A numerical and statistical implementation of a thermal model for a lithium-ion battery

Moralı, Uğur

doi:10.1016/j.energy.2021.122486

Cited by 25 publications

(5 citation statements)

References 37 publications

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“…The main features of the battery are presented in Table 1 . More details on the lithium-ion battery can be found in [ 27 ]. Figure 1b shows that 1 mm thickness phase change material was integrated with the XY plane.…”

Section: Methodsmentioning

confidence: 99%

Parameter effect quantification for a phase change material-based lithium-ion battery thermal management system

MORALI¹

2022

Turkish Journal of Chemistry

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The influence of discharge rate, ambient temperature, and phase change material on the maximum temperature and the highest temperature difference was investigated. The maximum temperature of the battery was tested with and without phase change material under extreme discharge rates (4C and 5C) and ambient temperatures (310 K and 320 K). Results showed that a phase change material reduced the maximum temperature from 327.94 K to 306.45 K for a 14.6 Ah lithium-ion battery discharged at 5C-rate and 320 K. Quantitatively determined parameter effects revealed that the PCM parameter considerably had a remarkable influence on maximum temperature compared to discharge rate and ambient temperature. Moreover, the influence of ambient temperature on the maximum temperature was approximately 2.5 times greater than the C-rate, while the influence of ambient temperature on the highest temperature difference was approximately 50 times greater than the C-rate. The quantified parameter effects can be used to improve the phase change material-battery cooling system.

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Section: Methodsmentioning

confidence: 99%

Parameter effect quantification for a phase change material-based lithium-ion battery thermal management system

MORALI¹

2022

Turkish Journal of Chemistry

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“…where δ is the thickness in m. The index 'i' refers to the layers that make up the cell. The inplane thermal conductivities, λ x and λ y , are identical and computed based on Equation ( 9), while the through-plane thermal conductivity λ z , in the thickness direction, is calculated based on Equation (10).…”

Section: Thermal Model: Finite Element 3d Model (Reference Model)mentioning

confidence: 99%

“…The density, the specific heat capacity, and the volume of each block, presented in the equations below, are determined previously based on the data available in Tables A1 and A3 of the Appendix A. The thermal resistances by conduction and convection employed in the heat equations were defined for each body, and in the three directions, with respect to the equations illustrated in Table A5 of Appendix A, where the thermal conductivities of the cell in x, y, and z directions were determined by using the data available in Table A3 of Appendix A and by applying Equations ( 9) and (10). In terms of the thermal conductivities of the tabs and the insulation layers, they were directly extracted from Tables A3 and A4 of Appendix A since the bodies are composed of one material.…”

Section: Thermal Model: Lumped 3d Thermal Resistance Modelmentioning

confidence: 99%

“…Many studies have investigated the thermal behavior of different types of Li-ion batteries under several operating cycles while using finite element thermal models coupled with electric models. Ugur has conducted a thermal modeling of a prismatic LiMn 2 O 4 cell using a three-dimensional (3D) thermal model based on the finite element approach coupled with the New, Tiedman, Gu, and Kim (NTGK) formulation for the heat generation calculation [10][11][12]. The author examined the effect of the ambient temperature, depth of discharge (DOD), discharge rate, and the heat convection coefficient on the thermal profile.…”

Section: Introductionmentioning

confidence: 99%

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Low-Computational Model to Predict Individual Temperatures of Cells within Battery Modules

Abbas,

Rizoug,

Trigui

et al. 2024

Batteries

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Predicting the operating temperature of lithium-ion battery during different cycles is important when it comes to the safety and efficiency of electric vehicles. In this regard, it is vital to adopt a suitable modeling approach to analyze the thermal performance of a battery. In this paper, the temperature of lithium-ion NMC pouch battery has been investigated. A new formulation of lumped model based on the thermal resistance network is proposed. Unlike previous models that treated the battery as a single entity, the proposed model introduces a more detailed analysis by incorporating thermal interactions between individual cells and tabs within a single cell scenario, while also considering interactions between cells and insulators or gaps, located between the cells, within the module case. This enhancement allows for the precise prediction of temperature variations across different cells implemented within the battery module. In order to evaluate the accuracy of the prediction, a three-dimensional finite element model was adopted as a reference. The study was performed first on a single cell, then on modules composed of several cells connected in series, during different operating conditions. A comprehensive comparison between both models was conducted. The analysis focused on two main aspects, the accuracy of temperature predictions and the computational time required. Notably, the developed lumped model showed a significant capability to estimate cell temperatures within the modules. The thermal results revealed close agreement with the values predicted by the finite element model, while needing significantly lower computational time. For instance, while the finite element model took almost 21 h to predict the battery temperature during consecutive charge/discharge cycles of a 10-cell module, the developed lumped model predicted the temperature within seconds, with a maximum difference of 0.42 °C.

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“…Lithium‐ion batteries have been widely used in electric vehicles because of their advantages, such as low self‐discharge rates, long cycle lifetimes, high energy, and high power density. [ 6,7 ] During the operation of an electric vehicle, the battery discharges continuously, and the current amplitude increases rapidly, especially under special acceleration and climbing conditions. When the battery discharges, a large quantity of heat is generated and accumulated by the interactions between the chemical substances within.…”

Section: Introductionmentioning

confidence: 99%

Iterative Dynamic Programming Strategy for Electric Vehicle Battery Thermal Management Optimization

Ma¹,

2022

Advcd Theory and Sims

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Lithium‐ion batteries are extensively used in electric vehicles because of their superior performance in the general environment of global energy shortage. However, lithium‐ion batteries generate large amounts of heat as the battery packs are charged and discharged. If this accumulated heat is not dissipated timely, then the power supply system will fail, resulting in safety accidents. Therefore, a high‐efficiency and energy‐saving optimization strategy is required for both ensuring operating temperature of the battery and saving energy. Herein, a lumped thermal model is established on the basis of the heat generation characteristics of batteries and Newton's law of cooling, and its exactitude is proven by comparison with a liquid cooling system established using AMESim. In addition, to reduce computation time and enhance the real‐time capability, an iterative dynamic programming (IDP) method is proposed to determine the optimal values iteratively in a multidimensional search space, in view of the high nonlinearity and time‐variability of a battery thermal management system as well as the complexity of dynamic programming (DP). Thus, the results from co‐simulations performed in Matlab and AMESim verify that the proposed strategy has high cooling efficiency and can save considerable energy for cooling system compared with PID and DP.

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A numerical and statistical implementation of a thermal model for a lithium-ion battery

Cited by 25 publications

References 37 publications

Parameter effect quantification for a phase change material-based lithium-ion battery thermal management system

Parameter effect quantification for a phase change material-based lithium-ion battery thermal management system

Low-Computational Model to Predict Individual Temperatures of Cells within Battery Modules

Iterative Dynamic Programming Strategy for Electric Vehicle Battery Thermal Management Optimization

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