计及退化阶段特征的服役电池容量预测

周涛; 缪书唯

doi:10.19799/j.cnki.2095-4239.2025.0925

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计及退化阶段特征的服役电池容量预测

储能测试与评价 | 更新时间：2026-04-29

- 计及退化阶段特征的服役电池容量预测
- Prediction of service battery capacity considering the characteristics of the degradation phase
- 储能科学与技术 2026年15卷第4期页码：1451-1462
- 作者机构：
  
  三峡大学电气与新能源学院，湖北宜昌 443002
- 作者简介：
  
  周涛（2001—），男，硕士研究生，研究方向为锂电池剩余容量预测，E-mail：3303347080@qq.com；
  缪书唯，副教授，研究方向为风电场并网系统可靠性评估与优化，E-mail：jabker@163.com。
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.0925
  中图分类号： TM 912;TP 183
- 收稿：2025-10-20，
  
  修回：2025-11-17，
  
  纸质出版：2026-04-28
- 稿件说明：
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周涛, 缪书唯. 计及退化阶段特征的服役电池容量预测[J]. 储能科学与技术, 2026, 15(4): 1451-1462.

ZHOU Tao, MIAO Shuwei. Prediction of service battery capacity considering the characteristics of the degradation phase[J]. Energy Storage Science and Technology, 2026, 15(4): 1451-1462.
周涛, 缪书唯. 计及退化阶段特征的服役电池容量预测[J]. 储能科学与技术, 2026, 15(4): 1451-1462. DOI： 10.19799/j.cnki.2095-4239.2025.0925.

ZHOU Tao, MIAO Shuwei. Prediction of service battery capacity considering the characteristics of the degradation phase[J]. Energy Storage Science and Technology, 2026, 15(4): 1451-1462. DOI： 10.19799/j.cnki.2095-4239.2025.0925.

摘要

精准预测锂电池容量对保证其安全稳定运行极为重要。为此，本研究将锂电池简称为电池，将已知全生命周期数据的电池称为测试电池，正在使用中且数据有限的电池称为服役电池，提出基于迁移学习且计及退化阶段特征的服役电池容量预测模型。首先，采用双Bacon-Watts方法，将测试电池全生命周期划分为早期、中期、末期3组退化阶段。接着，构建电池阶段性匹配机制，该机制根据测试电池早期容量数据与服役电池容量数据间的时间扭曲编辑距离，通过两步筛选获得与服役电池适配的测试电池。在退化阶段划分的基础上，通过识别测试电池当前阶段归属以及阶段内的相对位置信息，刻画其阶段编码。随后，应用多层感知机对阶段编码进行特征映射，将映射后的阶段特征嵌入长短期记忆网络中，同时引入加速退化损失项，引导模型学习电池实际退化规律，实现对测试电池的容量预测。最后，将测试电池的预测模型参数进行微调，并迁移至服役电池容量预测任务中。利用麻省理工学院公开数据集进行验证，模型预测平均绝对误差、平均绝对百分比误差及均方根误差均低于1%，为服役电池早期容量预测提供了可靠方案。

Abstract

Accurate prediction of lithium-ion battery capacity is of great significance for its safe and stable operation. Hence

this study refers to lithium-ion batteries as batteries

refers to batteries with known full-life cycle data as test batteries

refers to batteries currently in use with limited data as in-service batteries

and proposes an in-service battery capacity prediction model based on transfer learning that accounts for degradation phase characteristics. First

the double Bacon-Watts method is adopted to divide the entire life cycle of test batteries into three degradation phases: early

middle

and end phases. Subsequently

a phased matching mechanism for batteries is constructed. Based on the Time Warping Edit Distance between the early-stage capacity data of test batteries and the capacity data of in-service batteries

this mechanism obtains test batteries compatible with in-service batteries through a two-step screening process

providing high-quality data samples for subsequent model training. On the basis of phase division

the phase code is characterized by identifying both the current phase attribution of the test battery and the relative position information within that phase. Then

a multi-layer perceptron is applied to perform feature mapping on the phase codes. The mapped phase features are embedded into a long short-term memory network

while an accelerated degradation loss term is introduced simultaneously to guide the model in learning the actual degradation law of batteries

thereby achieving capacity prediction for the test batteries. Finally

the prediction model parameters of the test batteries are fine-tuned and then transferred to the capacity prediction task of in-service batteries. Validation was conducted using the public dataset from the Massachusetts Institute of Technology. The model's prediction results show that the mean absolute error

mean absolute percentage error

and Root Mean Squared Error are all below 1%

providing a reliable solution for the early-phase capacity prediction of in-service batteries.

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