锂离子电池健康状态多因子在线估计方法

陈猛 1; 乌江 1; 焦朝勇 1; 陈继忠 2; 张在平 3

doi:10.7652/xjtuxb202001021

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锂离子电池健康状态多因子在线估计方法

更新时间：2025-07-09

- 锂离子电池健康状态多因子在线估计方法
- Multi-Factor Online Estimation Method for Health Status of Lithium-Ion Battery
- 西安交通大学学报 2020年54卷第1期页码：169-175
- 作者机构：
  
  西安交通大学电气工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2019-06-27。作者简介: 陈猛(1990—),男,硕士生;乌江(通信作者),男,讲师。基金项目: 中国电力科学研究院有限公司新能源与储能运行控制国家重点实验室开放基金资助项目(DGB51201801575)
- DOI：10.7652/xjtuxb202001021
  中图分类号： TM912
- 网络首发：2020-01-10，
  
  纸质出版：2020
- 稿件说明：
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陈猛 1, 乌江 1, 焦朝勇 1, 等. 锂离子电池健康状态多因子在线估计方法[J]. 西安交通大学学报, 2020,54(1):169-175.

Multi-Factor Online Estimation Method for Health Status of Lithium-Ion Battery[J]. 2020, 54(1): 169-175.
陈猛 1, 乌江 1, 焦朝勇 1, 等. 锂离子电池健康状态多因子在线估计方法[J]. 西安交通大学学报, 2020,54(1):169-175. DOI： 10.7652/xjtuxb202001021.

Multi-Factor Online Estimation Method for Health Status of Lithium-Ion Battery[J]. 2020, 54(1): 169-175. DOI： 10.7652/xjtuxb202001021.

摘要

针对传统的锂离子电池健康状态估计方法仅用电池欧姆内阻作为单因子评估指标时存在较大误差的问题

提出了一种利用电池欧姆内阻、极化内阻与极化电容共3个模型参数构建的多因子评估模型。选用一阶RC等效电路模型作为基础电路模型

并通过仿真实验验证了所选择电路模型的准确性。对同一型号的多组三元锂离子电池进行循环老化实验

得到离线辨识的模型参数

发现等效电路模型中的欧姆内阻、极化内阻、极化电容与健康状态存在确定的关系。通过带约束的最小二乘算法求解对应模型参数的权重

并以卡尔曼滤波算法在线辨识模型参数

实时获得基于多因子模型的综合电池健康状态。将所提方法与仅用欧姆内阻评估的方法进行了对比

结果表明:所提方法评估锂离子电池真实健康状态的误差变化范围较小

基本在1%左右

精度更高。

Abstract

The traditional lithium-ion battery health state estimation method only uses the battery ohmic internal resistance as a single-factor evaluation index which leads to a big error. Therefore

it is necessary to construct a multi-factor evaluation model with three parameters including battery ohmic internal resistance

polari-ation internal resistance and polari-ation capacitance. The first-order RC equivalent circuit model serves as the basic circuit model. The accuracy of the selected circuit model is verified by a simulation. The equivalent circuit model parameters

namely the ohmic internal resistance

polari-ation internal resistance

and polari-ation capacitance are obtained via the cyclic aging experiment for the offline identification. The offline identification results verify the certain relationship of the above three model parameters with health state. The weight coefficients of the corresponding model parameters are solved with constrained least squares algorithm

and the Kalm

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references

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