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Energies 2017, 10(8), 1075; doi:10.3390/en10081075

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

1
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
2
Department of Electrical Power Engineering, Damascus University, Damascus B.P. 86, Syria
*
Author to whom correspondence should be addressed.
Received: 8 June 2017 / Revised: 21 July 2017 / Accepted: 22 July 2017 / Published: 25 July 2017
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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. View Full-Text
Keywords: battery modelling; continuous-discrete extended Kalman filter; state of charge; battery parameters; estimation battery modelling; continuous-discrete extended Kalman filter; state of charge; battery parameters; estimation
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Diab, Y.; Auger, F.; Schaeffer, E.; Wahbeh, M. Estimating Lithium-Ion Battery State of Charge and Parameters Using a Continuous-Discrete Extended Kalman Filter. Energies 2017, 10, 1075.

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