摘要
电池荷电状态估计是电池管理系统中的关键部分,针对这一问题,首先引入滞后模型对传统的电池等效电路模型进行改进,以便更好地模拟电池的滞后特性,提高模型的精度,再根据实验数据结合状态子空间辨识与最小二乘法对电池参数进行辨识,得到了使用的电池模型;在该模型基础上分别应用了扩展卡尔曼滤波和采样点卡尔曼滤波两种算法对电池SOC的估计效果及在估计精度、计算复杂度和鲁棒性等方面进行了比较,最终得出更适合该模型的SOC估计算法。
The estimation of state of charge of the battery is the key part of the battery management system. In order to solve this problem, the hysteresis model was introduced to improve the traditional battery equivalent circuit model, so as to simulate the hysteresis characteristics of the battery better and improve the precision of the model, and then based on the experimental data, the model parameters were identified combined with the state subspace identification and least squares method, obtaining the battery model. On the basis of this model, two algorithms of extended Kalman filter and Sampling-Point Kalman filter were used to estimate the battery SOC , and the estimation accuracy, computational complexity and robustness were compared, eventually obtaining the SOC estimation algorithmmore suitable for the model.
作者
齐志佳
袁学庆
李晓鹏
QI Zhi-jia;YUAN Xue-qing;LI Xiao-peng(Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang Liaoning 110016, China;Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang Liaoning 110016, China;University of Chinese Academy of Sciences, Beijing 110049, China)
出处
《电源技术》
CAS
北大核心
2019年第8期1300-1304,共5页
Chinese Journal of Power Sources
