Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32 - Barros, Antonio; Peretti, Edoardo; Fabroni, Davide; Carrera, Diego; Fragneto, Pasqualina; Boracchi, Giacomo (IEEE embedded systems letters, 2025)
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 estimation window size-to obtain an optimal memory-performance tradeoff, 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.