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Modeling of battery dynamics and hysteresis for power delivery prediction and SOC estimation

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  • Zhao, Xin
  • de Callafon, Raymond A.

Abstract

A modeling approach for battery as an Electrical Energy Storage System is proposed in this paper. The model aims to predict non-linear power delivery dynamics, given charge and discharge demand as a controllable input, not only in normal operating range of batteries, but also in extreme cases such as battery over-charging. In order to achieve that, the model is composed of separated voltage and current models. Several non-linear models, including Hammerstein model, non-linear open-circuit voltage characteristics, and Takacs hysteresis model are combined in the voltage and the current model, respectively. The state of charge of the battery can also be estimated in a recursive optimization fashion by the model. The parameterization and estimation methods of the model are described and also demonstrated on experimental data from a lithium iron phosphate (LiFePO4) cell. The experiment validation shows excellent agreement between measured and simulated voltage and current signals provided by the model during both normal operating and over-charging conditions. The contribution of this paper is given by the unique combination of data-based models used to capture linear dynamics, static non-linearity, and non-linear hysteresis effects in a single dynamic voltage/current model to simulate and predict the non-linear dynamic behavior of a battery as an energy storage/delivery system.

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  • Zhao, Xin & de Callafon, Raymond A., 2016. "Modeling of battery dynamics and hysteresis for power delivery prediction and SOC estimation," Applied Energy, Elsevier, vol. 180(C), pages 823-833.
    • Handle: RePEc:eee:appene:v:180:y:2016:i:c:p:823-833
    DOI: 10.1016/j.apenergy.2016.08.044
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    References listed on IDEAS

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    1. Yang, Fangfang & Xing, Yinjiao & Wang, Dong & Tsui, Kwok-Leung, 2016. "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Elsevier, vol. 164(C), pages 387-399.
    2. Yunfeng Jiang & Xin Zhao & Amir Valibeygi & Raymond A. De Callafon, 2016. "Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery," Energies, MDPI, vol. 9(8), pages 1-17, July.
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    Citations

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    Cited by:

    1. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.
    2. Wang, Limei & Sun, Jingjing & Cai, Yingfeng & Lian, Yubo & Jin, Mengjie & Zhao, Xiuliang & Wang, Ruochen & Chen, Long & Chen, Jun, 2023. "A novel OCV curve reconstruction and update method of lithium-ion batteries at different temperatures based on cloud data," Energy, Elsevier, vol. 268(C).
    3. Liu, Yuanzhi & Zhang, Jie, 2020. "Self-adapting J-type air-based battery thermal management system via model predictive control," Applied Energy, Elsevier, vol. 263(C).
    4. Jiang, Yunfeng & Xia, Bing & Zhao, Xin & Nguyen, Truong & Mi, Chris & de Callafon, Raymond A., 2017. "Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery," Energy, Elsevier, vol. 135(C), pages 171-181.

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