Estimation of the battery state of charge: A Switching Markov State-Space Model

Jana Kalawoun (1) , Patrick Pamphile (2, 3) , Gilles Celeux (2, 3) , Krystyna Biletska (1, 4) , Maxime Montaru (4, 5)

1 LS2D (CEA, LIST) - Laboratoire Sciences des Données et de la Décision (CEA, LIST)
2 Inria-CWI - Inria-CWI
3 LMO - Laboratoire de Mathématiques d'Orsay
4 LITEN - Laboratoire d'Innovation pour les Technologies des Energies Nouvelles et les nanomatériaux
5 DTS - Département des Technologies Solaires

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.

Mots clés: estimation theory; battery powered vehicles; battery charge measurement; Signal processing; Decision support systems; Particle Filter; EM algorithm; Switching Markov State-Space Model; Kalman Filter; State of Charge; Markov processes; Monte Carlo; simulation; modelling; state-space methods; battery state of charge estimation; battery SoC estimation

Domaines: Traitement du signal et de l'image [eess.SP]; Modélisation et simulation; Energie électrique; Méthodologie [stat.ME]

Jana Kalawoun, Patrick Pamphile, Gilles Celeux, Krystyna Biletska, Maxime Montaru. Estimation of the battery state of charge: A Switching Markov State-Space Model. EUSIPCO 2015 - The 23rd European Signal Processing Conference, Eurasip, Aug 2015, Nice, France. pp.1950-1954, ⟨10.1109/EUSIPCO.2015.7362724⟩. ⟨cea-01888103⟩