Battery State of Charge estimation with Kalman filter - Réseau de recherche en Théorie des Systèmes Distribués, Modélisation, Analyse et Contrôle des Systèmes
Rapport Année : 2024

Battery State of Charge estimation with Kalman filter

Résumé

This notebook explores the State of Charge (SoC) estimation of a battery using a state observer algorithm, the Kalman filter, or more precisely its nonlinear extension: the extended Kalman filter (EKF). The notebook provides three Python implementations of the Kalman filter: 1. a step-by-step literate programming version of the filter, using a sequence of notebook cells, to implement one step of the filter, 2. a generic implementation (all the above steps wrapped in a single function) and 3. a compact implementation specialized for SoC estimation with baked-in battery model. Source notebook is available as supplementary material of this record or as an interactive version at https://github.com/pierre-haessig/pierre-notebooks.

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Dates et versions

hal-04701587 , version 1 (18-09-2024)

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  • HAL Id : hal-04701587 , version 1

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Pierre Haessig. Battery State of Charge estimation with Kalman filter. CentraleSupélec; IETR UMR 6164. 2024. ⟨hal-04701587v1⟩
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