Open AccessModel‐based optimal parameter identification incorporating C‐rate, state of charge and temperature effect for advance battery management system in electric vehicles
Author(s) -
Sangwan Venu,
Sharma Avinash,
Kumar Rajesh,
Rathore Akshay Kumar
Publication year - 2018
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.2018.0003
Subject(s) - battery (electricity) , state of charge , parametric statistics , heuristic , wilcoxon signed rank test , engineering , battery pack , polynomial , computation , convergence (economics) , automotive engineering , mathematical optimization , computer science , algorithm , mathematics , power (physics) , statistics , mathematical analysis , physics , quantum mechanics , economic growth , economics , mann–whitney u test
The battery management system in electrified transportation requires an accurate battery model for online state estimation of the battery. The parameters of the battery model depend upon state of charge, C‐rate, and temperature. A detailed battery model defined by 31 polynomial coefficients is used for determination of battery parameters. The parameter estimation is formulated as an optimisation problem and six different meta‐heuristic optimisation techniques are utilised for solving it. The efficiency of optimisation techniques is compared in terms of solution quality, computation efficiency, and convergence characteristics. Further, their performance is analysed statistically using parametric ( t ‐test) and non‐parametric tests (Wilcoxon test). The parameters values estimated by applying optimisation techniques are cross‐validated with value of parameters extracted using standard constant‐current pulse charge–discharge test to establish the effectiveness of the proposed approach.