摘要
荷电状态(SOC)用于表征动力锂电池剩余电量。选用Thevenin等效模型作为模拟电池工作状态的等效电路模型,结合试验测量相关参数,运用电路以及多种数理知识,对构建的锂电池等效电路模型进行参数辨识,并通过曲线拟合等方法对辨识结果进行优化处理。在脉冲特性能力测试(HPPC)中对模型精度进行验证,模型表征误差稳定在1.1%以内。采用平方根无迹卡尔曼算法用状态变量的误差协方差的平方根代替状态变量的误差协方差,直接将协方差的平方根值进行传递。利用平方根无迹卡尔曼算法对荷电状态进行估计,对比无迹卡尔曼算法与平方根无迹卡尔曼算法的SOC估计效果。在25℃的条件下对三元锂电池进行动态应力测试工况(DST)试验,平方根无迹卡尔曼算法和无迹卡尔曼算法锂电池SOC估计的最大误差分别为0.55%与1.5%。试验结果表明,平方根无迹卡尔曼算法的跟踪效果较优,具有更高的SOC估计精度和稳定性。
The state of charge(SOC)is used to characterize the remaining capacity of the power lithium battery.The Thevenin equivalent model is selected as the equivalent circuit model for simulating the working state of the battery,and the releva nt parameters are measured experimentally.The circuit basic knowledge and mathematical and physical knowledge as well are used to identify the parameters of the constructed lithium battery equivalent circuit model,and optimize the identification results through curve fitting and other methods.The accuracy of the model is verified in the hybrid pulse power characteristic(HPPC),and the model characterization error is stable within 1.1%.The square root unscented Kalman algorithm uses the square root of the error covariance of the state variable to replace the error covariance of the state variable,and directly passes the square root value of the covariance.The square root unscented Kalman algorithm is used to estimate the state of charge,and the SOC estimation effect of the unscented Kalman algorithm and the square root unscented Kalman algorithm is compared.Dynamic stress test(DST)experiments are performed on ternary lithium batteries at 25℃.The maximum errors of the square root unscented Kalman algorithm and the unscented Kalman algorithm for lithium battery SOC estimation are 0.55% and 1.5% respectively.The experimental results show that the square root unscented Kalman algorithm has better tracking effect,and has higher SOC estimation accuracy and stability.
作者
吉伟康
王顺利
邹传云
夏黎黎
时浩添
JI Weikang;WANG Shunli;ZOU Chuanyun;XIA Lili;SHI Haotian(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,China)
出处
《自动化仪表》
CAS
2020年第9期42-46,50,共6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
锂电池
等效建模
荷电状态
卡尔曼滤波算法
动态应力测试
Lithium-ion battery
Equivalent modeling
State of charge
Kalman filter algorithm
Dynamic stress test
