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。
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 battery SOC fusion estimation method,which combines multiple open circuit voltage curves with extended Kalman filter.By using discrete data between SOC and corresponding open circuit voltage,two types of open circuit voltage curves are obtained through polynomial fitting and composite function fitting with logarithmic function.Based on these two types of open circuit voltage curves and combined with extended Kalman filter,their respective SOC estimation results are obtained.The SOC estimation results are finally achieved by using weighted summation to fuse respective SOC.The effectiveness of the proposed method is verified under the Dynamic 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.
作者
韦颖
WEI Ying(Faculty of Engineering,Anhui Sanlian University,Hefei 230601,China)
出处
《东莞理工学院学报》
2024年第5期76-82,共7页
Journal of Dongguan University of Technology
基金
安徽省高校自然科学基金项目重点项目(2022AH051982,2022AH051991)
安徽省高校学科(专业)带头人培育项目(DTR2023062)。
关键词
SOC估计
锂电池
融合
多开路电压曲线
EKF
SOC estimation
lithium batteries
fusion
multiple open circuit voltage curves
EKF
