锂离子电池属于刚性系统，呈现出丰富的非线性动力学特性和高度复杂性。其等效电路模型参数及SOC状态量随着电池的使用呈现出慢时变特性，利用常规的SOC估计方法通常存在精确度低、时效性差等不足。将电荷控制型忆阻器与一阶RC模型结合建立四阶混沌系统，利用状态观测器在线辨识混沌系统中的未知参数，实时获取一阶RC模型参数值；利用参数在线辨识值建立准确的一阶RC模型数学表达式，然后采用AEKF和SVR两个模型分别实时估计SOC时间序列，获得两个模型的SOC估计值；最后利用LSTM模型非线性组合AEKF和SVR的估计值，获得最终的锂离子电池SOC估计值。试验结果显示：非线性组合估计模型能实时准确地估计锂电池SOC，表明提出的非线性组合模型具有较优的非线性动态估计能力、较高的精确度及泛化能力。

 

Because lithium-ion battery is a rigid system, it presents rich nonlinear dynamic characteristics and high complexity. The parameters of the equivalent circuit model and SOC state show time-varying slowly with the use of the battery, and the SOC estimation method of using the gauge usually has some shortcomings such as low accuracy and poor timeliness. The charge control memristor was connected to a first-order RC model as a load to establish a fourth-order chaotic system. The unknown parameters of fourth-order chaotic system were identified online by using a state observer, and the R0, R1 and C1 values of first order RC model parameters were obtained in real time. The mathematical expression of the accurate first-order RC model was established by using the online parameter identification values. Then AEKF and SVR models were used to estimate SOC time series in real time, and SOC estimated values of the two models were obtained. Using the LSTM model nonlinear combination of AEKF and SVR model estimated values, the final lithium-ion battery SOC estimated values were finally obtained. The experimental results show that the nonlinear combined estimation model can accurately estimate SOC in real time, which indicates that the proposed nonlinear combined estimation model has better nonlinear dynamic estimation ability, higher accuracy and generalization ability.