Open AccessTemperature dependent state‐of‐charge estimation of lithium ion battery using dual spherical unscented Kalman filter
Author(s) -
Aung Htet,
Low Kay Soon
Publication year - 2015
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2014.0863
Subject(s) - kalman filter , state of charge , battery (electricity) , control theory (sociology) , extended kalman filter , unscented transform , mean squared error , lithium ion battery , computer science , fast kalman filter , engineering , mathematics , statistics , power (physics) , physics , artificial intelligence , control (management) , quantum mechanics
Accurate and reliable state‐of‐charge (SOC) estimation is an important task for battery management system in a satellite. Ambient temperature is one of the significant factors that affect SOC estimation. Since satellite operates at different temperatures throughout the orbit, it must be taken care of accordingly to safeguard the battery performance and reliability. Moreover, SOC estimation depends on battery model accuracy as well. The battery parameters are affected by temperature, SOC, charging and discharging rates. Hence, the parameters need to be updated accordingly to improve the battery model and the SOC estimation accuracy. In this study, a SOC estimation method and online parameter updating using a dual square root unscented Kalman filter based on unit spherical unscented transform is proposed. The proposed method has been validated experimentally and the results are compared with extended Kalman filter and unscented Kalman filter based on unit spherical unscented transform. Experimental results have shown that the proposed method has better performance in terms of lower root mean square error and absolute maximum error.