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Open AccessArticle

A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

1
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
3
The State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Energies 2017, 10(9), 1313; https://doi.org/10.3390/en10091313
Received: 18 July 2017 / Revised: 19 August 2017 / Accepted: 26 August 2017 / Published: 1 September 2017
An accurate state of charge (SOC) estimation is the basis of the Battery Management System (BMS). In this paper, a new estimation method which considers fractional calculus is proposed to estimate the lithium battery state of charge. Firstly, a modified second-order RC model based on fractional calculus theory is developed to model the lithium battery characteristics. After that, a pulse characterization test is implemented to obtain the battery terminal voltage and current, in which the parameter identification is completed based on least square method. Furthermore, the proposed method based on Fractional Unscented Kalman Filter (FUKF) algorithm is applied to estimate the battery state of charge value in both static and dynamic battery discharging experiment. The experimental results have demonstrated that the proposed method shows high accuracy and efficiency for state of charge estimation and the fractional calculus contributes to the battery state of charge estimation. View Full-Text
Keywords: second-order RC circuit model; fractional calculus; Fractional Unscented Kalman Filter; SOC estimation second-order RC circuit model; fractional calculus; Fractional Unscented Kalman Filter; SOC estimation
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Chen, Y.; Huang, D.; Zhu, Q.; Liu, W.; Liu, C.; Xiong, N. A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter. Energies 2017, 10, 1313.

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