锂离子电池模型参数辨识的改进自适应FFRLS算法研究

Research on Improved Adaptive FFRLS Algorithm for Parameter Identification of Lithium-ion Battery Model

为了提高锂离子电池模型参数辨识的准确性和鲁棒性。以递归最小二乘法(FFRLS)为基础,采用了一种经过改进的自适应FFRLS算法,通过分数阶阶乘设计实现了对模型参数的逐步辨识。这种算法的特点在于其自适应性,能够更好地适应电池系统的非线性和复杂性。通过在不同工况下对锂离子电池模型进行参数辨识和研究得到了大量的实验数据。结果显示,相较于传统方法,改进的自适应FFRLS算法在处理电池系统的复杂性和非线性特性方面取得了显著的改进。通过优化算法,为电池管理系统提供了更为精确的模型。这对于改善对电池性能的监控和控制具有重要意义,有助于提高电池的使用寿命和安全性。

In order to improve the accuracy and robustness of lithium-ion battery model parameter identification.Based on the Recursive Least Squares(FFRLS)method,an improved adaptive FFRLS algorithm is adopted,and the model parameters are identified stepwise through the fractional factorial design.This algorithm is characterized by its adaptability,which can better adapt to the nonlinearity and complexity of the battery system.A large number of experimental data were obtained by identifying and studying the parameters of the lithium-ion battery model under different working conditions.The results show that compared with the traditional method,the improved adaptive FFRLS algorithm has achieved significant improvement in dealing with the complexity and nonlinear characteristics of the battery system.Through the optimization algorithm,a more accurate model is provided for the battery management system.This is of great significance for improving the monitoring and control of battery performance,helping to improve the service life and safety of the battery.