A State of Charge Estimation Approach Based on Fractional Order Adaptive Extended Kalman Filter for Lithium-ion Batteries

This paper focuses on the state of charge (SOC) estimation of a lithium-ion battery in electric vehicles (EVs) based on a fractional order adaptive extended Kalman filter (FOAEKF). First, a fractional order model (FOM) is introduced to describe the physical behavior of the battery. Then, the paramet...

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Bibliographic Details
Published in2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) pp. 271 - 276
Main Authors Xu, Mengren, Zhu, Qiao, Zheng, Mengrqian
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2018
Subjects
adaptive extended Kalman filter
Batteries
Computational modeling
Estimation
fractional order model
genetic algorithm
Genetic algorithms
Integrated circuit modeling
Kalman filters
Lithium-ion battery
State of charge
state of charge estimation
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Summary:This paper focuses on the state of charge (SOC) estimation of a lithium-ion battery in electric vehicles (EVs) based on a fractional order adaptive extended Kalman filter (FOAEKF). First, a fractional order model (FOM) is introduced to describe the physical behavior of the battery. Then, the parameters of the FOM are identified by a genetic algorithm. The efficiency of the FOM is verified by comparing with the integral order one. After that, a FOAEKF algorithm is developed to deal with the state estimation problem of the FOM. Finally, two dynamic operation conditions are given to show the efficiency of the FOAEKF by comparing with the extended Kalman filter (EKF) for FOM and the adaptive extended Kalman filter (AEKF) for integral order one.
DOI:10.1109/DDCLS.2018.8516091