Open AccessDesign of adaptive H ∞ filter for implementing on state‐of‐charge estimation based on battery state‐of‐charge‐varying modelling
Author(s) -
Charkhgard Mohammad,
Zarif Mohammad Haddad
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.2014.0523
Subject(s) - state of charge , charge (physics) , state (computer science) , battery (electricity) , estimation , computer science , filter (signal processing) , control theory (sociology) , engineering , physics , electrical engineering , algorithm , artificial intelligence , quantum mechanics , systems engineering , power (physics) , control (management)
This study suggests a new method for modelling lithium‐ion battery types and state‐of‐charge (SOC) estimation using adaptive H ∞ filter (AHF). First, a universal linear model with some free parameters is considered for dynamical behaviour of the battery. The battery voltage and SOC are used as states of the model. Then for every period in the charge/discharge process the free parameters of the model are identified. Each period of process is associated with a specific SOC value, hence the parameters can be regarded as functions of SOC in the entire process. The functions are determined based on polynomial approximation and least squares method. The proposed SOC‐varying model is incorporated in AHF for SOC estimation. Moreover, a new method for adjusting the tuning parameters of the filter is suggested. The proposed method is verified by experimental tests on a lithium‐ion battery and is compared with adaptive extended Kalman filter and square‐root unscented Kalman filter