Multi-Factor Online Estimation Method for Health Status of Lithium-Ion Battery

doi:10.7652/xjtuxb202001021

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Multi-Factor Online Estimation Method for Health Status of Lithium-Ion Battery

更新时间：2025-07-09

- Multi-Factor Online Estimation Method for Health Status of Lithium-Ion Battery
- Vol. 54, Issue 1, Pages: 169-175(2020)
- 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：
- DOI：10.7652/xjtuxb202001021
  CLC： TM912
- Online First：10 January 2020，
  
  Published：2020
- 稿件说明：
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Multi-Factor Online Estimation Method for Health Status of Lithium-Ion Battery[J]. 2020, 54(1): 169-175.
DOI：

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

关键词

Keywords

references

NG K S, MOO C S, CHEN Y P, et al. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries [J]. Applied Energy, 2009, 86(9): 1506-1511.

OVEJAS V J, CUADRAS A, MORANTE J R. SoH evaluation of LiFePO4 cells using impedance and thermal measurements [C]∥2014 IEEE 11th International Multi-Conference on Systems, Signals Devices(SSD14). Piscataway, NJ, USA: IEEE, 2014: 1-6.

SARIKURT T, CEYLAN M, BALIKCI A. An analytical battery state of health estimation method [C]∥2014 IEEE 23rd International Symposium on Industrial Electronics(ISIE). Piscataway, NJ, USA: IEEE, 2014: 1605-1609.

KLASS V, BEHM M, LINDBERGH G. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation [J]. Journal of Power Sources, 2014, 270: 262-272.

何发尧, 胡欲立, 郭广华, 等. 基于人工神经网络估算锂离子电池的SOH [J]. 电源技术, 2017, 41(5): 708-710.

HE Fayao, HU Yuli, GUO Guanghua, et al. State of health estimation for lithium-ion batteries based on ANN [J]. Chinese Journal of Power Sources, 2017, 41(5): 708-710.

邓涛, 罗卫兴, 李志飞, 等. 双卡尔曼滤波法估计电动汽车电池健康状态 [J]. 电池, 2018, 48(2): 95-99.

DENG Tao, LUO Weixing, LI Zhifei, et al. Estimation state of health of electric vehicle battery by dual Kalman filter [J]. Battery Bimonthly, 2018, 48(2): 95-99.

程泽, 杨磊, 孙幸勉. 基于自适应平方根无迹卡尔曼滤波算法的锂离子电池SOC和SOH估计 [J]. 中国电机工程学报, 2018, 38(8): 2384-2393.

CHENG Ze, YANG Lei, SUN Xingmian. State of charge and state of health estimation of Li-ion batteries based on adaptive square-root unscented Kalman filters [J]. Proceedings of the CSEE, 2018, 38(8): 2384-2393.

朱丽群, 张建秋. 一种联合锂电池健康和荷电状态的新模型 [J]. 中国电机工程学报, 2018, 38(12): 3613-3620.

ZHU Liqun, ZHANG Jianqiu. A new model of jointed states of charge and health for lithium batteries [J]. Proceedings of the CSEE, 2018, 38(12): 3613-3620.

GHOLIZADEH M, SALMASI F R. Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model [J]. IEEE Transactions on Industrial Electronics, 2014, 61(3): 1335-1344.

邹峰. 锂离子电池健康状态评估及剩余使用寿命预测技术研究 [D]. 南京: 南京航空航天大学, 2016: 28-57.

RAMADASS P, HARAN B L, WHITE R, et al. Mathematical modeling of the capacity fade of Li-ion cells [J]. Journal of Power Sources, 2003, 123(2): 230-240.

SCHMIDT A P, BITZER M, IMRE á W, et al. Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries [J]. Journal of Power Sources, 2010, 195(22): 7634-7638.

赵云飞, 徐俊, 王霄, 等. 双自适应衰减卡尔曼滤波锂电池荷电状态估计 [J]. 西安交通大学学报, 2018, 52(12): 99-105.

ZHAO Yunfei, XU Jun, WANG Xiao, et al. An estimation method for state of charge of lithium-ion batteries using dual adaptive fading extended Kalman filter [J]. Journal of Xi’an Jiaotong University, 2018, 52(12): 99-105.

刘毅, 谭国俊, 何晓群. 优化电池模型的自适应Sigma卡尔曼荷电状态估算 [J]. 电工技术学报, 2017, 32(2): 108-118.

LIU Yi, TAN Guojun, HE Xiaoqun. Optimized battery model based adaptive sigma Kalman filter for state of charge estimation [J]. Transactions of China Electrotechnical Society, 2017, 32(2): 108-118.

易明亮. 基于内阻法的电池健康状态估计技术研究 [D]. 杭州: 杭州电子科技大学, 2013: 20-55.

唐焕文. 实用最优化方法 [M]. 3版. 大连: 大连理工大学出版社, 2005: 194-201.

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