Expanding the Peukert equation for battery capacity modeling through inclusion of a temperature dependency

Hausmann, Austin; Depcik, Christopher

doi:10.1016/j.jpowsour.2013.01.174

Cited by 129 publications

(133 citation statements)

References 21 publications

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“…27) were found with use of the method of least squares and Levenberg-Marquardt's optimization procedure. Upon that experimental data was used received for two batteries of each type (as in the work 13 ) in order to receive parameters for a specific battery type instead of ones for a specific accumulator. The optimization results are represented in Tables II and III. Let us compare the parameters received by us in the course of model (Eq.…”

Section: Resultsmentioning

confidence: 99%

“…1) application and shown in the Table II on one hand and the similar parameters corresponding to the same model received in the work. 13 These parameters are taken at the same discharge modes. Nevertheless the parameter β differs in these tables rather considerably.…”

Section: Resultsmentioning

confidence: 99%

“…3.0 C discharge current at chilled, room, and hot conditions. As in the article, 13 there were chosen specific resistances for the 0.5C, 1C and 3C loads that apply the desired load under open-circuit voltage conditions assuming no voltage drop when loaded. While this does not result in true 0.5C, 1C and 3C loads, it maintains relative discharge criteria consistent for all of the batteries.…”

Section: Methodsmentioning

confidence: 99%

“…This equipment allowed exact reproduction of the dynamical testing modes pointed out in the work. 13 MATLAB was used for analyzing the data obtained during the experimental tests. …”

Section: Methodsmentioning

confidence: 99%

“…The Equations 5-7 were originally derived for the lead-acid batteries but presently they are also applied for capacity determining of batteries and other electrochemical systems. 13,22 Let us generalize Peukert equation in such a way that it would not lead to a contradiction at small discharge currents. Hence we obtain the correlation.…”

Section: Analysis Of Empirical Correlationsmentioning

confidence: 99%

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Generalized Analytical Model for Capacity Evaluation of Automotive-Grade Lithium Batteries

Galushkin

Yazvinskaya

Галушкин

2014

J. Electrochem. Soc.

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In this study, an analytical model is represented for evaluation of remaining charge state of commercial automotive-grade lithium batteries. The model is elaborated for evaluation of battery remaining charge state under dynamic loads and various temperature conditions, which is typical for operation of batteries being parts of electric or hybrid electric vehicles. It is proved that use of the Peukert equation as a part of an analytical model leads to a restriction of field-of-use for those models as the Peukert equation is not applicable under small discharge rates. It has been also shown that in modern analytical models, the usually used temperature dependencies are not applicable at extremely low and extremely high temperatures of battery discharge. The reason is that they do not take into account neither the presence of a negative thermal critical point, under which the battery capacity output becomes equal to zero, nor a limitation of a capacity growth in conditions of a temperature increase. The represented analytical model solves such problems to a large extent and provides with a good prediction accuracy for battery remaining state of charge (relative error is not more than 4% Nowadays in connection with wide spread distribution of electric vehicles and hybrid electric vehicles, -the acute need has emerged in evaluation possibility of battery residual capacity. This problem matters a great deal both for the modern lithium batteries, which at the moment are mainly used ones as parts of electric vehicles, and for traditional batteries, for example nickel-cadmium or lead-acid ones, which are widely used in area of aviation, railway vehicles, monitoring systems, electric-light services, etc. 1-4For battery residual capacity evaluation, it is possible to use the most precise electrochemical models. They take into account a transport-component (of ions, electrons, neutral particles) as well as all the kinetic electrochemical processes. 5,6 Those models contain very many hardly defined battery characteristics such as electrode geometries, electrolyte concentration, diffusion coefficients, transfer coefficients, reaction rate coefficients, and other low-level phenomena. 5-11So while the existing electrochemical models would be likely more exact in their predictions and able to provide with better results, the creation, calibration and implementation of such models requires significant computational capabilities. This makes these simulations less suitable for on-road vehicular battery prediction. Additionally, despite of the extensive efforts having been spent on such models, they can quickly become inapplicable if the system in question requires a change in battery chemistry or configuration since calibration of the models is going on for a specific battery and pack configuration. As a result, the majority of these models requires either widespread calibration, exhaustive computational resources, or fails to tune the models for dynamic discharge conditions. Both factors limit their effectiveness in mobile applica...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…This equipment allowed exact reproduction of the dynamical testing modes pointed out in the work. 13 MATLAB was used for analyzing the data obtained during the experimental tests. …”

