Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32

Accurate and computationally light algorithms for estimating the state of charge (SoC) of a battery's cells are crucial for effective battery management on embedded systems. In this letter, we propose an adaptive extended Kalman filter (AEKF) for SoC estimation using a covariance adaptation tec...

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Veröffentlicht in:	IEEE embedded systems letters Jg. 17; H. 3; S. 160 - 163
Hauptverfasser:	Barros, Antonio, Peretti, Edoardo, Fabroni, Davide, Carrera, Diego, Fragneto, Pasqualina, Boracchi, Giacomo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2025
Schlagworte:	Adaptive extended Kalman filter (AEKF) Batteries Computational modeling Covariance matrices Data structures embedded implementation Integrated circuit modeling Kalman filters Load modeling Maximum likelihood estimation State of charge state of charge (SoC) estimation STM32 Voltage measurement
ISSN:	1943-0663, 1943-0671
Online-Zugang:	Volltext
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