Open AccessAn adaptive state of charge estimator for lithium‐ion batteries
Author(s) -
Ali Muhammad U.,
Khan Hafiz F.,
Masood Haris,
Kallu Karam D.,
Ibrahim Malik M.,
Zafar Amad,
Oh Semin,
Kim Sangil
Publication year - 2022
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.1141
Subject(s) - state of charge , robustness (evolution) , lithium ion battery , computer science , estimator , control theory (sociology) , initialization , kalman filter , lagrange multiplier , extended kalman filter , overcharge , algorithm , electronic engineering , battery (electricity) , mathematics , mathematical optimization , engineering , chemistry , statistics , power (physics) , biochemistry , physics , control (management) , quantum mechanics , artificial intelligence , gene , programming language
This study presents a data‐driven approach in conjunction with an adaptive extended Kalman filter (AEKF) to estimate lithium‐ion batteries' state of charge (SOC) online. The Thevenin battery model is used to evaluate the effects using battery voltage and current. The advantages of the Lagrange multiplier method are utilized to model the lithium‐ion battery. The Lagrange multiplier method continuously decreases the model error to adjust the Kalman gain of AEKF for accurate SOC estimation. Various current profiles such as hybrid pulse test, dynamic stress test, and Beijing dynamic stress test are used to verify the proposed approach's adaptability, robustness, and accuracy. It is observed that the proposed approach outperforms other methodologies (recursive least square–AEKF and forgetting factor recursive least square–AEKF) due to its high accuracy (mean average error of 0.32%). Additionally, the proposed approach exhibits robustness and high convergence speed despite deliberate erroneous initialization of parameters, thus indicating its applicability in online SOC estimation applications.