基于多新息CDKF算法的锂电池SOC估计

SOC estimation for lithium-ion batteries based on multi-innovation central difference Kalman filter algorithm

锂电池荷电状态(SOC)作为电池管理系统的核心指标之一,对电池管理系统运行起到至关重要的作用,其估计性能和鲁棒性是研究的重点。为提升SOC估计性能,建立二阶RC等效电路模型,将多新息理论(MI)与中心差分卡尔曼滤波算法(CDKF)结合,提出一种基于多新息的中心差分卡尔曼滤波算法(MI-CDKF),充分考虑当前时刻新息与历史信息。仿真结果表明,所提MI-CDKF与传统方法相比,在收敛速度和估计精度上均有所提升,同时对传感器漂移现象具有一定鲁棒性。

As one of the core indicators of battery management system,the state of charge(SOC)of lithium batteries plays a vital role in the operation of battery management system.Its estimation performance and robustness are the focus of research.To promote SOC estimation performance,a second-order RC equivalent circuit model was established.By combining the multi-innovation theory(MI)with the central difference Kalman filter,a new central difference Kalman filter(MI-CDKF)algorithm based on the multi-innovation theory(MI)was proposed,which fully considered the current innovation and the historical information.The simulation results show that compared with the traditional methods,the proposed MI-CDKF has improved convergence speed and estimation accuracy.In the meanwhile,it also has certain robustness against sensor drift phenomenon.