Open AccessIterative learning based model identification and state of charge estimation of lithium‐ion battery
Author(s) -
Zhu Qiao,
Chen JunXiong,
Xu MengEn,
Zou Chen
Publication year - 2019
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.2018.5427
Subject(s) - state of charge , extended kalman filter , battery (electricity) , control theory (sociology) , kalman filter , computer science , identification (biology) , algorithm , artificial intelligence , power (physics) , physics , botany , quantum mechanics , biology , control (management)
This work focuses on the accurate identification of lithium‐ion battery's non‐linear parameters by using an iterative learning method. First, the second‐order resistance‐capacitance model and its regression form of the battery are introduced. Then, when the battery repeatedly implements a discharge trial from the state of charge (SOC) 100 to 0%, an iterative learning based recursive least square (IL‐RLS) algorithm is presented to accurately identify the non‐linear parameters of the regression model. The essential idea of the IL‐RLS algorithm is to improve the current parameter estimates by learning the predictive errors of the previous trials. After that, the parameters are identified as the functions of SOC by using the IL‐RLS, which are verified by comparing with the results of the classic identification method for current pulses. As a result, an application‐oriented SOC estimation scheme is proposed, where the IL‐RLS calibrates the battery parameters offline and the classic extended Kalman filter (EKF) estimates the SOC in real‐time. Finally, based on the EKF as well as the parameters identified by the IL‐RLS, one static and three dynamic operating conditions are given to show the efficiency of the IL‐RLS, where all the SOC estimation errors are <2%.