Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32
Abstract
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 technique based on maximum likelihood estimation - a novelty in this domain. Furthermore, we tune a key design parameter - the window size - to obtain an optimal memory-performance trade-off, and experimentally demonstrate our solution achieves superior estimation accuracy with respect to existing alternative methods. Finally, we present a fully custom implementation of the AEKF for a general-purpose low-cost STM32 microcontroller, showing it can be deployed with minimal computational requirements adequate for real-world usage.
Keywords
Cite
@article{arxiv.2504.05936,
title = {Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32},
author = {António Barros and Edoardo Peretti and Davide Fabroni and Diego Carrera and Pasqualina Fragneto and Giacomo Boracchi},
journal= {arXiv preprint arXiv:2504.05936},
year = {2025}
}
Comments
Published in IEEE Embedded Systems Letters (2024)