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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| Published in: | IEEE embedded systems letters Vol. 17; no. 3; pp. 160 - 163 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.06.2025
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| ISSN: | 1943-0663, 1943-0671 |
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| 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. |
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| AbstractList | 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. |
| Author | Carrera, Diego Boracchi, Giacomo Barros, Antonio Fabroni, Davide Peretti, Edoardo Fragneto, Pasqualina |
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| Snippet | Accurate and computationally light algorithms for estimating the state of charge (SoC) of a battery's cells are crucial for effective battery management on... |
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| SubjectTerms | 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 |
| Title | Adaptive Extended Kalman Filtering for Battery State of Charge Estimation on STM32 |
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