车用动力锂电池的SOC估计

给出了动力锂电池管理系统的整体结构,并且对主控板和子控板的布局与功能进行了详尽介绍。建立了适合于Kalman滤波估计的锂离子动力电池的状态空间模型,该数学模型关系简单,易于工程实现。在此基础上,对模型进行了线性化处理,采用安时积分法、开路电压法结合扩展卡尔曼滤波(EKF)算法实现了对电池荷电状态(SOC)的准确估算。实验结果表明,EKF算法在估算过程中能保持很好的精度,对初始值的误差有很强的修正作用,在SOC估计中有很强的应用价值。

The SOC Estimation of Vehicle Power Lithium Battery

This paper proposes the management system of power lithium battery and also gives a detailed introduction to the overall structure and features of main-control board and the sub-control board.The state space model which is suitable for the establishment of lithium-ion battery by Kalman filter algorithm is estimated.The model has the advantage of simplicity and can be easily implemented.The model is linearized in estimating state of charge.The integral method,open circuit voltage method combined with extended Kalman filter(EKF) algorithm are taken for the estimation of battery state of charge(SOC).The experimental results show that EKF algorithm in the estimation process to maintain good accuracy, also have a strong role to the initial value of the error correction, so the algorithm of EKF has a strong application in the estimation of the SOC.