State-of-charge (SOC) inconsistency impacts the power, durability and safety of the battery pack. Therefore, it is necessary to measure the SOC inconsistency of the battery pack with good accuracy. We explore a novel method for modeling and estimating the SOC inconsistency of lithium-ion (Li-ion) battery pack with low computation effort. In this method, a second-order RC model is selected as the cell mean model (CMM) to represent the overall performance of the battery pack. A hypothetical Rint model is employed as the cell difference model (CDM) to evaluate the SOC difference. The parameters of mean-difference model (MDM) are identified with particle swarm optimization (PSO). Subsequently, the mean SOC and the cell SOC differences are estimated by using extended Kalman filter (EKF). Finally, we conduct an experiment on a small Li-ion battery pack with twelve cells connected in series. The results show that the evaluated SOC difference is capable of tracking the changing of actual value after a quick convergence.

充电状态（SOC）不一致会影响电池组的功率，耐用性和安全性。因此，有必要高精度地测量电池组的SOC不一致性。我们探索一种新颖的方法，以较低的计算量来建模和估计锂离子（Li-ion）电池组的SOC不一致性。在这种方法中，选择二阶RC模型作为电池均值模型（CMM），以表示电池组的整体性能。假设的Rint模型用作单元差异模型（CDM），以评估SOC差异。均值差模型（MDM）的参数通过粒子群优化（PSO）进行识别。随后，通过使用扩展卡尔曼滤波器（EKF）估算平均SOC和电池SOC差异。最后，我们对一个小型的锂离子电池组进行了实验，该电池组串联了十二个电池。结果表明，评估后的SOC差异能够快速收敛后跟踪实际值的变化。