Year: 2024
Type: article
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 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.
Source: IEEE Embedded Systems Letters
Authors: António Barros, Edoardo Peretti, Davide Fabroni, Diego Carrera, Pasqualina Fragneto, Giacomo Boracchi
Institutions: STMicroelectronics (Italy), Politecnico di Milano
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403938309