动力型锂电池SOC与SOH协同估计

锂电池及其应用近年来逐渐成为研究热点。以提高电池管理系统(BMS)对电池荷电状态(SOC)和健康状态(SOH)的估算精确度为目标，在建立二阶Thevenin等效电路模型基础上提出一种能在线协同估算电池荷电状态和健康状态的改进扩展卡尔曼滤波算法。通过分阶段脉冲放电实验，并利用最小二乘法求得模型参数。在动态应力测试工况(DST)下借助Matlab对比分析了改进扩展卡尔曼算法在SOC和SOH估计精确度、错误初值时算法收敛性、算法复杂度等方面的性能。实验表明，利用该算法可以精确估计出各采样点处的SOC和SOH，误差低于1%；且在初值不准确情况下，运行算法可快速收敛至真值附近，算法估算结果的准确性与模型参数的微调无关，鲁棒性较好。

Cooperative estimation of SOC and SOH for power lithium-ion batteries

Lithium battery and its application have gradually become a research hotspot in recent years. In order to improve the estimation accuracy of Battery Management System(BMS) for battery State Of Charge(SOC) and State Of Health(SOH), an improved extended Kalman filter algorithm for online collaborative estimation of battery state of charge and health is proposed based on the establishment of the second-order Thevenin equivalent circuit model. The model parameters are obtained by the least squares method through a staged pulse discharge experiment. The performance of improved extended Kalman algorithm in SOC and SOH estimation accuracy, convergence of algorithm and complexity of algorithm under Dynamic Stress Test(DST) conditions are compared and analyzed by Matlab. Experiments show that the improved extended Kalman filter algorithm can accurately estimate SOC and SOH at each sampling point, with an error about 1%. Moreover, when the initial value is not accurate, the algorithm can rapidly converge to the true value with good robustness, and the accuracy of the algorithm is almost unaffected by the fine-tuning of model parameters.