电动汽车动力锂电池健康状态的建模与估算

PDF全文下载：  201902017.pdf

文章编号： 16726987（2019）02011306； DOI： 10.16351/j.16726987.2019.02.017

晏勇a, 雷晓蔚b

(阿坝师范学院a.电子信息与自动化学院；b.科技处，四川 阿坝623002）

摘要： 复杂路况下，为提高电动汽车锂电池组健康状态SOH（state of health）估算实时性与准确性，通过扩展卡尔曼滤波算法估算荷电状态，结合锂电池组温度与单体锂电池电压，系统判断锂电池组健康状态，提示故障位置并及时更换。结果表明，通过建模对电动汽车锂电池组健康状态估算简单、方便、准确、高效。保证了锂电池处于最佳状态，提高了驾驶的舒适性与安全性，实用性强。

关键词： 锂电池；健康状态；建模与估算；扩展卡尔曼滤波；荷电状态

中图分类号： TM912文献标志码： A

引用格式： 晏勇, 雷晓蔚. 电动汽车动力锂电池健康状态的建模与估算\[J\]. 青岛科技大学学报（自然科学版）， 2019， 40（2）： 113118.

YAN Yong， LEI Xiaowei. SOH modeling and estimation for electric vehicle lithiumion battery\[J\]. Journal of Qingdao University of Science and Technology（Natural Science Edition), 2019, 40(2)： 113118.

SOH Modeling and Estimation for Electric Vehicle Lithiumion Battery

YAN Yonga， LEI Xiaoweib

(a.College of Electronic Information and Automations;   b.Department of Science and Technology, Aba Teachers University, Aba 623002，China)

Abstract: In order to improve the electric vehicle lithiumion SOH(state of health) estimation the realtime and accuracy under complex road conditions, using extended Kalman filtering algorithm for estimating the state of charge, combined with the temperature of lithiumion battery and the voltage of single lithiumion battery, The system can accurately judge the state of health for lithiumion battery and prompt the fault location to be replaced in time.The results show that the electric vehicle lithiumion battery health state estimation is simple, convenient, accurate and efficient by modeling. The system can keep the lithiumion battery at its best and improves the driving comfort and safety, strong practicability.

Key words: Llithiumion batery；SOH；modeling and estimation；EKF；SOC

收稿日期：  20181103

基金项目： 四川省教育厅自然科学基金重点项目 (17ZA0002).

作者简介： 晏勇（1983—），男, 副教授.