Parameter identification and state-of-charge estimation approach for enhanced lithium–ion battery equivalent circuit model considering influence of ambient temperatures  

作　　者：Hui Pang Lian-Jing Mou Long Guo 庞辉;牟联晶;郭龙(School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology)

机构地区：[1]School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology

出　　处：《Chinese Physics B》2019年第10期562-570,共9页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China(Grant No.51675423)

摘　　要：It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge(SOC) estimation, and the accurate SOC estimation is a significant issue for developing the battery management system in electric vehicles. To address this problem, in this paper we propose an enhanced equivalent circuit model(ECM) considering the influence of different ambient temperatures on the open-circuit voltage for a lithium-ion battery. Based on this model, the exponential-function fitting method is adopted to identify the battery parameters according to the test data collected from the experimental platform. And then, the extended Kalman filter(EKF) algorithm is employed to estimate the battery SOC of this battery ECM. The performance of the proposed ECM is verified by using the test profiles of hybrid pulse power characterization(HPPC) and the standard US06 driving cycles(US06) at various ambient temperatures, and by comparing with the common ECM with a second-order resistance capacitor. The simulation and experimental results show that the enhanced battery ECM can improve the battery SOC estimation accuracy under different operating conditions.It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge(SOC) estimation, and the accurate SOC estimation is a significant issue for developing the battery management system in electric vehicles. To address this problem, in this paper we propose an enhanced equivalent circuit model(ECM) considering the influence of different ambient temperatures on the open-circuit voltage for a lithium–ion battery. Based on this model, the exponential-function fitting method is adopted to identify the battery parameters according to the test data collected from the experimental platform. And then, the extended Kalman filter(EKF) algorithm is employed to estimate the battery SOC of this battery ECM. The performance of the proposed ECM is verified by using the test profiles of hybrid pulse power characterization(HPPC) and the standard US06 driving cycles(US06) at various ambient temperatures, and by comparing with the common ECM with a second-order resistance capacitor. The simulation and experimental results show that the enhanced battery ECM can improve the battery SOC estimation accuracy under different operating conditions.

关 键 词：LITHIUM-ION BATTERY parameter identification state of CHARGE AMBIENT temperature 

分 类 号：O4[理学—物理]