Mathematical Modeling and Parameter Estimation of Battery Lifetime using a Combined Electrical Model and a Genetic Algorithm

In this paper, a parametrization methodology based on the Genetic Algorithm meta-heuristic is proposed for the Chen and Rincón-Mora model parameter estimation, this model is used for the mathematical modeling of the Lithium-ion Polymer batteries lifetime. The model is also parametrized using the conventional procedures, which is based on the visual analysis of pulsed discharge curves, as presented in the literature. For both parametrization procedures, and for the model validation, experimental data obtained from a platform test are used. The results show that the proposed Genetic Algorithm is able to parametrize the model with better efficacy, presenting lower mean error, and also is a more agile process than the conventional one, been less subject to subjective aspects.

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Battery Model Parameters Estimation Using Simulated Annealing

TEMA (São Carlos) ◽

10.5540/tema.2017.018.01.0127 ◽

2017 ◽

Vol 18 (1) ◽

pp. 127 ◽

Cited By ~ 4

Author(s):

Marcia De Fatima Brondani ◽

Airam Teresa Zago Romcy Sausen ◽

Paulo Sérgio Sausen ◽

Manuel Osório Binelo

Keyword(s):

Mathematical Modeling ◽

Experimental Data ◽

Simulated Annealing ◽

Visual Analysis ◽

Lithium Ion ◽

Parameters Estimation ◽

Model Parameters ◽

Algorithm Efficiency ◽

Battery Model ◽

Discharge Curves

In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

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Parameter estimation for equivalent electrical model of lithium-ion cell

2017 19th European Conference on Power Electronics and Applications (EPE'17 ECCE Europe) ◽

10.23919/epe17ecceeurope.2017.8099328 ◽

2017 ◽

Cited By ~ 2

Author(s):

Grzegorz Dziechciaruk ◽

Bartlomiej Ufnalski ◽

Lech Grzesiak

Keyword(s):

Parameter Estimation ◽

Lithium Ion ◽

Electrical Model ◽

Lithium Ion Cell

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A Novel Screening Method Based on a Partially Discharging Curve Using a Genetic Algorithm and Back-Propagation Model for the Cascade Utilization of Retired Lithium-Ion Batteries

Electronics ◽

10.3390/electronics7120399 ◽

2018 ◽

Vol 7 (12) ◽

pp. 399 ◽

Cited By ~ 6

Author(s):

Xin Lai ◽

Dongdong Qiao ◽

Yuejiu Zheng ◽

Wei Yi

Keyword(s):

Genetic Algorithm ◽

Lithium Ion Batteries ◽

Screening Method ◽

Back Propagation ◽

Lithium Ion ◽

Maximum Error ◽

Economic Benefits ◽

Propagation Model ◽

Capacity Model ◽

Discharge Curves

Reusing the retired lithium-ion batteries from electric vehicles can generate considerable economic benefits. In this paper, a novel screening method based on partial discharge curves using a genetic algorithm and back-propagation (GA-BP) neural network for the retired cells is proposed. First, the discharge curves of the retired cells with different aging degrees were investigated. Based on this, the calculation method of internal resistance of retired cells was developed. Second, a novel capacity screening model based on a partially discharging process using a GA-BP model was proposed. In this model, the capacity and discharge characteristic data of a small number of sample cells were selected to train the capacity model using GA-BP, and the capacity of a large number of the remaining unsampled cells was estimated using the trained capacity model. Third, the screening simulation model with 108 retired cells was established, and the simulation results showed the effectiveness and rapidity of our proposed method. Finally, experimental verification was performed on the 20 retired cells with different aging degrees. The results showed that our proposed method is feasible, and the maximum error of capacity estimation was 2.951%.

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