{"title":"Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32","authors":"António Barros;Edoardo Peretti;Davide Fabroni;Diego Carrera;Pasqualina Fragneto;Giacomo Boracchi","doi":"10.1109/LES.2024.3489352","DOIUrl":null,"url":null,"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.","PeriodicalId":56143,"journal":{"name":"IEEE Embedded Systems Letters","volume":"17 3","pages":"160-163"},"PeriodicalIF":2.0000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Embedded Systems Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10740057/","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The IEEE Embedded Systems Letters (ESL), provides a forum for rapid dissemination of latest technical advances in embedded systems and related areas in embedded software. The emphasis is on models, methods, and tools that ensure secure, correct, efficient and robust design of embedded systems and their applications.