OCV处于平台期的汽车锂电池SOC估算的研究
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摘要
对于开路电压(OCV)处于平台期的锂电池,剩余电量(SOC)的变化几乎不引起开路电压的变化,初值为这一时期的电池进行SOC估算,误差会增大。基于SOC动态观测模型,使用UKF,PF法对SOC初值为60%的磷酸铁锂电池进行SOC估计,根据电池放电试验所获得数据进行仿真,并辅以初值不在平台期的电池放电试验,结果显示在平台期PF法鲁棒性很差,其余区域PF法则可迅速收敛,得到精准的估计,而UKF法在每个区域都相对稳定。在实际应用中应结合这两种算法的优点,这样才能在电池放电过程中得到精确的估值。
For the open-circuit voltage(OCV) of lithium battery during the plateau,the variation of the state of charge(SOC)may change the OCV hardly. If the initial value of a battery is used to estimate its SOC during the plateau,the estimation error may be increased. On the basis of SOC dynamic observation model,the UKF and PF methods are adopted to estimate the SOC by taking the 60% lithium-iron phosphate battery as the initial value of SOC,and perform the simulation according to the data obtained with battery discharge test. The battery discharge test was performed when the initial value was out of the plateau. The results show that the PF method has poor robustness during the plateau and can converge quickly in other regions for accurate estimation,but the UKF method is relatively stable at each region. The advantages of the two methods should be combined in practical application to obtain the accurate evaluation in the process of battery discharge.
For the open-circuit voltage(OCV) of lithium battery during the plateau,the variation of the state of charge(SOC)may change the OCV hardly. If the initial value of a battery is used to estimate its SOC during the plateau,the estimation error may be increased. On the basis of SOC dynamic observation model,the UKF and PF methods are adopted to estimate the SOC by taking the 60% lithium-iron phosphate battery as the initial value of SOC,and perform the simulation according to the data obtained with battery discharge test. The battery discharge test was performed when the initial value was out of the plateau. The results show that the PF method has poor robustness during the plateau and can converge quickly in other regions for accurate estimation,but the UKF method is relatively stable at each region. The advantages of the two methods should be combined in practical application to obtain the accurate evaluation in the process of battery discharge.
引文
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[10]NIRI E D,SINGH T.Unscented transformation based estimation of parameters of nonlinear models using heteroscedastic data[J].Pattern recognition,2016,55:160-171.
[11]汪永志,贝绍轶,汪伟,等.基于粒子滤波算法的动力电池SOC估计[J].机械设计与制造工程,2014(10):69-73.
[2]KONG S K,MOO C S,CHEN Y P,et al.Enhanced coulomb counting method for estimating state-of-charge and state-ofhealth of lithium-ion batteries[J].Applied energy,2009,86(9):1506-1511.
[3]PILLER S,PERRIN M,JOSSEN A.Methods for state-ofcharge determination and their applications[J].Journal of power sources,2001,96(1):113-120.
[4]谭晓军.电动汽车动力电池管理系统设计[M].广州:中山大学出版社,2011:2-3.
[5]赵又群,周晓凤,刘英杰.基于扩展卡尔曼粒子滤波算法的锂电池SOC估计[J].中国机械工程,2015(3):394-397.
[6]SHAO S,BI J,YANG F,et al.On-line estimation of state-ofcharge of Li-ion batteries in electric vehicle using the resampling particle filter[J].Transportation research part D:transport and environment,2014,32:207-217.
[7]HU X,LI S,PENG H,et al.Robustness analysis of state-ofcharge estimation methods for two types of Li-ion batteries[J].Journal of power sources,2012,217(11):209-219.
[8]高金辉,朱元培,刘永.用一种新的优化算法估计电动汽车电池SOC[J].电源技术,2014(1):75-77.
[9]江莉,李永富.精确测量蓄电池内阻方法的研究[J].电源世界,2006(6):28-29.
[10]NIRI E D,SINGH T.Unscented transformation based estimation of parameters of nonlinear models using heteroscedastic data[J].Pattern recognition,2016,55:160-171.
[11]汪永志,贝绍轶,汪伟,等.基于粒子滤波算法的动力电池SOC估计[J].机械设计与制造工程,2014(10):69-73.