Open AccessState‐of‐charge and state‐of‐health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries
Author(s) -
Malysz Pawel,
Gu Ran,
Ye Jin,
Yang Hong,
Emadi Ali
Publication year - 2016
Publication title -
iet electrical systems in transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.588
H-Index - 26
eISSN - 2042-9746
pISSN - 2042-9738
DOI - 10.1049/iet-est.2015.0030
Subject(s) - kalman filter , state of charge , electric vehicle , battery (electricity) , state (computer science) , compensation (psychology) , extended kalman filter , control theory (sociology) , powertrain , state of health , current (fluid) , driving cycle , engineering , current sensor , estimation , computer science , algorithm , electrical engineering , torque , power (physics) , physics , artificial intelligence , control (management) , systems engineering , quantum mechanics , thermodynamics , psychology , psychoanalysis
Pragmatic approaches are proposed to enhance battery state estimation using Kalman filter (KF) and extended KF. Notable novelties introduced include: the use of state/parameter constraints, asymmetric equivalent circuit model behaviour, inclusion of nominal models, and current sensor measurement bias estimation and compensation. The so‐called delta parameters are estimated to handle cell variations, aging, and online deviation of parameters. Strategic simplifications that enable the use of traditional KF algorithm are described. Unique filter structures are presented for state‐of‐charge and state‐of‐health estimation, the latter focuses on capacity and impedance estimation. The performance of the proposed approaches is demonstrated on experimental drive‐cycle data designed for electric vehicle (EV) and hybrid EV applications.