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Open AccessArticle

State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

1
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
2
Chongqing Datang International Wulong Hydropower Development Co., Ltd., Wulong 408500, Chongqing, China
*
Author to whom correspondence should be addressed.
Inventions 2019, 4(4), 66; https://doi.org/10.3390/inventions4040066
Received: 12 July 2019 / Revised: 23 August 2019 / Accepted: 25 October 2019 / Published: 1 November 2019
(This article belongs to the Special Issue Innovative Battery Systems and Energy Storage)
The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.
Keywords: composite battery model; state of charge; improved extended Kalman filter; state of charge estimation composite battery model; state of charge; improved extended Kalman filter; state of charge estimation
MDPI and ACS Style

Ding, N.; Prasad, K.; Lie, T.T.; Cui, J. State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm. Inventions 2019, 4, 66.

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