Open AccessGeneral Model and SOC Estimation of Battery
Author(s) -
Meng Li,
Haipeng Guo,
Xiaowei Zhao
Publication year - 2021
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2021.15.17
Subject(s) - kalman filter , state of charge , battery (electricity) , computer science , set (abstract data type) , state (computer science) , control theory (sociology) , noise (video) , extended kalman filter , value (mathematics) , algorithm , power (physics) , artificial intelligence , physics , control (management) , quantum mechanics , machine learning , image (mathematics) , programming language
Monitoring the battery state is of great importance for the safety and normal of the systems which are powered by batteries. SOC (State of Charge) is one of the most important state parameters of battery. SOC cannot be measured directly. The Kalman filter algorithm is one of the techniques often applied to estimate SOC value. An accurate model is necessary for this algorithm. In this paper, a general SOC model is set up. It takes into account not only the difference between discharging and charging work conditions, but also the influence of the working atmosphere, such as temperature and discharging rate. Then based on this general model, unscented Kalman filter method is used to predict the SOC value. It can avoid the error which is caused by ignoring high-order terms, which is a shortcoming exist in the extended Kalman filter method. The simulation experiments prove the approach can get satisfactory results even when the measurement data is mixed with noise or the initial SOC value is not accurate.