采用多模模型的锂离子电池荷电状态联合估计算法

Joint Estimation Algorithm for State of Charge of Li-Ion Battery with Multi-Mode Model

针对单一的等效电路模型难以准确描述全时段的锂离子电池、估计电池荷电状态(SOC)准确度低的问题,提出采用多模模型的锂离子电池荷电状态联合估计算法。利用电化学阻抗谱分析不同SOC下锂离子电池的阻抗分布,并以此构建等效电路模型来描述整个充放电过程中的锂离子电池,得到一种基于变阶RC模型的多模模型。利用贝叶斯定阶准则综合模型的准确度和实用性来确定具体阶数,采用带有遗忘因子的递推最小二乘法对模型参数进行在线辨识,利用扩展卡尔曼滤波算法(EKF)求得锂离子电池的实时SOC。在恒流工况以及动态应力测试工况下,与传统基于一阶RC模型和二阶RC模型的EKF算法进行了多组实验对比。结果表明:采用多模模型的联合算法在不同工况下估计的SOC精度提高了30%以上,并均可在两个迭代周期内追踪到准确值。

Aiming at the problem that a single equivalent circuit model is difficult to accurately describe the full-time lithium-ion battery and the low accuracy of battery state of charge(SOC)estimation,a joint estimation algorithm of lithium-ion battery state of charge with a multi-mode model is proposed.Electrochemical impedance spectroscopy is used to analyze the impedance distribution of lithium-ion batteries under different SOC,and an equivalent circuit model is constructed to describe the lithium-ion batteries during the entire charging and discharging process.A multi-mode model based on the variable-order RC model is obtained.The accuracy and practicability of the Bayesian order criterion are adopted to determine the specific order,the recursive least square method with forgetting factor is chosen to identify the model parameters online,and the extended Kalman filter algorithm(EKF)is adopted to obtain real-time SOC of Li-ion battery.Under constant current conditions and dynamic stress test conditions,a multi-group experimental comparison with the traditional EKF algorithm based on the first-order RC model and the second-order RC model is carried out.The results show that the joint algorithm with the multi-mode model works well under different conditions.The accuracy of the estimated SOC is improved by more than 30%,and the accurate value can be tracked within two iteration cycles.