A Mathematical Model to Study Capacity Fading in Lithium Ion Batteries: Formation and Dissolution Reactions

Ramesh, Srivatsan; Krishnamurthy, Balaji

doi:10.1149/2.0221504jes

Cited by 16 publications

(12 citation statements)

References 33 publications

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“…In the past decade, considerable accomplishments have been made in building models to predict the cycle life of LIBs . These models can be categorized into three types.…”

Section: Introductionmentioning

confidence: 71%

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Time‐Effective Accelerated Cyclic Aging Analysis of Lithium‐Ion Batteries

et al. 2019

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We propose a time-effective framework for accelerated cyclic aging analysis of lithium-ion batteries. The proposed framework involves the coupling of a physico-chemical capacity-fade model that considers the cyclic aging mechanisms of the LiMn 2 O 4 /graphite cell, with a physics-based porous-composite electrode model to predict cycling performance at different temperatures. A one-dimensional simple empirical life model is then developed from the coupled physico-chemical capacityfade model and the physics-based porous-composite electrode model predictions. An accelerated cyclic aging analysis based on the principle of time-temperature superposition is performed using the developed one-dimensional simple life empirical model. The proposed framework is used to predict the maximum number of cycles and the highest temperature required for accelerated cyclic aging analysis of LiMn 2 O 4 /graphite cells. The efficacy of the proposed framework is validated with experimental cycle-performance data obtained from LiMn 2 O 4 /graphite coin cells at 25 and 60°C.

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“…In the past decade, considerable accomplishments have been made in building models to predict the cycle life of LIBs . These models can be categorized into three types.…”

Section: Introductionmentioning

confidence: 71%

“…We also assume that the solvent species generated from the dissolution reaction at the film/electrode interphase diffuse back to the electrode/film interphase and reacts with Li ions to form the SEI layer. The formation and dissolution of SEI at the anode can be respectively expressed as:

true S + 2 L i^{+} + 2 e^{-} \to_{}^{k_{S, 2}} P + E

true P + E \to_{}^{k_{S, 3}} S + 2 L i^{+} + 2 e^{-}

…”

Section: Methodsmentioning

confidence: 99%

Time‐Effective Accelerated Cyclic Aging Analysis of Lithium‐Ion Batteries

et al. 2019

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“…The equations for the OCV of both electrodes depend on the stoichiometric coefficient of the electrode [17], whose value is β ± = c ± ss (t)/c ± s,max . The parameter β − is substituted in the variable x of Equation (24), and β + is replaced by y in Equation ( 25) [27].…”

Section: Single-particle Model (Spm)mentioning

confidence: 99%

“…This model serves to solve the equation of the diffusion of lithium in the solid particle, and it can be complemented by the remaining algebraic equations of the SPM. When the model uses volume-averaged equations and a parabolic profile approximation for the solid-phase concentration, the solid-phase partial differential equation reduces to two ordinary differential equations [24][25][26][27][28].…”

Section: Three-parameter Model (Tpm)mentioning

confidence: 99%

Lithium-Ion Battery Modeling Including Degradation Based on Single-Particle Approximations

2020

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This paper introduces a physical–chemical model that governs the lithium ion (Li-ion) battery performance. It starts from the model of battery life and moves forward with simplifications based on the single-particle model (SPM), until arriving at a more simplified and computationally fast model. On the other hand, the implementation of this model is developed through MATLAB. The goal is to characterize an Li-ion cell and obtain its charging and discharging curves with different current rates and different cycle depths, as well as its transitory response. In addition, the results provided are represented and compared, and different methods of estimating the state of the batteries are applied. They include the dynamics of the electrolyte and the effects of aging caused by a high number of charging and discharging cycles of the batteries. A complete comparison with the three-parameter method (TPM) is represented in order to demonstrate the superiority of the applied methodology.

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“…Unfortunately, the transition metal ions of LiNi x Co y Mn 1 −x −y O 2 could catalyze electrolyte decomposition reaction due to its high oxidizing activity, which results in severe capacity fading of the battery [4][5][6] . Especially, when the battery is used at a relatively high temperature ( > 45 °C), dramatic losses of its energy and power happen, leading to a short serving life time [7][8][9] . And there will be more gas generated in the cells at higher temperature, bringing about security problems.…”

Section: Introductionmentioning

confidence: 99%