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Energies 2015, 8(6), 5916-5936; doi:10.3390/en8065916

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

1
Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
2
College of Optoelectronics Engineering, Shenzhen University, Shenzhen 518060, China
3
Sunwoda Electronic Co. Ltd., Shenzhen 518108, China
*
Author to whom correspondence should be addressed.
Academic Editor: Haolin Tang
Received: 20 May 2015 / Revised: 5 June 2015 / Accepted: 9 June 2015 / Published: 17 June 2015
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Abstract

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. View Full-Text
Keywords: Adaptive Cubature Kalman filter; state of charge; lithium-ion battery; electric vehicle Adaptive 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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MDPI and ACS Style

Xia, B.; Wang, H.; Tian, Y.; Wang, M.; Sun, W.; Xu, Z. State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter. Energies 2015, 8, 5916-5936.

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