一种锂离子电池荷电状态估计与功率预测方法

为了能够准确估计锂离子电池的荷电状态(SOC),同时对电池实际可用的最大充、放电功率进行预测,在研究电池充、放电过程中的滞回现象的基础上,建立基于电压滞回特性的二阶RC等效电路模型。为了避免因噪声统计特性造成的误差,将H∞滤波算法应用到锂离子电池的SOC估计中,减少了估计过程中的模型误差和算法误差,提高了估计的鲁棒性。将电池电压、电流和SOC的估计值作为联合约束条件预测锂离子电池实际可用的最大充、放电功率,对电池做脉冲充、放电实验,实验分析表明,与混合脉冲功率特性(HPPC)测试方法相比,联合约束算法提高了预测电池功率的准确性。

Method for Estimation of State of Charge and Power Prediction of Lithium-Ion Battery

In order to estimate the state of charge (SOC) of lithium-ion battery precisely and predict the actual maximum charge/discharge power of the battery,a second-order RC hysteresis model was established based on the hysteresis phenomena which appeared during the charge-discharge process.The H∞ filter algorithm was used to estimate SOC,which avoided the error caused by the statistical characteristics of noise.This method greatly reduces the model error and algorithm error,and improves the robustness of parameter estimation.Regard the battery voltage,current and the estimation of SOC as constraint conditions to predict the actual maximum charge-discharge power of the lithium-ion battery.Compared with the hybrid pulse power characterization (HPPC)method,the pulse charge-discharge experiments showed that the method proposed had higher accuracy in estimating the battery power.