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Energies 2015, 8(12), 13458-13472; doi:10.3390/en81212378

A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter

1
Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
2
Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
*
Author to whom correspondence should be addressed.
Academic Editor: K.T. Chau
Received: 3 October 2015 / Revised: 17 November 2015 / Accepted: 18 November 2015 / Published: 26 November 2015
(This article belongs to the Collection Electric and Hybrid Vehicles Collection)
View Full-Text   |   Download PDF [3625 KB, uploaded 26 November 2015]   |  

Abstract

The estimation of state of charge (SOC) is a crucial evaluation index in a battery management system (BMS). The value of SOC indicates the remaining capacity of a battery, which provides a good guarantee of safety and reliability of battery operation. It is difficult to get an accurate value of the SOC, being one of the inner states. In this paper, a strong tracking cubature Kalman filter (STCKF) based on the cubature Kalman filter is presented to perform accurate and reliable SOC estimation. The STCKF algorithm can adjust gain matrix online by introducing fading factor to the state estimation covariance matrix. The typical second-order resistor-capacitor model is used as the battery’s equivalent circuit model to dynamically simulate characteristics of the battery. The exponential-function fitting method accomplishes the task of relevant parameters identification. Then, the developed STCKF algorithm has been introduced in detail and verified under different operation current profiles such as Dynamic Stress Test (DST) and New European Driving Cycle (NEDC). Making a comparison with extended Kalman filter (EKF) and CKF algorithm, the experimental results show the merits of the STCKF algorithm in SOC estimation accuracy and robustness. View Full-Text
Keywords: strong tracking cubature Kalman filter; state of charge; lithium-ion battery; electric vehicle strong tracking cubature Kalman filter; state of charge; lithium-ion battery; electric vehicle
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xia, B.; Wang, H.; Wang, M.; Sun, W.; Xu, Z.; Lai, Y. A New Method for State of Charge Estimation of Lithium-Ion Battery Based on Strong Tracking Cubature Kalman Filter. Energies 2015, 8, 13458-13472.

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