Cai Zhiduan, Zhen Aigang
School of Intelligent Manufacturing, Huzhou College, Huzhou, 313000, China.
School of Engineering, Huzhou University, Huzhou, 313000, China.
Sci Rep. 2024 May 31;14(1):12541. doi: 10.1038/s41598-024-63160-2.
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is advantageous for maintaining the stability of electrical systems. In this paper, an interpretable online method which can reflect capacity regeneration is proposed to accurately estimate the RUL. Firstly, four health indicators (HIs) are extracted from the charging and discharging process for online prediction. Then, the HIs model is trained using support vector regression to obtain future features. And the capacity model of Gaussian process regression (GPR) is trained and analyzed by Shapley additive explanation (SHAP). Meanwhile, the state space for capacity prediction is constructed with the addition of Gaussian non-white noise to simulate the capacity regeneration. And the modified predicted HIs and noise are obtained by unscented Kalman filter. Finally, according to SHAP explainer, the predicted HIs acting as the baseline and the modified HIs containing information on capacity regeneration are chosen to predict RUL. In addition, the bounds of confidence intervals (CIs) are calculated separately to reflect the regenerated capacity. The experimental results demonstrate that the proposed online method can achieve high accuracy and effectively capture the capacity regeneration. The absolute error of failure RUL is below 5 and the minimum confidence interval is only 2.
准确预测锂离子电池的剩余使用寿命（RUL）有利于维持电气系统的稳定性。本文提出了一种能够反映容量再生的可解释在线方法，以准确估计RUL。首先，从充放电过程中提取四个健康指标（HI）用于在线预测。然后，使用支持向量回归训练HI模型以获得未来特征。并通过Shapley加法解释（SHAP）对高斯过程回归（GPR）的容量模型进行训练和分析。同时，通过添加高斯非白噪声来构建用于容量预测的状态空间，以模拟容量再生。并通过无迹卡尔曼滤波器获得修正后的预测HI和噪声。最后，根据SHAP解释器，选择作为基线的预测HI和包含容量再生信息的修正HI来预测RUL。此外，分别计算置信区间（CI）的边界以反映再生容量。实验结果表明，所提出的在线方法能够实现高精度，并有效捕捉容量再生。失效RUL的绝对误差低于5，最小置信区间仅为2。
