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Signal Processing by Switching Markov State-Space Models: Estimation of the State of Charge of an Electric Battery

Abstract : Switching Markov State-Space Models (SMSSM) are linear models whose parameters randomly change over time according to a finite discrete Markov chain. This generalization allows, for instance, to dealing with systems which are locally linear. However, it can be difficult to implement SMSSM on real-world applications. In this paper we present techniques and methods to solve the four basic problems of SMSSM implementation, namely the identifiabil-ity, the model parameters inference, the order selection and the online state inference. As an illustration, we consider the problem of estimating the State of Charge (SoC) of an electric battery. For this purpose, we develop a new SoC model, implemented with a SMSSM, and show its ability to accurately estimate the SoC of the battery of an electric vehicle under different usage conditions.
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Preprints, Working Papers, ...
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https://hal.inria.fr/hal-01149641
Contributor : Patrick Pamphile Connect in order to contact the contributor
Submitted on : Thursday, May 28, 2015 - 9:43:46 AM
Last modification on : Wednesday, April 20, 2022 - 3:37:39 AM
Long-term archiving on: : Monday, September 14, 2015 - 8:20:13 PM

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  • HAL Id : hal-01149641, version 1

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Jana Kalawoun, Patrick Pamphile. Signal Processing by Switching Markov State-Space Models: Estimation of the State of Charge of an Electric Battery. 2015. ⟨hal-01149641⟩

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