Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation

Yujie Wang, Mince Li and Zonghai Chen

Applied Energy, 2020, vol. 278, issue C, No S0306261920312265

Abstract: In recent years, with the promotion of new energy vehicles and micro-grids, energy storage technology has achieved considerable development. The hybridization of the lithium-ion batteries with ultra-capacitors can effectively improve the power capability of the energy storage system, which is suitable for the vehicle and micro-grid applications. High precision modeling is the foundation of the state estimation and energy management of the energy storage system. The fractional-order model provides applicable computational complexity and accuracy. This paper presents the experimental study of the fractional-order models used for lithium-ion batteries and ultra-capacitors. In this work, the experimental results of the electrochemical impedance spectroscopy of both lithium-ion batteries and ultra-capacitors are systematically analyzed. Then the temperature compensating fractional-order models of the batteries and ultra-capacitors are developed. Moreover, the particle swarm optimization based global optimization is proposed for on-line parameter identification with strict constraints. Therefore the parameters of the fractional-order models can be extracted from daily vehicle operating conditions. Finally, the accuracy of the presented model is proved by dynamic cycle conditions at different temperatures. The results indicate that the proposed method can well approximate the voltage of both the lithium-ion batteries and the ultra-capacitors with mean relative errors less than 4% and 3%, respectively.

Keywords: Fractional-order modeling; Electrochemical impedance spectroscopy; Frequency domain analysis; System Identification; Global optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.apenergy.2020.115736

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