电子测量与仪器学报

庞晓琼,李笑,李晓杰,张鑫.联合电池组不一致性评估的RUL区间预测方案[J].电子测量与仪器学报,2026,40(1):91-101

联合电池组不一致性评估的RUL区间预测方案

RUL interval prediction scheme combing battery pack inconsistency evaluation

DOI：

英文关键词:lithium-ion battery pack remaining useful life inconsistency evaluation interval prediction

基金项目:研究生教育创新计划项目（2024AL20）、山西省自然科学基金（202403021211188）、山西省重点研发项目（202102010101011）资助

作者	单位
庞晓琼	中北大学计算机科学与技术学院太原030051
李笑	中北大学计算机科学与技术学院太原030051
李晓杰	中北大学能源与动力工程学院太原030051
张鑫	中北大学计算机科学与技术学院太原030051

Author	Institution
Pang Xiaoqiong	The School of Computer Science and Technology, North University of China, Taiyuan 030051，China
Li Xiao	The School of Computer Science and Technology, North University of China, Taiyuan 030051，China
Li Xiaojie	The School of Energy and Power Engineering, North University of China, Taiyuan 030051,China
Zhang Xin	The School of Computer Science and Technology, North University of China, Taiyuan 030051，China

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中文摘要:

为确保锂离子电池组运行的可靠性与安全性，对其剩余使用寿命（RUL）进行准确且稳健的预测至关重要。然而，电池组内部的不一致性会加速其退化，从而增加了RUL预测的难度。同时，传统的数值预测方法难以适应不同安全级别和紧急情况的需求。为此，提出了一种联合电池组不一致性评估和RUL区间预测的方案。首先，基于电压和温度数据，提取一系列反映电池组不一致性的健康指标（HI）；其次，采用样本熵方法对这些HI进行客观加权，以评估电池组的不一致性；然后，将不一致性评估结果纳入HI，并通过模糊信息粒化（FIG）技术处理，为区间预测提供上下界；最终，利用长短时记忆神经网络建立预测模型，FIG处理后的上下界序列作为输入，容量的上下界序列作为输出，实现电池组RUL的点预测和区间预测。实验结果表明，策略能有效评估电池组的不一致性，且评估结果与电池组退化程度高度相关。此外，对于不同起始点的训练数据，点预测的误差控制在0.32 Ah以内，区间预测的综合评价指标P>1.97，表明预测方法具有良好的可行性和有效性。

英文摘要:

To ensure the reliability and safety of lithium-ion battery pack operation, it is very important to accurately and robustly predict its remaining useful life (RUL). However, the inconsistency within the battery pack will accelerate the process of degradation, which increases the difficulty of RUL prediction. At the same time, the traditional numerical prediction method is difficult to adapt to the needs of different security and emergency levels. Therefore, this study proposes a scheme combining battery pack inconsistency evaluation and RUL interval prediction. Firstly, based on the voltage and temperature data, multiple health indicators (HI) reflecting the inconsistency of battery pack were extracted. Secondly, the sample entropy method is used to objectively weight these HIs to evaluate the inconsistency of the battery pack. Then, the inconsistency evaluation results were incorporated into the health indicator system and processed by fuzzy information granulation (FIG) to provide upper and lower bounds for interval prediction. Finally, the long-term and short-term memory (LSTM) neural network was used modeling, taking the upper and lower bound sequences processed by FIG as inputs and the upper and lower bound sequences of capacity as outputs, and the point prediction and interval prediction of RUL is achieved. The experimental results show that this strategy can effectively evaluate the inconsistency of battery pack, and the evaluation results are highly correlated with the degree of battery pack degradation. In addition, for the training data at different starting points, the error of point prediction results is controlled within 0.32 Ah, the comprehensive evaluation criterion P for interval prediction is higher than 1.97, indicating the feasibility and effectiveness of the prediction method.

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