Zheng Liu1,2, Xuanju Dang This email address is being protected from spambots. You need JavaScript enabled to view it.1, Benqin Jing1,2 and Jianbo Ji2

1School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guangxi Province, 541004, P.R. China
2School of Electronic and Automation, Guilin University of Aerospace Technology, Guangxi Province, 541004, P.R. China


Received: December 4, 2018
Accepted: September 6, 2019
Publication Date: December 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201912_22(4).0002  


Reliable model parameters identification is the key evaluation index for battery management system (BMS) in electric vehicles (EVs). To ensure the sustainability of lithium-ion battery (LIB) under unknown measurement noise, an effective LIB model with updated parameters should be developed. To soften the impact of measurement noise from the transducer, a combined equivalent circuit model (ECM) that considers the current noise as a compensation factor is introduced into the LIB. To identify the model parameters recursively based on suppression of the parameters perturbations in the ECM, a dual extended kalman filter algorithm is applied. Finally, the Dynamic Stress Test sequence (DST) and the Federal Urban Driving Schedule (FUDS) are loaded on LIB to test the validity of the improved approach. The experiment results demonstrate the effectiveness of improved model and filtering method in terms of parameters identification.

Keywords: Lithium-ion Battery, Parameters Identification, Dual Kalman Filter, Equivalent-circuit Model



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