摘要
荷电状态(SOC)的准确估计对锂离子电池的在线实时监测和安全控制具有重要意义。以中航锂电池为研究对象,选择二阶阻容(RC)模型对电池工作特性进行表征,并结合多种工况情形对锂离子电池进行研究分析。考虑到参数辨识的初值对在线辨识修正效果的影响,搭建仿真模型与电池脉冲工况特性比较验证,仿真误差在0.05 V以内。在此基础上,构建含有遗忘因子的递推最小二乘法(FFRLS)的在线参数辨识系统,对电池动态应力测试工况(DST)进行仿真预测,相对误差在1.50%以内。针对离线参数辨识的不足,采用在线参数辨识结合扩展卡尔曼(EKF)算法对工况下电池SOC进行估计。试验结果表明,在线参数辨识下,EKF算法能够有效表征系统SOC估算,相对误差精度在0.3%以内。
Accurate estimation of state of charge(SOC)is great significance for online real-time monitoring and safety control of lithium ion batteries.Based on china aviation lithium battery as the research object,chooses the second-order resistancecapacitance(RC),model to charaltevise the battery performance,and connecting with the working condition of a variety of circumstances to study and analysis of lithium ion batteries.Considering the influence of the initial value of parameter identification on the correction effect of online identification,a simulation model was built to compare and verify the working condition characteristics of battery pulse,and the simulation error was within 0.05 V.On this basis,the online parameter identification system of recursive least squares method with forgetting factor(FFRLS)was constructed to simulate and predict the dynamic stress test(DST)of the battery,and the relative error was within 1.50%.Aiming at the deficiency of offline parameter identification,online parameter identification combined with extended Kalman filter(EKF)algorithm was used to estimate the SOC of the battery under operating conditions.The experimental results showed that EKF algorithm could effectively characterize the SOC estimation of the system under online parameter identification,with the relative error accuracy within 0.3%.
作者
李博文
王顺利
于春梅
李建超
谢伟
LI Bowen;WANG Shunli;YU Chunmei;LI Jianchao;XIE Wei(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,China;Mianyang Product Quality Supervision and Inspection Institute,National Electrical Safety Supervision and Inspection Center,Mianyang 621000,China;Sichuan Huatai Electric Co.,Ltd.,Suining 629000,China)
出处
《自动化仪表》
CAS
2020年第3期41-46,52,共7页
Process Automation Instrumentation
基金
国家自然科学基金资助项目(61801407)
四川省科技厅重点研发基金资助项目(2018GZ0390、2019YFG0427)
四川省教育厅科研基金资助项目(17ZB0453)
西南科技大学素质类教改(青年发展研究)专项基金资助项目(18xnsu12)。
关键词
荷电状态
锂离子电池
二阶阻容模型
脉冲工况
在线参数辨识
含有遗忘因子的递推最小二乘法
动态应力测试工况
扩展卡尔曼滤波
State of charge(SOC)
Lithium ion battery
Second-order resistance capacity model
Pulse condition
Online parameter identification
Recursive least square method with forgetting factor
Dynamic stress test
Extended Kalman filter(EKF)
