Open AccessBattery State Estimation based on Dual Extended Kalman Filtering with Fixed Step
Author(s) -
Weihong Zang,
Facheng Wang,
Zhonghua Li,
Wei Zhou
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2200/1/012023
Subject(s) - state of charge , kalman filter , battery (electricity) , voltage , robustness (evolution) , resistor , online model , computer science , control theory (sociology) , equivalent circuit , capacitor , extended kalman filter , engineering , power (physics) , electrical engineering , mathematics , biochemistry , physics , chemistry , statistics , control (management) , quantum mechanics , artificial intelligence , gene
With the rapid popularization of new energy vehicles, users pursue reliable mileage as well as stable and efficient power battery charging and discharging performance, which puts forward higher requirements for on-board battery management system (BMS). Realizing online update of battery model parameters and accurate estimation of charged state has also become one of the key technical problems in the field of new energy vehicles at present. In this paper, based on the second-order resistor-capacitor (RC) equivalent circuit model, multiple parameters in the voltage relaxation stage were obtained through the curve relationship between open circuit voltage and state of charge, and the fast time-varying parameters were converted into parameters of different step sizes. An estimation model of state of charge based on fixed step Kalman Filtering algorithm was built by Simulink to realize online estimation of battery parameters and state of charge estimation. In this paper, a kind of ternary lithium battery was selected to establish data sets and test, which verified good robustness and high accuracy of the model.