An electrochemical–thermal model based on dynamic responses for lithium iron phosphate battery

Li, Jie; Cheng, Yun; Wang, Tingyun; Tang, Yiwei; Lin, Yongcheng; Zhang, Zhian; Liu, Yexiang

doi:10.1016/j.jpowsour.2014.01.007

Cited by 170 publications

(124 citation statements)

References 49 publications

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“…The thermal parameters required are given in Table 4, the data in Table 4 are adapted from the reference [19,[25][26][27][28][29]. [29] 160 2700 903 Cu [29] 400 8900 385 can [19] 44.5 7850 475…”

Section: Simulation Model Buildingmentioning

confidence: 99%

“…Analysed in the same way, it presents the same conclusion under the operation conditions of the BRT lines and suburban lines. Therefore, the Tr model is assumed to be a linear regression relation of two variables, as shown in Equation (26). It can be seen from Figure 14 that the temperature rise (T r ) and the number of cycles (c), and the temperature rise (T r ) and the heat transfer coefficient (h) of the battery are approximately linear.…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

“…Therefore, the T r model is assumed to be a linear regression relation of two variables, as shown in Equation (26).…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

“…In order to estimate the values of α 0 , α 1 , α 2 , n times the independent observed values of T r , c, and h are obtained by COMSOL, which should meet Equation (26), and they are shown in Equation (27).…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

“…Then, the relationship amongst T r , c, and h can be calculated by Equation (26) to Equation (34) using MATLAB. When the ambient temperature is 30 • C, the relationship amongst T r , c, and h under the operation conditions of the regular lines is shown in Equation (35), under the operation conditions of the BRT lines is shown in Equation (36), and under the operation conditions of the suburban lines is shown in Equation (37).…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

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Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

Siyu

Chen

2017

Energies

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This study investigated the heat problems that occur during the operation of power batteries, especially thermal runaway, which usually take place in high temperature environments. The study was conducted on a ternary polymer lithium-ion battery. In addition, a lumped parameter thermal model was established to analyze the thermal behavior of the electric bus battery system under the operation conditions of the driving cycles of the Harbin city electric buses. Moreover, the quantitative relationship between the optimum heat transfer coefficient of the battery and the ambient temperature was investigated. The relationship between the temperature rise (T r ), the number of cycles (c), and the heat transfer coefficient (h) under three Harbin bus cycles have been investigated at 30 • C, because it can provide a basis for the design of the battery thermal management system. The results indicated that the heat transfer coefficient that meets the requirements of the battery thermal management system is the cubic power function of the ambient temperature. Therefore, if the ambient temperature is 30 • C, the heat transfer coefficient should be at least 12 W/m 2 K in the regular bus lines, 22 W/m 2 K in the bus rapid transit lines, and 32 W/m 2 K in the suburban lines.

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Section: Simulation Model Buildingmentioning

confidence: 99%

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

“…Therefore, the T r model is assumed to be a linear regression relation of two variables, as shown in Equation (26).…”

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

Section: Multiple Linear Regression Analysismentioning

confidence: 99%

See 3 more Smart Citations

Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

Siyu

Chen

2017

Energies

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Digital Twin‐Assisted Degradation Diagnosis and Quantification of NMC Battery Aging Effects During Fast Charging

Guo,

Sun,

Guo

et al. 2024

Advanced Energy Materials

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A comprehensive understanding of aging mechanisms and degradation diagnosis under fast charging in battery electric vehicles is needed. However, there is a lack of tools to capture both macroscopic and microscopic parameters, still focusing on experiment results analysis. To get a comprehensive degradation insight for improved service life, this study explores aging mechanisms in LiNi0.5Co0.2Mn0.3O2 graphite batteries under different fast charging conditions (0.6C to 2C) for up to 1000 equivalent full cycles (EFCs). An improved digital twin is used to quantify aging effects and identify aging modes, combining electrochemical techniques with post‐mortem analysis for assessing chemical and structural degradation. After 1000 EFCs, the dominant aging modes are loss of active material (LAM) in the negative electrode and loss of lithium inventory (LLI), surpassing LAM in the positive electrode. Compared to constant current charging, multistep fast charging effectively mitigates Li plating effects and graphite cracking, resulting in a 13% reduction in LAM. Additionally, optimizing the depth of discharge leads to at least 4% reductions in LLI and 16% in LAMpos. This study proves the electrochemical digital twin's potential for quantifying aging effects and serves as a basis for future physics‐informed machine learning to predict aging behavior and modes.

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Development of Small‐Scale Monitoring and Modeling Strategies for Safe Lithium‐Ion Batteries

Zhang

Chen

Wang

et al. 2021

Batteries & Supercaps

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Lithium‐ion batteries (LIBs) have the advantages of high energy density, stable working voltage, long cycling life, and no memory effect. They have been widely used as power sources for electric vehicles (EVs) and hybrid electric vehicles (HEVs). However, during periods of usage or storage, some unfavorable factors such as thermal runaway, volumetric expansion, and growth of lithium dendrites can severely reduce the reliability and safety of LIBs. Therefore, an accurate health estimation system and a reliable life expectancy strategy toward LIBs have been proved significantly important. However, due to the large volume and high price, most characterization methods in the laboratory cannot be applied directly to commercial electric devices. Therefore, small portable monitor methods are more practical. In this review, we first systematically reviewed the recent progress of modeling methods towards LIBs. Some typical modeling strategies (e. g., thermal models, electrical models and aging models) and advanced sensors which can be imbedded in LIBs are emphatically introduced and compared. At last, some promising directions of development on portable in‐situ monitor strategies are also predicted and supposed.

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An electrochemical–thermal model based on dynamic responses for lithium iron phosphate battery

Cited by 170 publications

References 49 publications

Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

Determination of the Optimum Heat Transfer Coefficient and Temperature Rise Analysis for a Lithium-Ion Battery under the Conditions of Harbin City Bus Driving Cycles

Digital Twin‐Assisted Degradation Diagnosis and Quantification of NMC Battery Aging Effects During Fast Charging

Development of Small‐Scale Monitoring and Modeling Strategies for Safe Lithium‐Ion Batteries

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