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

F. Sun, R. Xiong, H. He, W. Li, J.E.E. Aussems

    Research output: Contribution to journalArticleAcademicpeer-review

    168 Citations (Scopus)

    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. (C) 2012 Elsevier Ltd. All rights reserved
    Original languageEnglish
    Pages (from-to)378-386
    JournalApplied Energy
    Volume96
    DOIs
    Publication statusPublished - 2012

    Fingerprint

    Dive into the research topics of 'Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries'. Together they form a unique fingerprint.

    Cite this