Practical Capacity Fading Model for Li-Ion Battery Cells in Electric Vehicles

Lam, Long; Bauer, Pavol

doi:10.1109/tpel.2012.2235083

Cited by 231 publications

(120 citation statements)

References 27 publications

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Section: Capacity Fading Model Ignoring Temperature Effectmentioning

confidence: 99%

“…Unlike the laboratory testing condition, cells in the field-use condition are usually run under complex temperature profiles. Previous researches indicate that the capacity fading process of Lithium-ion cells is significantly influenced by temperature [5,7,10]. In [10], capacity fading of Lithium-ion cells can be divided into true capacity fading and temporary capacity loss.…”

mentioning

confidence: 99%

“…Previous researches indicate that the capacity fading process of Lithium-ion cells is significantly influenced by temperature [5,7,10]. In [10], capacity fading of Lithium-ion cells can be divided into true capacity fading and temporary capacity loss. True capacity fading leads to permanent capacity loss as a result of lithium ion and active material consuming, where high temperatures will accelerate the fading rate.…”

mentioning

confidence: 99%

“…Consequently, it will lead to the SEI formation on the cracked surfaces, which will consume the active Lithium-ion. This diffusion-inducedstresses failure mechanism usually makes capacity fade linearly over cycles [1,10,18]. …”

mentioning

confidence: 99%

“…2 as a case of constant stress degradation tests, and the regression-based model is often used to handle this type of degradation data [6]. In the previous literature [1,10,18], capacity fading of Lithium-ion cells is generally assumed to follow a linear trend with cycles, namely:where the intercept a and slope b are unknown parameters, c(n) is the cell capacity at nth cycle.The estimators of model parameters for each cell can be obtained with the linear regression techniques. The end of life (EOL) for Lithium-ion cell is often defined as the number of charge/discharge cycles before cell capacity falls below 80% of its rated capacity [13].…”

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Cycle life prediction of lithium-ion cells under complex temperature profiles

Cheng

Pan

et al. 2016

EiN

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These studies mentioned above, however, are mainly conducted based on the cycle life tests under one or more deterministic temperature levels, in which the high-precision temperature chamber and rigid data acquisition methods are required. Unlike the laboratory testing condition, cells in the field-use condition are usually run under complex temperature profiles. Previous researches indicate that the capacity fading process of Lithium-ion cells is significantly influenced by temperature [5,7,10]. In [10], capacity fading of Lithium-ion cells can be divided into true capacity fading and temporary capacity loss. True capacity fading leads to permanent capacity loss as a result of lithium ion and active material consuming, where high temperatures will accelerate the fading rate. On the other hand, temporary capacity loss is due to the temperature drop in a certain cycle, which is somewhat recoverable if the temperature goes back. A more accurate capacity fading model can be obtained by considering these temperature effects, especially for the cells operating under field-use conditions.However, because of the difficulty in modeling the complicated temperature effects, the existing papers regarding capacity fading modeling or cycle life prediction under complex temperature profiles are very rare. In most of them, the classic regression-based approach [6] is adopted, which assumes that field conditions are deterministic or to simply use the mean value of temperatures while ignore their variability. This approach may result in significant prediction errors. For predicting the cycle life of Lithium-ion cells without the temperature chamber, this paper proposes a cycle life prediction method considering complex temperature profiles, including a cycle life test plan and an improved capacity fading model.In our test, cells experience ambient temperature that continuously varies at all times. It will lead to the variance of cell capacity. The variance contains abundant information about cycle life and the relationship between cycle life and temperature. If we can effectively mine the information from the immense performance data using data modeling methods, the cycle life of Lithium-ion cells can be predicted accurately.In this paper, firstly, the cycle life test plan for Lithium-ion cells under complex temperature profiles is introduced. Then, the classic regression-based life prediction method which ignores temperature effects is reviewed. Based on the classic method, we establish a more accurate capacity fading model, by taking into account the effects of temperature on both the actual capacity fading and the temporal capacity loss. Using the data acquired from the cycle life test, the parameters of the model are estimated. At last, the cycle life of this type of Lithium-ion cells is predicted. Experimental Testing proceduresIn the cycle life test, 6 LiFePO 4 18650 cells, which are indexed as Cell #1, Cell #2,· · ·, Cell #6 respectively, are used to charge and discharge repetitively. The parameters setup of these cells i...

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Section: Capacity Fading Model Ignoring Temperature Effectmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Cycle life prediction of lithium-ion cells under complex temperature profiles

Cheng

Pan

et al. 2016

EiN

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Simulation of Factor on SOC Inconsistency within a Series‐Connected Lithium‐Ion Battery Pack

Zhang

Yang

Chi

2022

Advcd Theory and Sims

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This paper outlines a new simulation approach to study the effect of factors on the state of charge (SOC) consistency of Li‐ion battery packs. The proposed method is based on a system model and the Monte Carlo method. The system model includes 100 serial connected equivalent circuit models and their respective parameters. This paper divides the internal and external factors into two categories: independent factors such as internal resistance, capacity, Coulombic efficiency, leakage current, and comprehensive factors related to temperature. The Monte Carlo method simulates the differences and distribution of independent factors among cells. Temperature‐dependent factors are modeled with empirical formulas. Simulation results show that the main factors affecting SOC consistency are differences in battery temperature, leakage current, and Coulombic efficiency. Meanwhile, slight differences in capacity and internal resistance have little effect on SOC inconsistency. Furthermore, this paper demonstrates the effectiveness of the system model.

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Investigation of the Effect of Temperature on Lithium‐Sulfur Cell Cycle Life Performance Using System Identification and X‐Ray Tomography

Shateri

Auger

Fotouhi

et al. 2022

Batteries & Supercaps

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In this study, cycle life performance of a prototype lithiumsulfur (LiÀ S) pouch cell is investigated using system identification and X-ray tomography methods. LiÀ S cells are subjected to characterization and ageing tests while kept inside a controlledtemperature chamber. After completing the experimental tests, two analytical approaches are used: i) The parameter variations of an equivalent-circuit model due to ageing are determined using a system identification technique. ii) Physical changes of the aged LiÀ S cells are analyzed using X-ray tomography. The results demonstrate that LiÀ S cell's degradation is significantly affected by temperature. Comparing to 10 °C, LiÀ S cell capacity fade happens 1.4 times faster at 20 °C whereas this number increases to 3.3 at 30 °C. In addition, X-ray results show a significant swelling when temperature rises from 10 to 20 °C, correspondingly the gas volume increases from 13 to 62 mm 3 .

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Practical Capacity Fading Model for Li-Ion Battery Cells in Electric Vehicles

Cited by 231 publications

References 27 publications

Cycle life prediction of lithium-ion cells under complex temperature profiles

Cycle life prediction of lithium-ion cells under complex temperature profiles

Simulation of Factor on SOC Inconsistency within a Series‐Connected Lithium‐Ion Battery Pack

Investigation of the Effect of Temperature on Lithium‐Sulfur Cell Cycle Life Performance Using System Identification and X‐Ray Tomography

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