车载动力电池放电过程健康状态在线估计

doi:10.12068/j.issn.1005-3026.2022.11.004

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (11): 1544-1551.DOI: 10.12068/j.issn.1005-3026.2022.11.004

车载动力电池放电过程健康状态在线估计

刘芳¹，刘彦鹏¹，李静东¹，卜凡涛²

(1.天津工业大学天津市自主智能技术与系统重点实验室，天津300387； 2.东软睿驰汽车技术(沈阳)有限公司，辽宁沈阳110179)

发布日期:2022-12-06
通讯作者: 刘芳
作者简介:刘芳(1983-)，女，辽宁沈阳人，天津工业大学副教授.
基金资助:
国家重点研发计划项目(2021YFB2501800)；国家自然科学基金资助项目(61802280， 61806143， 61772365， 41772123)；天津市技术创新引导专项(基金)( 21YDTPJC00130).

Online Estimation of State of Health During Discharging of Vehicle Power Battery

LIU Fang¹， LIU Yan-peng¹， LI Jing-dong¹， BU Fan-tao²

1. Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems， Tiangong University， Tianjin 300387， China; 2. Neusoft Reach Automotive Technology， Co.， Ltd， Shenyang 110179， China.

Published:2022-12-06
Contact: LI Jing-dong
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对电动汽车无规则随机充放电特点及在线检测需求，考虑到由于电池一致性问题导致难以保证离线实验数据分析法估计精度的问题，提出一种以离线获取的电池健康状态(SOH)外在指征函数为基础的基于无迹卡尔曼滤波(unscented Kalman filter，UKF)思想的在线闭环校正SOH估算架构.该方法优点在于:能够在随机放电过程中快速估算出高精度的SOH值，算法复杂度相对降低，易于实际工程实现且具有较好的鲁棒性.通过验证可以证明，提出的车载动力电池放电过程SOH估算方法具有较好的实用性及较高的估算精度.

关键词: 健康状态；无迹卡尔曼滤波；自回归模型；电动汽车；锂离子电池

Abstract: According to the characteristics of irregular random charge and discharge of electric vehicles and the requirements of on-line detection， it is difficult to ensure the accuracy of off-line experimental data analysis methods due to battery consistency problems. In this paper， an on-line closed-loop correction SOH (state of health) estimation architecture based on the idea of unscented Kalman filter(UKF)is proposed， which is based on the off-line SOH external indicator function. The advantage of this method is that it can quickly estimate the high-precision SOH value in the random discharge process and the algorithm complexity is relatively reduced. It is easy to implement in practical engineering and the proposed method has better robustness. Through verification， it can be proved that the SOH estimation method proposed in this paper has better practicability and higher estimation accuracy.

Key words: state of health (SOH); unscented Kalman filter; auto regression model; electric vehicle; lithium ion battery

中图分类号:

TP20

刘芳，刘彦鹏，李静东，卜凡涛. 车载动力电池放电过程健康状态在线估计[J]. 东北大学学报（自然科学版）, 2022, 43(11): 1544-1551.

LIU Fang， LIU Yan-peng， LI Jing-dong， BU Fan-tao. Online Estimation of State of Health During Discharging of Vehicle Power Battery[J]. Journal of Northeastern University(Natural Science), 2022, 43(11): 1544-1551.

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车载动力电池放电过程健康状态在线估计

Online Estimation of State of Health During Discharging of Vehicle Power Battery

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