基于改进遗传算法的锂离子电池ECM模型参数辨识

Parameter identification of lithium-ion battery ECM model based on improved genetic algorithm

可靠的动力电池等效电路(Equivalent Circuit Model, ECM)模型是电动汽车电池管理系统重要的技术支撑,而等效电路模型的精确性很大程度上取决于参数辨识精度。以容量3.5 A·h的三元锂离子电池为研究对象,采用双极化ECM模型构建锂离子电池模型,在此基础上提出一种基于改进遗传算法的参数识别方法,对模型中的参数进行辨识;基于所辨识的参数建立电池仿真模型,分别采用变流和恒流工况验证所构建的电池模型。结果表明,误差均控制在±0.02 V以内,所提出的改进遗传算法参数辨识方法具有较高的精度。

The reliable power battery equivalent circuit (Equivalent Circuit Model, ECM) model is an important technical support for EV battery management system, and the accuracy of the ECM largely depends on the accuracy of parameter identification. Taking 3.5 A·h ternary lithium-ion battery as the research object, a dual-polarization ECM model was used to construct a lithium-ion battery model. On this basis, a parameter identification method based on improved genetic algorithm was proposed, and the parameters in the model were effectively identified. The battery simulation model was established based on the identified parameters, and the constructed battery model was verified by variable current and constant current conditions respectively, and the error was less than ±0.02 V. The results showed that the improved genetic algorithm parameter identification method had high precision.