Open AccessState‐of‐charge estimation technique for lithium‐ion batteries by means of second‐order extended Kalman filter and equivalent circuit model: Great temperature robustness state‐of‐charge estimation
Author(s) -
Fang Yanyan,
Zhang Qiang,
Zhang Hang,
Xu Weicheng,
Wang Linshu,
Shen Xueling,
Yun Fengling,
Cui Yi,
Wang Lin,
Zhang Xin
Publication year - 2021
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/pel2.12129
Subject(s) - state of charge , extended kalman filter , equivalent circuit , control theory (sociology) , robustness (evolution) , kalman filter , lithium ion battery , battery (electricity) , algorithm , voltage , mathematics , power (physics) , computer science , engineering , chemistry , statistics , electrical engineering , physics , thermodynamics , biochemistry , control (management) , artificial intelligence , gene
Abstract The present work focuses on the state‐of‐charge (SOC) estimation of a lithium‐ion battery in terms of a second‐order extended Kalman filter (EKF). First, an equivalent circuit model is introduced to describe the performance of lithium‐ion batteries. The model parameters are then identified through hybrid pulse power characterization experiments conducted over a wide range of temperatures (−10 to 55°C). A two‐dimensional mathematical relationship is established with respect to the SOC and temperature based on a dual‐fifth polynomial expression. The main effects and sensitivities of the SOC and temperature on the parameters are analysed according to the principle of variance analysis and partial derivatives. An estimation algorithm is developed, which combines the two‐dimensional parameter model and second‐order EKF. Finally, the proposed approach is validated compared to other estimation schemes through discharge experiments under extreme temperatures and dynamic loading profiles, which yields experimental results that estimate the SOC with an absolute error of less than 4.5% under harsh conditions. This not only demonstrates that it can characterize dependency of the model parameters on the operating conditions and address the uncertainty of model parameters, but also verifies the advantage of present method at low temperatures especially at sub‐zero temperatures.