state of charge estimation of lithium-ion batteries using an adaptive cubature kalman filter

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ID: 180558
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.
Reference Key
xia2015energiesstate Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Bizhong Xia;Haiqing Wang;Yong Tian;Mingwang Wang;Wei Sun;Zhihui Xu
Journal acs combinatorial science
Year 2015
DOI
10.3390/en8065916
URL
http://www.mdpi.com/1996-1073/8/6/5916
Keywords
lithium-ion battery state of charge

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