基于变参数模型的锂电池荷电状态观测方法（英文）

锂电池荷电状态(SOC)观测技术作为电池管理系统(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.本文以改善锂电池荷电状态的观测结果为目的,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(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(EKF),然后结合Takagi-Sugeno模糊模型原理,设计Takagi-Sugeno和EKF联合估计器(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联合估计器可以改善SOC观测精度.

Li-ion batteries state-of-charge observation method based on model with variable parameters

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 improving the SOC observation results, an accurate and efficient SOC observation method is designed for Li-ion battery based on a 2nd-order resistor-capacitor (RC) model with variable parameters. Firstly, the 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 (TS) fuzzy principle, the Takagi-Sugeno and extended Kalman filter union estimator (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. The results show that TS–EKF union estimator can improve the observation accuracy of SOC.