An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO 4 (ANR26650)

Victor Pizarro-Carmona, Marcelo Cortés-Carmona, Rodrigo Palma-Behnke, Williams Calderón-Muñoz, Marcos E. Orchard and Pablo A. Estévez
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Victor Pizarro-Carmona: Department of Electrical Engineering, Universidad de Antofagasta, Antofagasta 1240000, Chile
Marcelo Cortés-Carmona: Department of Electrical Engineering, Universidad de Antofagasta, Antofagasta 1240000, Chile
Rodrigo Palma-Behnke: Department of Electrical Engineering, Universidad de Chile, Santiago 8320000, Chile
Williams Calderón-Muñoz: Department of Mechanical Engineering, Universidad de Chile, Santiago 8320000, Chile
Marcos E. Orchard: Department of Electrical Engineering, Universidad de Chile, Santiago 8320000, Chile
Pablo A. Estévez: Department of Electrical Engineering, Universidad de Chile, Santiago 8320000, Chile

Energies, 2019, vol. 12, issue 4, 1-16

Abstract: This article focused on the estimation of the state of charge (SoC) of a Li-con Cell by carrying out a series of experimental tests at various operating temperatures and SoC. The cell was characterized by electrochemical impedance spectroscopy (EIS) tests, from which the impedance frequency spectrum for different SoC and temperatures was obtained. Indeed, the cell model consisted of a modified Randles circuit type that included a constant phase element so-called Warburg impedance. Each circuit parameter was obtained from the EIS tests. The obtained were been used to develop two numerical models for each parameter, i.e., one based on numerical correlations and the other based on the artificial neural network (ANN) method. A genetic algorithm was used to solve and optimize the numerical models. The accuracy of the models was examined and the results showed that the ANN-based model was more accurate than the correlations-based model. The root mean square relative error (RMSRE) of the parameters Rs, R 1 , C 1 and W for the ANN-based model were: 4.63%, 13.65%, 10.96% and 4.4%, respectively, compared to 7.09%, 27.45%, 34.36% and 7.07% for the correlations-based model, respectively. The SoC was estimated using the extended Kalman filter based on a Randles model, with an estimation RMSRE of about 1.19%.

Keywords: Li-ion cell; electrochemical impedance spectroscopy; genetic algorithm; neural network; extended Kalman filter (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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