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Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries

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  • Sun, Fengchun
  • Xiong, Rui
  • He, Hongwen
  • Li, Weiqing
  • Aussems, Johan Eric Emmanuel

Abstract

A model-based dynamic multi-parameter method for peak power estimation is proposed for batteries and battery management systems (BMSs) used in hybrid electric vehicles (HEVs). The available power must be accurately calculated in order to not damage the battery by over charging or over discharging or by exceeding the designed current or power limit. A model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries is proposed to calculate the reliable available power in real time, and the design limits such as cell voltage, cell current, cell SoC, cell power are all used as its constraints; more importantly, the relaxation effect also is considered. Where, to improve the model’s accuracy, the ohmic resistance of Thevenin model for the lithium–ion battery has been refined; in order to further improve the polarization parameters identification precision, a genetic algorithm has been used to gain the optimal time constant. Lastly, a test with several consecutive Federal Urban Driving Schedules (FUDSs) profiles is carried to evaluate the model-based dynamic multi-parameter method for peak power estimation. The experimental and simulation results indicate that the model-based dynamic multi-parameter method for peak power estimation can calculate the terminal voltage and the current available power much more reliably and accurately.

Suggested Citation

  • Sun, Fengchun & Xiong, Rui & He, Hongwen & Li, Weiqing & Aussems, Johan Eric Emmanuel, 2012. "Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries," Applied Energy, Elsevier, vol. 96(C), pages 378-386.
    • Handle: RePEc:eee:appene:v:96:y:2012:i:c:p:378-386
    DOI: 10.1016/j.apenergy.2012.02.061
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    References listed on IDEAS

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    1. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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