Enhanced prognostic model for lithium ion batteries based on particle filter state transition model modification

Buddhi Arachchige, Suresh Perinpanayagam, Raul Jaras

    Research output: Contribution to journalArticleResearchpeer-review

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    Abstract

    This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL) and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery's State of Charge (SOC) and State of Life (SOL) by utilizing the battery's physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge) time with cycling has also been included, integrating both EOL (End of Life) and EOD prediction models in order to get more accuracy in the estimations.

    Original languageEnglish
    Article number1172
    Pages (from-to)1-19
    Number of pages19
    JournalApplied Sciences (Switzerland)
    Volume7
    Issue number11
    DOIs
    Publication statusPublished - 15 Nov 2017

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    electric batteries
    lithium
    filters
    ions
    Voltage measurement
    Electric current measurement
    estimates
    Temperature measurement
    electrical measurement
    temperature measurement
    Lithium-ion batteries
    cycles
    predictions

    Cite this

    Arachchige, Buddhi ; Perinpanayagam, Suresh ; Jaras, Raul. / Enhanced prognostic model for lithium ion batteries based on particle filter state transition model modification. In: Applied Sciences (Switzerland). 2017 ; Vol. 7, No. 11. pp. 1-19.
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    title = "Enhanced prognostic model for lithium ion batteries based on particle filter state transition model modification",
    abstract = "This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL) and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery's State of Charge (SOC) and State of Life (SOL) by utilizing the battery's physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge) time with cycling has also been included, integrating both EOL (End of Life) and EOD prediction models in order to get more accuracy in the estimations.",
    keywords = "Capacity fade, IVHM (Integrated Vehicle Health Monitoring), Probability density function, Remaining Useful Life (RUL) estimation, State of Charge",
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    Enhanced prognostic model for lithium ion batteries based on particle filter state transition model modification. / Arachchige, Buddhi; Perinpanayagam, Suresh; Jaras, Raul.

    In: Applied Sciences (Switzerland), Vol. 7, No. 11, 1172, 15.11.2017, p. 1-19.

    Research output: Contribution to journalArticleResearchpeer-review

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    T1 - Enhanced prognostic model for lithium ion batteries based on particle filter state transition model modification

    AU - Arachchige, Buddhi

    AU - Perinpanayagam, Suresh

    AU - Jaras, Raul

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    AB - This paper focuses on predicting the End of Life and End of Discharge of Lithium ion batteries using a battery capacity fade model and a battery discharge model. The proposed framework will be able to estimate the Remaining Useful Life (RUL) and the Remaining charge through capacity fade and discharge models. A particle filter is implemented that estimates the battery's State of Charge (SOC) and State of Life (SOL) by utilizing the battery's physical data such as voltage, temperature, and current measurements. The accuracy of the prognostic framework has been improved by enhancing the particle filter state transition model to incorporate different environmental and loading conditions without retuning the model parameters. The effect of capacity fade in the reduction of the EOD (End of Discharge) time with cycling has also been included, integrating both EOL (End of Life) and EOD prediction models in order to get more accuracy in the estimations.

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