Open AccessParameter identification of a lithium‐ion battery based on the improved recursive least square algorithm
Author(s) -
Ren Biying,
Xie Chenxue,
Sun Xiangdong,
Zhang Qi,
Yan Dan
Publication year - 2020
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.2019.1589
Subject(s) - recursive least squares filter , identification (biology) , algorithm , battery (electricity) , computer science , system identification , control theory (sociology) , power (physics) , data modeling , artificial intelligence , adaptive filter , control (management) , botany , physics , quantum mechanics , database , biology
Accurate parameter identification of a lithium‐ion battery is a critical basis in the battery management systems. Based on the analysis of the second‐order RC equivalent circuit model, the parameter identification process using the recursive least squares (RLS) algorithm is discussed firstly. The reason for the RLS algorithm affecting the accuracy and rapidity of model parameter identification is pointed out. And an improved RLS algorithm is proposed, an inner loop with the estimated parameter vector updated multiple times is inserted into the conventional RLS algorithm, so that the identification results are improved. The test platform of a single lithium‐ion battery is built. The experimental results show that the improved RLS algorithm has better tracking ability, smaller prediction error and has a moderate computational burden compared with the conventional RLS algorithm and a variable forgetting factor RLS algorithm.