The accurate prediction of state of charge (SOC) is indispensable in the battery management system (BMS). Herein, a new gas–liquid dynamics (GLD) battery model based on the gas–liquid system is proposed to estimate SOC precisely and reliably for the lithium-ion battery (LIB). Electron transmission process, terminal voltage lag, lithium-ion diffusion and balance, as well as the ohmic resistance effect of LIBs can be clearly embodied in this model. Concurrently, the presented SOC estimator neither couples intelligent algorithms nor involves complicated matrix operations to guarantee the real-time performance of the online estimation. The genetic algorithm (GA) is adopted to identify model parameters. The estimation results of GLD model show the maximum errors of 1.74%, 3.02% and 2% under the Dynamic Stress Test (DST) cycle, the Urban Dynamometer Driving Schedule (UDDS) and constant current discharging test at 0.6–1.8C with the SOC reducing from 100% to 0, respectively. This model has the merits of simple structure, high-precision and strong robustness.

在电池管理系统（BMS）中，准确预测充电状态（SOC）是必不可少的。在此，提出了一种基于气液系统的新型气液动力学（GLD）电池模型，以精确可靠地估算锂离子电池（LIB）的SOC。该模型可以清楚地体现出电子传输过程，端电压滞后，锂离子扩散和平衡以及LIB的欧姆电阻效应。同时，提出的SOC估计器既不耦合智能算法，也不涉及复杂的矩阵运算以保证在线估计的实时性。采用遗传算法（GA）识别模型参数。GLD模型的估计结果显示，在动态应力测试（DST）循环下，最大误差为1.74％，3.02％和2％，城市测功机行驶时间表（UDDS）和0.6–1.8C恒流放电测试，SOC分别从100％降低至0。该模型结构简单，精度高，鲁棒性强。