Dynamic identification of model parameters for energy storage batteries
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    Abstract:

    As the important indices for reflecting the comprehensive performance of the battery, the parameters of lithium-ion are unavailable by direct observation. Through identifying internal circuit parameters by establishing multiple linear regression model, a new method for the state of charge estimation of lithium-ion batteries by indirectly observing the batteries balance electromotive force was proposed. The equivalent capacitance was introduced to represent capacity character of lithium-ion batteries in this new method, which was based on an improved Thevenin battery model. Through measuring changes of open-circuit voltage and charge before and after the discharge process in a cycle period, the equivalent capacitance can be recognized in a long time scale. As the main variable for estimating state of charge, the balance electromotive force can be dynamically estimated through the equivalent capacitance in a short time scale. Finally, a pulse discharging experiment of a cycle period verified the effectiveness and accuracy of the improved battery model and the proposed dynamic identification method.

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LIU Yingquan, LU Junyong, LONG Xinlin, WEI Jingbo, ZHOU Ren, WU Yiting. Dynamic identification of model parameters for energy storage batteries[J]. Journal of National University of Defense Technology,2019,41(5):87-92.

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History
  • Received:April 25,2018
  • Revised:
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  • Online: September 30,2019
  • Published: October 28,2019
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