Estimation of the battery state of charge: A Switching Markov state-space model

Abstract : An efficient estimation of the State of Charge (SoC) of a battery is a challenging issue in the electric vehicle domain. The battery behavior depends on its chemistry and uncontrolled usage conditions, making it very difficult to estimate the SoC. This paper introduces a new model for SoC estimation given instantaneous measurements of current and voltage using a Switching Markov State-Space Model. The unknown parameters of the model are batch learned using a Monte Carlo approximation of the EM algorithm. Validation of the proposed approach on an electric vehicle real data is encouraging and shows the ability of this new model to accurately estimate the SoC for different usage conditions.
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https://hal-cea.archives-ouvertes.fr/cea-01888103
Contributor : Marie-France Robbe <>
Submitted on : Thursday, October 4, 2018 - 5:07:33 PM
Last modification on : Monday, July 8, 2019 - 11:26:04 AM

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Jana Kalawoun, Patrick Pamphile, Gilles Celeux, Krystyna Biletska, Maxime Montaru. Estimation of the battery state of charge: A Switching Markov state-space model. 2015 23rd European Signal Processing Conference (EUSIPCO), Aug 2015, Nice, France. pp.7362724, ⟨10.1109/EUSIPCO.2015.7362724⟩. ⟨cea-01888103⟩

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