一种基于IMM-ABSE算法的锂离子电池组SOC估算

A State-of-Charge Estimation Method for Lithium-lion Battery Pack Based on IMM-ABSE Algorithm

信号噪声干扰、电池模型对温度与老化的适应性及单体不一致性等因素直接影响电池组电荷状态(State of Charge,SOC)估算精度.为实现锂离子电池组SOC的准确估计,提出了一种使用交互多模型(Interacting Multiple Model,IMM)和自适应电池状态估计器(Adaptive Battery State Estimator,ABSE)相结合的估算方法.首先,基于电池组综合特性建立电池交互模型,通过ABSE对单体SOC进行估算并嵌入IMM模型中.然后,计算各模型的信息分配因子,并根据信息分配因子对各模型的SOC进行概率融合,得到精度较高的电池组SOC.最后,在不同温度的组合工况下,评估该算法的鲁棒性和普适性.实验结果表明,该方法适用于系统输入信号存在噪声、全气候工况和单体间存在不一致性的环境,在有效充放电期间平均误差小于2%.

Signal noise interference,adaptability of battery model to temperature and aging,and inconsistency of the battery pack are vital factors which have the influence on the accuracy of State of Charge(SOC)estimation.To estimate the SOC of battery pack accurately,this paper proposes a novel method that combines the Interacting Multiple Model(IMM)and the Adaptive Battery State Estimator(ABSE).Firstly,the battery interaction models are established based on the comprehensive characteristics of the battery pack.The SOC is estimated by ABSE and embedded in the IMM model.Then,the information distribution factors of each model are calculated,and the SOC of each model is probabilistically fused according to the information distribution factors to obtain a battery pack SOC with higher precision.Finally,the robustness and universality of the algorithm are evaluated under combined conditions of different temperature.The experimental results show that this method is effective for various conditions including the input signals with noise,complicated condition under the whole climate,and inconsistency between batteries.The estimation error can be controlled within the range of 2%during effective charging and discharging cycles.