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| Titre : | Parameter identification of lithium-ion battery model for BMS applications | | Type de document : | theses et memoires | | Auteurs : | Abou Baker Ameur, Auteur ; Mohammed Amin Khelifa, Directeur de thèse | | Editeur : | Tizi-Ouzou : UMMTO F.G.E.I | | Année de publication : | 2024 | | Importance : | 53 p | | Présentation : | ill. | | Format : | PDF | | Note générale : | Bibliogr. | | Langues : | Anglais | | Mots-clés : | Lithium-ion batteries Battery Management Systems Equivalent Circuit Model Parameter identification Rao-1 optimization Energy storage systems. | | Résumé : | Lithium-ion batteries (LiBs) have become the cornerstone of modern energy storage systems, powering applications ranging from electric vehicles to renewable energy grids.
However, their safe and efficient operation depends heavily on accurate modeling and reliable parameter identification strategies, which are primarily managed through Battery Management Systems (BMS).
This thesis investigates advanced techniques for modeling lithium-ion batteries and estimating their internal parameters to improve BMS accuracy and reliability.
The study begins with a comprehensive analysis of lithium-ion battery technologies, emphasizing the necessity of precise monitoring due to complex electrochemical behavior.
Three principal modeling approaches—electrochemical, data-driven, and equivalent circuit models (ECMs)—are critically evaluated. Thevenin-based ECMs are identified as the optimal balance between computational efficiency and model fidelity for real-time applications.
To address parameter identification challenges in ECMs, this thesis employs the Rao-1 algorithm, a simplified, parameter-free optimization method that eliminates complex hyperparameter tuning requirements.
Applied to real driving cycle data, Rao-1 successfully estimated twelve unknown battery model parameters with error rates below 2.5%, demonstrating exceptional robustness and practical applicability | | Diplôme : | Master | | Permalink : | ./index.php?lvl=notice_display&id=37896 |
Parameter identification of lithium-ion battery model for BMS applications [theses et memoires] / Abou Baker Ameur, Auteur ; Mohammed Amin Khelifa, Directeur de thèse . - Tizi-Ouzou (Tizi-Ouzou) : UMMTO F.G.E.I, 2024 . - 53 p : ill. ; PDF. Bibliogr. Langues : Anglais | Mots-clés : | Lithium-ion batteries Battery Management Systems Equivalent Circuit Model Parameter identification Rao-1 optimization Energy storage systems. | | Résumé : | Lithium-ion batteries (LiBs) have become the cornerstone of modern energy storage systems, powering applications ranging from electric vehicles to renewable energy grids.
However, their safe and efficient operation depends heavily on accurate modeling and reliable parameter identification strategies, which are primarily managed through Battery Management Systems (BMS).
This thesis investigates advanced techniques for modeling lithium-ion batteries and estimating their internal parameters to improve BMS accuracy and reliability.
The study begins with a comprehensive analysis of lithium-ion battery technologies, emphasizing the necessity of precise monitoring due to complex electrochemical behavior.
Three principal modeling approaches—electrochemical, data-driven, and equivalent circuit models (ECMs)—are critically evaluated. Thevenin-based ECMs are identified as the optimal balance between computational efficiency and model fidelity for real-time applications.
To address parameter identification challenges in ECMs, this thesis employs the Rao-1 algorithm, a simplified, parameter-free optimization method that eliminates complex hyperparameter tuning requirements.
Applied to real driving cycle data, Rao-1 successfully estimated twelve unknown battery model parameters with error rates below 2.5%, demonstrating exceptional robustness and practical applicability | | Diplôme : | Master | | Permalink : | ./index.php?lvl=notice_display&id=37896 |
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