Experimental study on heat generation characteristics of lithium-ion batteries using a forced convection calorimetry method

Cao, Renfeng; Zhang, Xingjuan; Yang, Han; Wang, Chao

doi:10.1016/j.applthermaleng.2022.119559

Cited by 19 publications

(5 citation statements)

References 38 publications

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“…The Figure . 5b in [36] demonstrates that the HGR increased with an increase in the discharge current. The correlation between the HGR and discharge current is observed to be nonlinear based on the most commonly used thermal model [37] in Equation (1).…”

Section: Data Collection and Preprocessingmentioning

confidence: 90%

“…The temperature and internal resistance are also related to the discharge current. The Figure . 7b in [36] demonstrates that the HGR decreased with an increase in the ambient temperature, and its variation rate was also nonlinear.…”

Section: Data Collection and Preprocessingmentioning

confidence: 95%

“…The training and testing data were measured by our research group and published in [36]. We used Panasonic NCR18650 batteries with a rated capacity of 2900 mAh.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

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Prediction of the Heat Generation Rate of Lithium-Ion Batteries Based on Three Machine Learning Algorithms

2023

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The heat generation rate (HGR) of lithium-ion batteries is crucial for the design of a battery thermal management system. Machine learning algorithms can effectively solve nonlinear problems and have been implemented in the state estimation and life prediction of batteries; however, limited research has been conducted on determining the battery HGR through machine learning. In this study, we employ three common machine learning algorithms, i.e., artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR), to predict the battery HGR based on our experimental data, along with cases of interpolation and extrapolation. The results indicated the following: (1) the prediction accuracies for the interpolation cases were better than those of extrapolation, and the R2 values of interpolation were greater than 0.96; (2) after the discharge voltage was added as an input parameter, the prediction of the ANN was barely affected, whereas the performance of the SVM and GPR were improved; and (3) the ANN exhibited the best performance among the three algorithms. Accurate results can be obtained by using a single hidden layer and no more than 15 neurons without the additional input, where the R2 values were in the range of 0.89–1.00. Therefore, the ANN is preferable for predicting the HGR of lithium-ion batteries.

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Section: Data Collection and Preprocessingmentioning

confidence: 90%

Section: Data Collection and Preprocessingmentioning

confidence: 95%

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Prediction of the Heat Generation Rate of Lithium-Ion Batteries Based on Three Machine Learning Algorithms

2023

Self Cite

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“…A common approach is to used calorimetric measurements to determine the battery heat capacity and the anisotropic thermal conductivity: Vertiz et al [40] used Accelerating Rate Calorimeter (ARC) technique to determine heat capacity and thermal conductivity of a lithium-ion pouch cell, coupling an electrical circuit model to a thermal circuit model. Sheng et al [41] imposed controlled heat flux to investigate thermal parameters of a prismatic lithium-ion cell, whereas Cao et al studied the heat generation characteristics through heat flux measurements of commercial 18650 cells [42]. Calorimetric measurements, though precise, require expensive equipment: this is one of the reason why many papers on thermal modelling of Li-ion cells cited above adopted the volume-fraction method to determine the battery thermal parameters from the materials properties.…”

Section: Thermal Characterization Testsmentioning

confidence: 99%

Testing and Characterizing Commercial 18650 Lithium-Ion Batteries

Zatta,

De Cesaro,

Dal Cin

et al. 2024

Preprint

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Reduced-order electrothermal models play a key role in the design and control of lithium- ion cell stacks, calling for accurate model parameter calibration. This paper presents a complete electrical and thermal experimental characterization procedure for coupled modeling of cylindrical lithium-ion cells in order to implement them in a prototype Formula SAE hybrid racing car. The main 4goal of the tests is to determine cell capacity variations with temperatures and discharge currents, to predict the open circuit voltage of the cell and its entropic component. A simple approach for the characterization of the battery equivalent electrical circuit and a two-steps thermal characterization method are also shown. The investigations are carried out on four commercial 18650 NMC lithium cells. The model demonstrated to predict the battery voltage at a RMS error lower than 20mV and the temperature to a RMS error equal to 0.5 ◦C. The authors hope that this manuscript can contribute to the development of standardized characterization techniques for such cells, while offering experimental data and validated models that can be used by researchers and BMS designers in different applications.

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“…Additionally, surface heat dissipation was measured on the brass fin using a thermocouple and Peltier's Element (PE). Cao et al [15] also presented a method to characterize the heat generation of lithium-ion battery cells under various discharge currents, ambient temperatures, and aging cycles, utilizing a forced convection calorimeter in experimental setups. This method operates on the principle of thermal balance, wherein the heat generated equals the heat forcibly convected into the air flowing within the calorimeter chamber.…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation of Heat Production in the Two-Wheeler Electric Vehicle Battery via Torque Load Variation Test

Pujowidodo,

Prasetyo,

Soewono

et al. 2024

WEVJ

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Experimental studies were conducted to investigate the effect of varying torque loads on the temperature distribution on the surface of lithium-ion batteries (72 volts–20 Ah) in real commercial two-wheeler electric vehicles as part of our previous research. An electric vehicle engine was installed in a dyno testing laboratory and used as the main load for the battery. Ambient temperature and relative humidity were controlled using an air conditioning system. The test results are presented as surface temperature distributions on each side of the battery at various torque loads. The highest temperature on the battery’s surface was found to be approximately 40 °C at a torque load of 100%. Unfortunately, the heat generated by the battery during testing could not be measured for further research. This paper presents a numerical study of battery heat generation at 100% torque load using Ansys Fluent 2020 R1©. This tool is employed to calculate the heat flux from the battery surface to the ambient air. The CFD tool was initially validated against available experimental data and commonly used correlations for natural convection along a vertically heated wall. Good agreements between the current predictions and experimental data were observed for laminar flow regimes. Convective heat transfer between the battery surface and ambient air was simulated. The results indicate that the commonly used heat transfer correlation for vertical plates accurately predicts the heat transfer rate on the battery surface, and it was found that the heat generation rate is 1199 W/m3.

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Experimental study on heat generation characteristics of lithium-ion batteries using a forced convection calorimetry method

Cited by 19 publications

References 38 publications

Prediction of the Heat Generation Rate of Lithium-Ion Batteries Based on Three Machine Learning Algorithms

Prediction of the Heat Generation Rate of Lithium-Ion Batteries Based on Three Machine Learning Algorithms

Testing and Characterizing Commercial 18650 Lithium-Ion Batteries

Numerical Investigation of Heat Production in the Two-Wheeler Electric Vehicle Battery via Torque Load Variation Test

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