Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter

Yasser Diab, François Auger, Emmanuel Schaeffer and Moutassem Wahbeh
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Yasser Diab: Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), Université de Nantes, Centre de Recherche et de Transfert de Technologie (CRTT), B.P. 406, 37 Bd de l’Université, Saint Nazaire CEDEX 44602, France
François Auger: Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), Université de Nantes, Centre de Recherche et de Transfert de Technologie (CRTT), B.P. 406, 37 Bd de l’Université, Saint Nazaire CEDEX 44602, France
Emmanuel Schaeffer: Institut de Recherche en Energie Electrique de Nantes Atlantique (IREENA), Université de Nantes, Centre de Recherche et de Transfert de Technologie (CRTT), B.P. 406, 37 Bd de l’Université, Saint Nazaire CEDEX 44602, France
Moutassem Wahbeh: Department of Electrical Power Engineering, Damascus University, Damascus B.P. 86, Syria

Energies, 2017, vol. 10, issue 8, 1-19

Abstract: A real-time determination of battery parameters is challenging because batteries are non-linear, time-varying systems. The transient behaviour of lithium-ion batteries is modelled by a Thevenin-equivalent circuit with two time constants characterising activation and concentration polarization. An experimental approach is proposed for directly determining battery parameters as a function of physical quantities. The model’s parameters are a function of the state of charge and of the discharge rate. These can be expressed by regression equations in the model to derive a continuous-discrete extended Kalman estimator of the state of charge and of other parameters. This technique is based on numerical integration of the ordinary differential equations to predict the state of the stochastic dynamic system and the corresponding error covariance matrix. Then a standard correction step of the extended Kalman filter (EKF) is applied to increase the accuracy of estimated parameters. Simulations resulting from this proposed estimator model were compared with experimental results under a variety of operating scenarios—analysis of the results demonstrate the accuracy of the estimator for correctly identifying battery parameters.

Keywords: battery modelling; continuous-discrete extended Kalman filter; state of charge; battery parameters; estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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