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Thermal Behavior Analysis of Lithium Ion Cells used in EVs and HEVs
Technical Paper
2019-28-0163
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The batteries for electric vehicles (EV) generate heat during discharging cycles. During these rapid discharge cycles the temperature of cell may increase above allowable limits. The high temperature of lithium ion cell is the primary factor affecting the cell performance and life. To develop efficient cooling mechanism for batteries, thermal behavior of secondary cell is must know. In this research, experimentally the thermal behavior analysis of cylindrical lithium ion cells at constant current discharge cycles with different current rates for each cycle is evaluated. The experiments were carried out at three discharge cycles of 1C, 2C and 3C rates and two battery chemistries namely NiMnCo and NiCoAlare considered for analysis. The instantaneous temperature of cell was measured using thermal imager and increase in overall cell surface temperature at different discharge rates, for entire discharging interval has been studied. An empirical relation for average surface temperature of cell at different current rates and depth of discharge has been obtained which may find application in defining the discharge algorithms. The rates of internal heat generation in both types of cell chemistry are calculated from the temperature data obtainedexperimentally. The extensive comparison of two cell chemistries on the basis of internal heat generation rate and rise in average surface temperature of cells for different current rates will help in battery selection and efficient designing of cooling mechanisms in battery pack. The experimental results in this research conclude that NiMnCo cell has least instantaneous internal heat generation rate at all current rates and is therefore safer and more thermally stable than NiCoAl cell.
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Citation
Lonkar, S., Jain, A., and Bhalerao, V., "Thermal Behavior Analysis of Lithium Ion Cells used in EVs and HEVs," SAE Technical Paper 2019-28-0163, 2019, https://doi.org/10.4271/2019-28-0163.Data Sets - Support Documents
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