基于剩余充电电量的锂离子电池组容量快速估计方法

针对传统锂离子电池组容量确定方法存在的效率低、能耗高且只能离线应用等问题,提出一种基于电池剩余充电电量的锂离子电池组容量快速估计方法。首先,基于充电电压曲线一致性原理,以电池组内率先充电至充电截止电压的电池单体电压曲线为基准,通过电压曲线的平移缩放与线性插值计算出各单体电池的剩余充电电量与剩余充电时间,从而实现各单体电池的荷电状态(State of Charge, SOC)在线估计,在此基础上实现电池组容量的快速估计。其次,在电池单体模型的基础上建立电池组的仿真模型,并在全SOC区域上对模型参数进行分段辨识。通过所建立的仿真模型得到电池组的充放电曲线,并对电池组容量进行估计。最后,对4个单体串联而成的电池组进行充电试验。研究结果表明:仿真容量与估计容量误差为1.2%以内,验证了所提出的容量快速估计算法的有效性;利用所提方法估计出电池组容量与试验得到的电池组容量的误差为2.61%;该方法根据电池充电曲线的平移与缩放即可在线估计出电池组容量,可应用于新电池组容量的在线快速估计,能在保证3%估计误差的基础上将检测效率提高到传统方法的2倍以上。

A Rapid Capacity-estimation Method of Lithium-ion Battery Pack Based on the Remaining Charge Capacity

Aiming at the problems of low efficiency,high energy consumption,and offline application of traditional methods for determining the capacity of lithium-ion battery (LIB) packs,a fast estimation method of the capacity of LIB packs based on the remaining battery charge capacity is proposed in this study.First,based on the principle of consistency of the charging voltage curve,the battery cell that is first charged to the charging cutoff voltage in the pack is selected as the benchmark.The remaining charging capacity and charging time of each cell are then calculated through the translation,scaling,and linear interpolation of the voltage curves,and the online fast SOC estimation of each cell is achieved.On this basis,a fast estimation of LIB pack capacity was realized.Subsequently,a simulation model of the LIB pack was established based on the cell model,and the model parameters were identified in the entire SOC region.The charge-discharge curve of a battery pack was obtained using the established simulation model,and the capacity of the battery pack was estimated.Finally,a charging experiment of a battery pack composed of four cells in series was carried out.The results show that the error between capacity simulation and capacity estimation is controlled by 1.2%,which verifies the effectiveness of the proposed fast capacity estimation algorithm.The capacity of the battery pack is estimated by the proposed method compared with the battery pack capacity obtained by the experiments,and the estimation error is 2.61%.This method can estimate the battery pack capacity online according to the translation and scaling of the battery charging curves and can be applied to the rapid online estimation of the capacity of a new battery pack.It can improve the detection efficiency to more than twice that of the traditional method and ensure a 3% estimation error.