基于SOC-OCV优化曲线与EKF的锂离子电池荷电状态全局估计

SOC-OCV曲线是锂离子电池状态估计的基础。针对传统HPPC测试法在非测试点不能描述电池非线性特性和小电流恒流放电法得到的OCV曲线精度不足等问题,提出一种基于粒子群优化算法的OCV曲线优化方法。该方法将小电流恒流放电得到的OCV曲线进行平移,以平移曲线在测试点与HPPC测试得到的OCV值之间的误差和最小为优化目标,对OCV曲线进行优化。然后,以优化OCV曲线为基础对2阶RC模型的模型参数进行辨识和模型端电压估计。结果表明:与HPPC法相比,基于优化OCV曲线的模型精度具有更高的全局精度,在低SOC区域的模型精度提高了一倍。最后,基于优化的OCV曲线和辨识的模型参数,设计扩展卡尔曼滤波算法对SOC进行全SOC区域估计。试验结果表明,基于优化OCV曲线和扩展卡尔曼滤波算法的SOC估计误差在全SOC区域上都能保持在2%以内。

An Overall Estimation of State⁃of⁃Charge Based on SOC⁃OCV Optimization Curve and EKF for Lithium⁃ion Battery

The SOC?OCV curve is the basis of the state estimation for lithium?ion battery.To solve the prob?lems of failure of the traditional HPPC test method to describe the nonlinear characteristics of the battery at non?test points and the low accuracy of the OCV curve by the small?constant?current discharge method,an OCV curve opti?mization method based on the particle swarm optimization algorithm is proposed.In this method,the OCV curve by the small?constant?current discharge method is translated,and the OCV curve is optimized by minimizing the sum of error between the translation curve at the test point and the OCV value obtained by HPPC test.Then,the model parameters of the second?order RC model are identified and the model terminal voltage is estimated based on the op?timized OCV curve.The experimental results show that the overall model accuracy based on the optimized OCV curve is higher than that based on HPPC,and the former is twice the accuracy of the latter in the low SOC region.Fi?nally,based on the optimized OCV curve and identified model parameters,an EKF algorithm is designed to esti?mate the SOC in the whole SOC region.The test results show that the SOC estimation error based on the optimized OCV curve and EKF algorithm can keep within 2%over the whole SOC region.