Section: Methodsmentioning

confidence: 99%

Section: Analysis Of Empirical Correlationsmentioning

confidence: 99%

See 3 more Smart Citations

Generalized Analytical Model for Capacity Evaluation of Automotive-Grade Lithium Batteries

Galushkin

Yazvinskaya

Галушкин

2014

J. Electrochem. Soc.

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Revisiting the Importance of Sulfur Electrode‐Current‐Collector Interface in Lithium‐Sulfur Batteries

Reis,

Yari,

D'Haen

et al. 2023

Batteries & Supercaps

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This work demonstrates that, in lithium‐sulfur (Li‐S) batteries, the presence of the carbon/binder domain is more crucial at the vicinity of the current collector rather than in the bulk of the electrode. The electrode composition at the current collector interface is showcased to significantly impact the energy density, rate performance, reproducibility, and mechanical attributes of the Li‐S cells made thereof. The coating of a micrometric interlayer composed of carbon/binder at the current collector‐electrode interface is shown to be a promising avenue for the formulation of the sulfur electrodes with a low dead mass and volume while preserving a low polarization and high sulfur utilization. The short‐term spatial redistribution of the solid (dis)charge end‐products is tracked revealing the preferential precipitation of the S and Li2S at large cavities and near the current collector, respectively. The non‐uniform transformations inside the sulfur electrode is further corroborated by detecting and analyzing the precipitation waves inside a pressure electrochemical cell.

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State-of-charge estimators considering temperature effect, hysteresis potential, and thermal evolution for LiFePO₄ batteries

Xie

Bai³

2018

Int J Energy Res

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Summary To achieve accurate state‐of‐charge (SoC) estimation for LiFePO4 batteries, the effects of temperature, hysteresis, and thermal evolution are elaborately modeled. Open‐circuit voltage is regarded as the sum of electromotive force and hysteresis potential (Vh), where electromotive force is constructed as the function of SoC and temperature and Vh is reproduced with a geometrical model. By simulating battery heat generation and dissipation, a thermal evolution model is established and exploited for open‐circuit voltage and parameter identification. Then, on the basis of a second‐order equivalent circuit model, 2 SoC estimation schemes are proposed: One scheme uses the recursive least square with forgetting factor algorithm and off‐line equivalent circuit model parameters derived by the differential evolution algorithm; the other scheme resorts to the adaptive extended Kalman filter (EKF) and online tuned parameters. Experiments validate the effectiveness of the hysteresis model and the thermal evolution model. In contrast to a joint EKF estimator, experimental results under different temperatures and initial states suggest that both the proposed estimators are superior to the joint EKF estimator. Benefiting from the online updated parameters, the adaptive EKF estimator behaves best for giving consistent SoC‐tracking performance under different conditions.

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Expanding the Peukert equation for battery capacity modeling through inclusion of a temperature dependency

Cited by 129 publications

References 21 publications

Generalized Analytical Model for Capacity Evaluation of Automotive-Grade Lithium Batteries

Generalized Analytical Model for Capacity Evaluation of Automotive-Grade Lithium Batteries

Revisiting the Importance of Sulfur Electrode‐Current‐Collector Interface in Lithium‐Sulfur Batteries

State-of-charge estimators considering temperature effect, hysteresis potential, and thermal evolution for LiFePO₄ batteries

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Expanding the Peukert equation for battery capacity modeling through inclusion of a temperature dependency

Cited by 129 publications

References 21 publications

Generalized Analytical Model for Capacity Evaluation of Automotive-Grade Lithium Batteries

Generalized Analytical Model for Capacity Evaluation of Automotive-Grade Lithium Batteries

Revisiting the Importance of Sulfur Electrode‐Current‐Collector Interface in Lithium‐Sulfur Batteries

State-of-charge estimators considering temperature effect, hysteresis potential, and thermal evolution for LiFePO4 batteries

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Product

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State-of-charge estimators considering temperature effect, hysteresis potential, and thermal evolution for LiFePO₄ batteries