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Embedded strategy for battery module states estimation using tiny machine learning models
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DOI：10.1016/j.est.2026.120673.png)
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Abstract
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• TinyML model optimization via neural projection for embedded SoC/SoH smart sensing. • Variable-length SoH models using partial-charge ICA curves and voltage data • End-to-end BMS framework scaling SoC/SoH from cell to module in real system
Keywords:
State of charge
State of health
TinyML
Embedded machine learning
Cell-to-module
Model optimization
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