考虑多时间尺度特性的锂离子电池建模与参数辨识

doi:doi:10.19912/j.0254-0096.tynxb.2024-0718

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考虑多时间尺度特性的锂离子电池建模与参数辨识

更新时间：2025-10-22

- 考虑多时间尺度特性的锂离子电池建模与参数辨识
- Vol. 46, Issue 9, Pages: 263-272(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0718
  CLC： TM912
- Published Online：14 October 2025，
  
  Published：2025
- 稿件说明：
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[J]. 2025, 46(9): 263-272.
DOI：

[J]. 2025, 46(9): 263-272. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0718.

摘要

针对锂离子电池采用传统参数辨识方法会导致辨识结果精度低、工况适应性差等问题

根据锂离子电池在动力学特性上表现出的不同时间尺度特性对二阶RC等效电路模型进行重构

并将其分解为快动态（FD）部分与慢动态（SD）部分

采用改进的遗忘因子递推最小二乘方法（IFRLS）和自适应卡尔曼滤波算法（AKF）对模型的FD与SD部分进行区分辨识

避免模型参数之间的相互干扰

最后在多种工况下将所提方法与传统的参数辨识方法进行对比分析

结果证明所提方法的有效性与准确性对于储能系统的实际应用有一定的工程借鉴价值。

Abstract

The traditional parameter identification methods for lithium-ion batteries will lead to problems such as low accuracy of identification results and poor adaptability to working conditions

and sudden error and even distortion of output voltage curve due to sudden parameter changes in low SOC region. In this paper

the second-order RC equivalent circuit model is reconstructed according to the different time-scale characteristics of lithium-ion battery dynamics

and it is decomposed into fast dynamic （FD） part and slow dynamic （SD） part. The FD part and SD part of the model are distinguished by the improved IFRLS and the adaptive Kalman filter algorithm （AKF） to avoid the mutual interference between the model parameters. Finally

the proposed method is compared with the traditional parameter identification methods under various working conditions

and the results prove the effectiveness and accuracy of the proposed parameter identification method

which has certain engineering reference value for the practical application of energy storage system.

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references

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