SOC Fusion Estimation for Lithium Batteries Based on Multiple Open Circuit Voltage Curves Combined With EKF
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作者信息
1. 安徽三联学院 工学部,安徽合肥 230601
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摘要
准确估计电池的荷电状态(state of charge,SOC)对电动汽车具有重要意义.针对单一的锂电池开路电压曲线对基于模型SOC估计方法的局限性,提出了一种应用多开路电压曲线结合扩展卡尔曼滤波的锂电池SOC融合估计方法.利用SOC与对应开路电压之间的离散数据,通过多项式拟合和含有对数函数的复合函数拟合方式,获得了两种开路电压曲线.分别基于这两种开路电压曲线并结合扩展卡尔曼滤波算法,获得了各自的SOC估计结果.利用加权求和对获得的SOC进行融合,得到最终的SOC估计结果.在动态应力测试工况和美国联邦城市驾驶工况下,验证了所提方法的有效性.两种工况下,SOC融合估计的平均绝对误差和均方根误差均出现了明显下降.
Abstract
Accurately estimating the state of charge(SOC)of batteries is of great importance for electric vehicles.To address the limitations of model-based SOC estimation methods using a single open circuit voltage curve,this paper proposes a lithium bat-tery SOC fusion estimation method,which combines multiple open circuit voltage curves with extended Kalman filter.By using dis-crete data between SOC and corresponding open circuit voltage,two types of open circuit voltage curves are obtained through polyno-mial fitting and composite function fitting with logarithmic function.Based on these two types of open circuit voltage curves and com-bined with extended Kalman filter,their respective SOC estimation results are obtained.The SOC estimation results are finally a-chieved by using weighted summation to fuse respective SOC.The effectiveness of the proposed method is verified under the Dynam-ic Stress Test and Federal Urban Driving Schedule.Under these tests,both the average absolute error and root-mean square error of SOC fusion estimation have a significant decrease.
关键词
SOC估计/锂电池/融合/多开路电压曲线/EKF
Key words
SOC estimation/lithium batteries/fusion/multiple open circuit voltage curves/EKF
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