Modeling extreme deformations in lithium ion batteries

Mallarapu, Anudeep; Kim, Jinyong; Carney, Kelly S.; DuBois, Paul; Santhanagopalan, Shriram

doi:10.1016/j.etran.2020.100065

Cited by 41 publications

(19 citation statements)

References 21 publications

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“…The aging model (Figure 1C) is also implemented to elucidate its effects on TR. It is assumed that the thickness evolution of the negative electrode to form the SEI is the main mechanism of aging 19 and that it occurs uniformly throughout the cell; this suggests that aging only affects the chemical heat generation of the negative electrode. Nevertheless, this assumption can account for the degradation of all components because the chemical degradation of each component is correlated with temperature.…”

Section: Mathematical Modeling For Thermal Runawaymentioning

confidence: 99%

“…Further,

R_{*}

also depends on each component and conforms to the Arrhenius law. The chain reaction of the TR in LFP cells commences from the SEI layer and sequentially propagates toward the negative electrode, positive electrode, and electrolyte, because the onset of TR varies with the temperature, depending on the material characteristics 19 . Hence,

R_{*}

for each component should be appropriately modeled to account for its chemical degradation characteristics by Arrhenius equation, although the chemical degradation behavior of each component is similar.…”

Section: Mathematical Modeling For Thermal Runawaymentioning

confidence: 99%

“…The chain reaction of the TR in LFP cells commences from the SEI layer and sequentially propagates toward the negative electrode, positive electrode, and electrolyte, because the onset of TR varies with the temperature, depending on the material characteristics. 19 Hence, R Ã for each component should be appropriately modeled to account for its chemical degradation characteristics by Arrhenius equation, although the chemical degradation behavior of each component is similar. The characteristics of R Ã with the onset temperature of TR are in the following order: SEI layer < negative electrode < positive electrode < electrolyte.…”

Section: Chemical Reaction Degradationmentioning

confidence: 99%

“…Specifically, the thermal abuse condition was modeled to investigate the mechanism and characteristic temperatures of TR 15,16 . Exothermic reactions of TR have also been modeled using ordinary differential equations in order to observe electrical short circuits 17 ; several other modeling approaches 18,19 have been investigated to describe the electric abuse condition. A multiphysics safety model has also been proposed to describe the mechanical abuse condition 20 …”

Section: Introductionmentioning

confidence: 99%

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Detailed modeling investigation of thermal runaway pathways of a lithium iron phosphate battery

Kwak

Kim

Hong³

et al. 2021

Intl J of Energy Research

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Summary This study investigates the thermal runaway (TR) pathways of a lithium iron phosphate (LFP) battery to establish important considerations for its operation and design. A multiphysics TR model was developed by accounting for several phenomena, such as the chemical reaction degradation of each component, thermodynamics, and aging. The parameters used in the model were experimentally optimized under two scenarios, and TR pathways were subsequently characterized under the electric and thermal abuse conditions. In the electric abuse condition, the onset time and peak temperature are important metrics representing TR hazards because they are strongly correlated with the TR pathways subjected to the heat balance between internal heat generation and heat exchange with the environment. The surface‐to‐volume ratio (S/V) was identified as the most important metric for ensuring thermal safety, as it results in delayed TR with higher S/V under the electric abuse condition and accelerated TR under the thermal abuse condition. From both operational and design perspectives, this contradiction should be carefully considered. Moreover, with regard to aging, TR pathways varied under the electric and thermal abuse conditions. Aging only affected the peak temperature of the TR pathways under the electric abuse condition, whereas it only altered the onset temperature of TR pathways under the thermal abuse condition. This difference can be attributed to the different heat sources and heat balance mechanisms. Thus, understanding the dependency of TR under the electric and thermal abuse conditions on aging and cooling is important for the optimal design of LFP cells with high thermal stability and for establishing novel thermal management strategies. This intensive investigation can contribute toward ensuring thermal stability and reliability of LFP batteries from various perspectives.

