An Online State of Charge Estimation Algorithm for Lithium Batteries

LI Wenfan; YANG Kun; MA Haifeng; WANG Yaoxiong; WANG Junxiong

doi:10.7652/xjtuxb202409016

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An Online State of Charge Estimation Algorithm for Lithium Batteries

更新时间：2025-07-28

- An Online State of Charge Estimation Algorithm for Lithium Batteries
- Vol. 58, Issue 9, Pages: 164-172(2024)
- 作者机构：
  
  1. 西北机电工程研究所,陕西,咸阳,712000
  2. 西安交通大学电气工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202409016
  CLC： TM93
- Online First：10 September 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
LI Wenfan, YANG Kun, MA Haifeng, et al. An Online State of Charge Estimation Algorithm for Lithium Batteries[J]. 2024, 58(9): 164-172.
DOI：

LI Wenfan, YANG Kun, MA Haifeng, et al. An Online State of Charge Estimation Algorithm for Lithium Batteries[J]. 2024, 58(9): 164-172. DOI： 10.7652/xjtuxb202409016.

摘要

针对三元锂电池的在线荷电状态估计问题

提出了一种基于开路电压在线计算和查表法的锂电池在线荷电状态估计算法。首先

对锂电池进行一阶戴维南等效建模

通过等效电路模型

推导并化简得到端口电压、端口电流和开路电路之间的关系; 然后

利用递归最小二乘法完成了开路电压的在线求解

通过锂电池工作中的端口电压、电流参数实现了开路电压近似值的实时计算; 最后

介绍了所提出在线荷电状态估算算法的实现流程

并通过典型数据集对所提出算法的估计精度、运算量等性能进行了对比分析和实验验证。实验结果表明:所提出的在线荷电状态估计算法的估算误差低于15%

运算速度是改进卡尔曼滤波法的2.5倍

表明所提在线荷电状态估计算法精度较高且运算速度较快

适合用于在线荷电状态估算。

Abstract

An online state of charge estimation algorithm for ternary lithium batteries is proposed based on online calculation of open circuit voltage and table lookup method. Firstly

the first-order Thevenin equivalent model is applied to the lithium battery

and the relationship between port voltage

port current

and open circuit voltage is derived through the equivalent circuit model. Then

the recursive least squares method is used to solve the open circuit voltage online

and real-time calculation of the approximate open circuit voltage is achieved through the port voltage and current parameters during the operation of the lithium battery. Finally

the implementation process of the proposed online state of charge estimation algorithm is introduced

and the estimation accuracy

computational complexity

and other performance of the proposed algorithm are compared

analyzed

and experimentally verified using typical datasets. The experimental results show that the estimation error of the proposed online state of charge estimation algorithm is less than 15%

and the computational speed is 2.5 times that of the extended Kalman filter method. This indicates that the proposed online state of charge estimation algorithm has high accuracy and low computational complexity

which make it suitable for online state of charge estimation.

关键词

Keywords

references

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