Research on SOC Estimation Precision of Power Battery based on Extended Kalman Filtering Algorithm 

Qing ZHANG, Yun ZHAO, Bingqing XIE

College of Electromechanical and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, 400074, CHINA 

Abstract: The state of charge (SOC) is an important parameter index of the reactive power battery. With the world's environmental pollution and the lack of oil resources, electric vehicles is developing rapidly. Improv-ing the efficiency and service life of the power battery is based on an advanced SOC estimation model and algorithm. In this paper, the establishment of second - order RC dynamic network model took the ideal vehicle battery - lithium battery as an example, through the pulse test method to explore the relationship between the voltage of the open circuit(OCV), RC polarized resistance ｏｒ other model parameters and the working environmental temperature, its own charge state, etc. At the same time, the Extended Kalman Filtering Algorithm is introduced to establish the state equation and the measurement equation respectively, taking the Coulomb coefficient and the Noise figure into account. This paper have provided an optimal control strategy of the BMS to ensure the battery life, optimize driving, and improve the "bottleneck" problem of battery research.

Keywords: SOC estimation; EKF algorithm; Second-Order RC dynamic network model; BMS control system