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Section: Mathematical Modeling For Thermal Runawaymentioning

confidence: 99%

“…Further,

R_{*}

R_{*}

Section: Mathematical Modeling For Thermal Runawaymentioning

confidence: 99%

Section: Chemical Reaction Degradationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detailed modeling investigation of thermal runaway pathways of a lithium iron phosphate battery

Kwak

Kim

Hong³

et al. 2021

Intl J of Energy Research

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“…The partial incremental capacity analysis is conducted with support vector regression and second-order extended Kalman filtering strategies. [41][42][43][44] The temperature and charge-discharge current rates are used as the main variables, in which the characteristics are initialized as dependent variables.…”

Section: Introductionmentioning

confidence: 99%

Improved Compound Correction‐Electrical Equivalent Circuit Modeling and Double Transform‐Unscented Kalman Filtering for the High‐Accuracy Closed‐Circuit voltage and State‐of‐Charge Co‐Estimation of Whole‐Life‐Cycle Lithium‐Ion batteries

Wang

Takyi‐Aninakwa

et al. 2022

Energy Tech

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For complex energy storage conditions, it is necessary to monitor the state‐of‐charge (SOC) and closed‐circuit voltage (CCV) status accurately for the reliable power supply application of lithium‐ion batteries. Herein, an improved compound correction‐electrical equivalent circuit modeling (CC‐EECM) method is proposed by considering the influencing effects of ambient temperature and charge–discharge current rate variations to estimate the CCV. Then, an improved adaptive double transform‐unscented Kalman filtering (ADT‐UKF) method is constructed with recursive sampling data correction to estimate the nonlinear SOC. A dynamic window function filtering strategy is constructed to obtain the new sigma point set for the online weighting coefficient correction. For a temperature range of 5–45 °C, the CCV for the improved CC‐EECM responds well with a maximum error of 0.008608 V, and the maximum SOC estimation error is 6.317%. The proposed ADT‐UKF method improves the CCV and SOC estimation reliability and adaptability to the time‐varying current rate, temperature, and aging factors.

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Mechanism and Control Strategies of Lithium‐Ion Battery Safety: A Review

Lai,

Zhao,

Song

et al. 2024

Small Methods

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Lithium‐ion batteries (LIBs) are extensively used everywhere today due to their prominent advantages. However, the safety issues of LIBs such as fire and explosion have been a serious concern. It is important to focus on the root causes of safety accidents in LIBs and the mechanisms of their development. This will enable the reasonable control of battery risk factors and the minimization of the probability of safety accidents. Especially, the chemical crosstalk between two electrodes and the internal short circuit (ISC) generated by various triggers are the main reasons for the abnormal rise in temperature, which eventually leads to thermal runaway (TR) and safety accidents. Herein, this review paper concentrates on the advances of the mechanism of TR in two main paths: chemical crosstalk and ISC. It analyses the origin of each type of path, illustrates the evolution of TR, and then outlines the progress of safety control strategies in recent years. Moreover, the review offers a forward‐looking perspective on the evolution of safety technologies. This work aims to enhance the battery community's comprehension of TR behavior in LIBs by categorizing and examining the pathways induced by TR. This work will contribute to the effective reduction of safety accidents of LIBs.

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Modeling extreme deformations in lithium ion batteries

Cited by 41 publications

References 21 publications

Detailed modeling investigation of thermal runaway pathways of a lithium iron phosphate battery

Detailed modeling investigation of thermal runaway pathways of a lithium iron phosphate battery

Improved Compound Correction‐Electrical Equivalent Circuit Modeling and Double Transform‐Unscented Kalman Filtering for the High‐Accuracy Closed‐Circuit voltage and State‐of‐Charge Co‐Estimation of Whole‐Life‐Cycle Lithium‐Ion batteries

Mechanism and Control Strategies of Lithium‐Ion Battery Safety: A Review

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