基于恒流充电数据的锂电池容量估计方法

程伟达; 孙硕; 尹航; 王宗亮; 蔡巍; 宋佳锴

doi:10.19912/j.0254-0096.tynxb.2025-0090

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基于恒流充电数据的锂电池容量估计方法

更新时间：2026-07-22

- 基于恒流充电数据的锂电池容量估计方法
- Vol. 47, Issue 6, Pages: 552-561(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0090
  CLC： TM911
- Online First：08 July 2026，
  
  Published：2026
- 稿件说明：
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程伟达, 孙硕, 尹航, et al. 基于恒流充电数据的锂电池容量估计方法[J]. 2026, 47(6): 552-561.
DOI：

程伟达, 孙硕, 尹航, et al. 基于恒流充电数据的锂电池容量估计方法[J]. 2026, 47(6): 552-561. DOI： 10.19912/j.0254-0096.tynxb.2025-0090.

摘要

针对锂电池使用过程中容量无法直接测量的难题

提出一种基于恒流充电数据的锂电池容量估计方法。首先

从恒流充电阶段提取两种老化特征序列：一是恒流充电阶段提取的充电容量特征序列

二是等充电电压上升的时间间隔特征序列。其次

为尽可能充分利用所测得的充电信息

提取老化特征中关键的容量衰减因素

构建一种新的一维卷积神经网络容量估计模型。最后

在马里兰锂电池数据集上验证所提模型的性能。实验结果表明

所提方法可实现电池容量准确且稳健的估计。此外

所提模型经过少量数据迁移学习后

可准确估计其他放电倍率下的电池放电容量

经过前50个循环周期的迁移学习

模型在0.5C放电倍率下的均方根误差仅为0.58%。

Abstract

Accurate estimation of lithium battery capacity is the basis for its safe and reliable operation. However

the lithium battery capacity attenuation mechanism is not clear

and its capacity shows nonlinear attenuation during use

making it extremely difficult to accurately estimate the lithium battery capacity. In order to solve these problems

this paper proposes a method to estimate the capacity of lithium batteries by using constant current charging data and a one-dimensional convolutional neural network model. Firstly

two aging feature sequences are extracted from the constant current charging stage： the constant current charging capacity and the time interval of equal charging voltage rise. Secondly

in order to make full use of the measured charging information and extract the key capacity attenuation factors in aging feature sequences

a new one-dimensional convolutional neural network capacity estimation model is constructed. Finally

the proposed capacity estimation model is verified and analyzed on the aging data set of lithium batteries in Maryland. The experimental results show that the proposed method can achieve an accurate and robust estimation of battery capacity on the complete constant current charging curve and partial constant current charging curve

which is better than other capacity estimation methods. The root mean square error （RMSE） and mean absolute error （MAE） can be controlled within 0.68% and 0.50%. Furthermore

after transfer learning with a small amount of date

the proposed model can accurately estimate battery discharge capacity at other discharge rates. After transfer learning for the first 50 cycles

the root mean square error of the model at a 0.5 discharge rate is only 0.58%.

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references

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