车用锂离子电池SOC估算研究

Research on SOC Estimation of Lithium-Ion Batteries for Vehicles

电池荷电状态(SOC)作为电池管理系统(BMS)后续控制、管理、诊断中的基本参数之一,其实时精确的估算对于BMS的可靠性与准确性有着重要的影响。在SOC估算算法中,卡尔曼滤波算法凭借着估算精度高、实用性好等优点被广泛应用。卡尔曼滤波算法估算SOC主要包括3个步骤。首先介绍了电池模型,对于常用的电等效电路模型进行了详细的分析,包括Rint模型、Randles模型、nRC模型。接着,讲解了电池模型与卡尔曼滤波算法结合的2个步骤,即状态空间方程的离散化及参数辨识。然后,重点探讨和比较了几种卡尔曼算法的基本原理以及应用的不同,主要包括扩展卡尔曼滤波算法、无迹卡尔曼滤波算法和立方卡尔曼滤波算法。最后,通过对卡尔曼滤波算法在车用锂离子电池SOC估算中应用的分析,总结出了可以进一步提高SOC估算精度以及实用性的建议。

The battery state of charge(SOC)is one of the basic parameters in the subsequent control,management,and diagnosis of the battery management system(BMS).Its real-time accurate estimation have an important impact on the reliability and accuracy of the BMS.Among the SOC estimation algorithms,the Kalman filter algorithm is widely used due to its high estimation accuracy and good practicability.The Kalman filter algorithm to estimate SOC mainly includes three steps.Firstly,the battery model is introduced,and the commonly used electrical equivalent circuit models are analyzed in detail,including Rint model,Randles model,and nRC model.Secondary,the battery model and Kalman filter algorithm are expounded in two steps,i.e.the discretization and parameter identification of the state space equation.And then,the basic principles and application differences of several Kalman algorithms are discussed and compared mainly including the extended Kalman filter algorithm,no trace Kalman filter algorithm and cubic Kalman filter algorithm.Finally,through the analysis of the application for the Kalman filter algorithm in the SOC estimation of automotive lithium-ion batteries,further improve the SOC estimation accuracy and practicability are summarized.