Parametric analysis of Li-ion battery based on laboratory tests

Kopczyński, Artur; Liu, Zhiyin; Krawczyk, Paweł

doi:10.1051/e3sconf/20184400074

Cited by 9 publications

(4 citation statements)

References 4 publications

(4 reference statements)

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“…where

is the overvoltage;

is the open circuit voltage;

is the output voltage during the discharge; I is the discharge current; and R is the internal resistance of batteries. The internal resistance of WLIBs is higher than the commercial LIBs, and it would increase rapidly during overdischarge ( 18 , 19 ). As shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

A method for using the residual energy in waste Li-ion batteries by regulating potential with the aid of overvoltage response

Sun

Song

2023

Proc. Natl. Acad. Sci. U.S.A.

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The value of considerable residual energy in waste Li-ion batteries (WLIBs) is always neglected. At present, “this energy” is always wasted during the discharge process of WLIBs. However, if this energy could be reused, it would not only save a lot of energy but also avoid the discharge step of recycling of WLIBs. Unfortunately, the instability of WLIBs potential is a challenge to efficient utilization of this residual energy. Here, we propose a method that could regulate the cathode potential and current of the battery by simply adjusting the solution pH to utilize 35.08%, 88.4%, and 84.7% of the residual energy for removing heavy metal ions from wastewater, removing Cr (VI) from wastewater, and recovering copper from the solution, respectively. By taking advantage of the high internal resistance R of WLIBs and the sudden change of battery current I caused by iron passivation on the positive electrode of the battery, this method could induce the response of overvoltage η (η = IR) inside the battery at different pH levels to regulate the cathode potential µ of the battery to the three intervals. The potential ranges of the battery cathode corresponding to pH < 3.4, pH ≈ 3.4, and pH > 4 were µ > −0.47V, −0.47V < µ < −0.82V, and µ < −0.82V, respectively. This study provides a promising way and theoretical basis for the development of technologies for reusing residual energy in WLIBs.

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“…where

is the overvoltage;

is the open circuit voltage;

Section: Resultsmentioning

confidence: 99%

A method for using the residual energy in waste Li-ion batteries by regulating potential with the aid of overvoltage response

Sun

Song

2023

Proc. Natl. Acad. Sci. U.S.A.

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“…As can be seen in the mentioned figure, the methodology starts with a literature review and model development. The proposed battery model is a "synthetic" model since it is based on experimental data and reported results from different authors [39][40][41][42]. The proposed battery model is based on an equivalent hybrid electrical circuit model, which includes the development of electrical and electrochemical equations.…”

Section: Methodsmentioning

confidence: 99%

Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms

Camas-Náfate,

Coronado-Mendoza,

Vargas-Salgado

et al. 2024

Energies

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In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

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“…One of the most used methodologies is charging at constant voltages and discharging at constant currents; thus, recommendations given by the manufacturers were taken into account [43,44].…”

Section: Proposed Modelmentioning

confidence: 99%

Modeling and Simulation of a Commercial Lithium-Ion Battery with Charge Cycle Predictions

et al. 2022

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The analysis of the behavior of lithium-ion batteries has gained considerable interest in recent years. There are different alternatives for the analysis of this behavior; however, depending on the type of modeling, there are application and optimization restrictions. In this work, a hybrid model has been made that is capable of predicting the characteristics of a lithium-ion battery. As a novelty, the simplification, at the same time, facilitates the sampling of parameters for their prompt selection for optimization. A new model open to the user is proposed, which has proven to be efficient in simulation time. For example, one hour simulates it in 5 min, providing information detailing how these parameters, State of Health (SOH), Open Circuit Voltage (VOC), State of charge (SOC), and Number of charge and discharge cycles, in the face of temperature variations and charge and discharge cycles. Opening the possibility of optimizing the parameters with different techniques to estimate the performance and dynamics in the face of temperature change and charge and discharge cycles. A model based on linear regressions, manufacturer characteristics, and integrating equations in the electrical model of electrochemical phenomena is proposed.

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Parametric analysis of Li-ion battery based on laboratory tests

Cited by 9 publications

References 4 publications

A method for using the residual energy in waste Li-ion batteries by regulating potential with the aid of overvoltage response

A method for using the residual energy in waste Li-ion batteries by regulating potential with the aid of overvoltage response

Optimizing Lithium-Ion Battery Modeling: A Comparative Analysis of PSO and GWO Algorithms

Modeling and Simulation of a Commercial Lithium-Ion Battery with Charge Cycle Predictions

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