Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model

Ming Cai, Weijie Chen and Xiaojun Tan
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Ming Cai: School of Engineering, Sun Yat-sen University, 135 Xingang Xi Road, Haizhu District, Guangzhou 510275, China
Weijie Chen: School of Engineering, Sun Yat-sen University, 135 Xingang Xi Road, Haizhu District, Guangzhou 510275, China
Xiaojun Tan: School of Engineering, Sun Yat-sen University, 135 Xingang Xi Road, Haizhu District, Guangzhou 510275, China

Energies, 2017, vol. 10, issue 10, 1-16

Abstract: State-of-charge (SOC) estimation is essential for the safe and effective utilization of lithium-ion batteries. As the SOC cannot be directly measured by sensors, an accurate battery model and a corresponding estimation method is needed. Compared with electrochemical models, the equivalent circuit models are widely used due to their simplicity and feasibility. However, such integer order-based models are not sufficient to simulate the key behavior of the battery, and therefore, their accuracy is limited. In this paper, a new model with fractional order elements is presented. The fractional order values are adaptively updated over time. For battery SOC estimation, an unscented fractional Kalman filter (UFKF) is employed based on the proposed model. Furthermore, a dual estimation scheme is designed to estimate the variable orders simultaneously. The accuracy of the proposed model is verified under different dynamic profiles, and the experimental results indicate the stability and accuracy of the estimation method.

Keywords: state-of-charge; unscented Kalman filter; fractional order modeling; online estimation; lithium-ion battery (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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