Thermal Optimization Strategies for Li-Ion Batteries: Predictive Temperature AlgorithmSource: Journal of Thermal Science and Engineering Applications:;2024:;volume( 016 ):;issue: 008::page 81003-1Author:Antonio, Metallo
DOI: 10.1115/1.4065471Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Performance, safety, and longevity of batteries are all strongly impacted by thermal management, which is an essential component of battery design and operation. This work examines how accurate temperature control can result in significant improvements in performance and reliability with a focus on battery thermal heating. Predicting the temperature achieved by the battery during operation not only avoids conditions that lead to thermal runaway but also guarantees that the battery is used optimally within an optimal temperature range. Within the optimal temperature range, several advantages are observed. First, battery efficiency improves significantly as electrochemical processes occur more efficiently. Furthermore, by lowering the possibility of short circuits and improving overall battery safety, thermal stability aids in the prevention of undesirable phenomena like dendrite growth. By lessening the deterioration brought on by thermal degradation processes, thermal optimization also affects battery longevity. Based on experimental tests, a finite element method (FEM) model is developed. A model for thermal runaway propagation is established by combining thermal runaway and conduction models with an Arrhenius law-based combustion model. The study employed a cylindrical Li-ion cell to conduct tests, taking into account three parameters: discharge rate (CRate), ambient temperature (Tamb), and initial battery temperature (T0). An algorithm based on the three variables was developed using the simulation results. The algorithm enables the accurate prediction of rising battery temperature during use, facilitating the setting of an optimal maximum discharge rate considering initial and ambient temperatures, thereby ensuring optimal performance within the desired temperature range.
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contributor author | Antonio, Metallo | |
date accessioned | 2024-12-24T18:42:42Z | |
date available | 2024-12-24T18:42:42Z | |
date copyright | 5/23/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 1948-5085 | |
identifier other | tsea_16_8_081003.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4302605 | |
description abstract | Performance, safety, and longevity of batteries are all strongly impacted by thermal management, which is an essential component of battery design and operation. This work examines how accurate temperature control can result in significant improvements in performance and reliability with a focus on battery thermal heating. Predicting the temperature achieved by the battery during operation not only avoids conditions that lead to thermal runaway but also guarantees that the battery is used optimally within an optimal temperature range. Within the optimal temperature range, several advantages are observed. First, battery efficiency improves significantly as electrochemical processes occur more efficiently. Furthermore, by lowering the possibility of short circuits and improving overall battery safety, thermal stability aids in the prevention of undesirable phenomena like dendrite growth. By lessening the deterioration brought on by thermal degradation processes, thermal optimization also affects battery longevity. Based on experimental tests, a finite element method (FEM) model is developed. A model for thermal runaway propagation is established by combining thermal runaway and conduction models with an Arrhenius law-based combustion model. The study employed a cylindrical Li-ion cell to conduct tests, taking into account three parameters: discharge rate (CRate), ambient temperature (Tamb), and initial battery temperature (T0). An algorithm based on the three variables was developed using the simulation results. The algorithm enables the accurate prediction of rising battery temperature during use, facilitating the setting of an optimal maximum discharge rate considering initial and ambient temperatures, thereby ensuring optimal performance within the desired temperature range. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Thermal Optimization Strategies for Li-Ion Batteries: Predictive Temperature Algorithm | |
type | Journal Paper | |
journal volume | 16 | |
journal issue | 8 | |
journal title | Journal of Thermal Science and Engineering Applications | |
identifier doi | 10.1115/1.4065471 | |
journal fristpage | 81003-1 | |
journal lastpage | 81003-14 | |
page | 14 | |
tree | Journal of Thermal Science and Engineering Applications:;2024:;volume( 016 ):;issue: 008 | |
contenttype | Fulltext |