An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

Blaifi, S.; Moulahoum, Samir; Çolak, İlhami; Merrouche, W.

doi:10.1016/j.apenergy.2016.02.062

Cited by 56 publications

(30 citation statements)

References 27 publications

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“…They have an initial value of 1 to maintain the original model performance. Table 1 compares the parameters identified by [40] with the values published by [39] and [41][42][43] showing a high dispersion between the different authors. The model is able to choose the discharging equation when the input signal has a positive sign (+A); otherwise, the model chooses the charging equation when the current sign is negative (−A).…”

Section: Battery Model Testmentioning

confidence: 96%

Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms

et al. 2018

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Accurate and efficient battery modeling is essential to maximize the performance of isolated energy systems and to extend battery lifetime. This paper proposes a battery model that represents the charging and discharging process of a lead-acid battery bank. This model is validated over real measures taken from a battery bank installed in a research center placed at “El Chocó”, Colombia. In order to fit the model, three optimization algorithms (particle swarm optimization, cuckoo search, and particle swarm optimization + perturbation) are implemented and compared, the last one being a new proposal. This research shows that the identified model is able to estimate real battery features, such as state of charge (SOC) and charging/discharging voltage. The comparison between simulations and real measures shows that the model is able to absorb reading problems, signal delays, and scaling errors. The approach we present can be implemented in other types of batteries, especially those used in stand-alone systems.

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Section: Battery Model Testmentioning

confidence: 96%

Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms

et al. 2018

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“…Its basic function is to transform parameters in the most effective way, so as to enhance the efficiency of the system. Basically, GA will randomly generate N chromosomes and imitate the process of biological evolution, including selection, crossover, and mutations based on good individuals surviving and breed good individuals to optimize the variables problem [115]. An example of the genetic algorithm steps is shown in Figure 8.…”

Section: (3) Extreme Machine Learning (Elm)mentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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“…Hence in this work, GA and PSO are compared to assess the speed, the robustness and the accuracy on two Li-ions with different characteristics. Since the experiments are held in the BoL and 25 • C a single-objective approach is proposed as in [109]. According to literature research, the optimization criterion to define the optimal value can be set with either the mean or their square residual, or the RMS minimization.…”

Section: Parameter Extraction With Heuristic Optimizationmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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Md.Sazzad.Hosen@vub.be (M.S.H.); Mohsen.Akbarzadeh.Sokkeh@vub.be (M.A.S.); Shovon.Goutam@vub.be (S.G); Joris.Jaguemont@vub.be (J.J.); Maitane.Berecibar@vub.be (M.B.); Joeri.van.mierlo@vub.be (J.V.M.) 2 Flanders Make,Abstract: A lithium-ion battery cell's electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light-and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs' parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

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An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications

Cited by 56 publications

References 27 publications

Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms

Modelling, Parameter Identification, and Experimental Validation of a Lead Acid Battery Bank Using Evolutionary Algorithms

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

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