基于变参数模型的锂电池荷电状态观测方法(英文)
Li-ion batteries state-of-charge observation method based on model with variable parameters

锂电池荷电状态（State of Charge, SOC）观测技术作为电池管理系统（Battery Management System,BMS）的关键技术，在维持电池系统设备安全高效运作、延长电池组整体生命周期等方面均起着不可或缺的作用。本文对锂离子电池荷电状态的观测方法进行了研究，基于二阶变参数锂电池模型，设计了一种有效的改善SOC 观测精度的方法。首先，根据SOC 的定义，建立了安时积分估计（Amper-Hour integral estimator, AH），通过引入二阶变参数锂电池模型建立扩展卡尔曼滤波估计器（Extended Kalman Filter estimator, EKF），然后结合Takagi-Sugeno模糊模型原理，设计Takagi-Sugeno和EKF联合估计器（Takagi-Sugeno and Extended Kalman Filter union estimator, TS-EKF）。最后，在Simulink 仿真平台上验证了SOC 观测方法的准确性和实用性。结果表明，本文所设计的Takagi-Sugeno和EKF联合估计器可以提高SOC观测精度。
The observation technology of the battery state of charge (SOC) plays an indispensable role in maintaining the safety and high efficiency of the battery manage system (BMS) and prolonging the battery’s life period etc.. In this paper, the observation method of the Li-ion battery’s SOC is carried on aiming at the current problems of the low precision of the SOC observation results, an accurate and efficient SOC observation method is designed for Li-ion battery based on a 2nd-order RC model with variable parameters. Firstly, the A Amper-Hour (AH) integral estimator is built according to the definition of SOC, and then the Extended Kalman Filter (EKF) estimator is established by introducing the EKF principle; Then, combined with the Takagi-Sugeno fuzzy principle, the TS-EKF union estimator is eventually designed. Finally, the accuracy and the practicability of the SOC observation method with the core technologies are verified based on the simulation platform of Simulink