Mathematical Model for Combined Effect of SEI Formation and Gas Evolution in Li-Ion Batteries

Rashid, Mir Akmam Noor; Gupta, Amit

doi:10.1149/2.0041410eel

Cited by 34 publications

(25 citation statements)

References 22 publications

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“…It is also crucial to optimize the electrode architecture to significantly increase the population of active particles to sustain the overall galvanostatic current and at the same time to improve the homogeneity of the distribution of active particles to prevent the extent of shocks and fractures induced by high local currents. 24 From the perspective of numerical simulation, it is important to identify the impact of SEI formation with the associated gas release within the electrode on the transport properties of Li ions and cycling performance of LIBs during discharge and charge 12 to further speed up the optimization of electrode structures. Altogether, the three dimensional microscale investigations of gas in the present study contribute to the understanding of the complex discharge and charge processes.…”

Section: Resultsmentioning

confidence: 99%

Three-Dimensional Visualization of Gas Evolution and Channel Formation inside a Lithium-Ion Battery

Sun

Markötter

Manke

et al. 2016

ACS Appl. Mater. Interfaces

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Gas generation within lithium ion batteries (LIBs) gives rise to safety concerns that question their applicability. By employing synchrotron X-ray imaging, the gas and channel evolution occurring in an operating LIB have been directly visualized in their inherent 3D state as a function of discharge and charge. Using the spatial 3D distribution of gas bubbles and channels, the active particles that dictate the performance of a functional LIB were identified and visualized in 3D. Delithiation and lithiation are interpreted as the process of activating particles continuously in a step-bystep way. The present work not only demonstrates the generation and evolution of gas within LIB in 3D, but also reveals the distribution of active particles for the first time. These fundamentally findings presented here shed light on a range of processes that could not previously be characterized in 3D and can provide practical guidance for the

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Section: Resultsmentioning

confidence: 99%

Three-Dimensional Visualization of Gas Evolution and Channel Formation inside a Lithium-Ion Battery

Sun

Markötter

Manke

et al. 2016

ACS Appl. Mater. Interfaces

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“…Figure 8b shows the exemplary behavior of a cell with a uniform conductivity κ SEI of 1 × 10 −7 S m −1 for lithium-ions and electrons within the SEI in the order of often used values in literature. 47,77 The plot shows that we get a totally different power behavior as a result and, therefore, prediction of available energy with a model that does not distinguish between the conductivity of electrons and lithium-ions in the SEI -although we calculate the same capacity fade. …”

Section: Capacity and Power Fade Behavior With New Model-depicted Inmentioning

confidence: 89%

“…Lawder et al 46 studied the influence of different driving cycle profiles on the capacity fade of electric vehicle batteries and ascribed the total capacity fade to SEI growth. The effects of gas evolution due to SEI growth were modeled by Rashid et al 47 On the cathode side, Cai et al 48 implemented an SOC independent manganese disproportionation which …”

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confidence: 99%

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

Kindermann

Keil

Frank

et al. 2017

J. Electrochem. Soc.

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In this paper we introduce a pseudo two-dimensional (P2D) model for a common lithium-nickel-cobalt-manganese-oxide versus graphite (NCM/graphite) cell with solid electrolyte interphase (SEI) growth as the dominating capacity fade mechanism on the anode and active material dissolution as the main aging mechanism on the cathode. The SEI implementation considers a growth due to non-ideal insulation properties during calendar as well as cyclic aging and a re-formation after cyclic cracking of the layer during graphite expansion. Additionally, our approach distinguishes between an electronic (σ SEI ) and an ionic (κ SEI ) conductivity of the SEI. This approach introduces the possibility to adapt the model to capacity as well as power fade. Simulation data show good agreement with an experimental aging study for NCM/graphite cells at different temperatures introduced in literature. © The Author Lithium-ion batteries are one of the most promising candidates for energy storage in future stationary storage systems and electric vehicles.1-3 Enormous research efforts have been conducted to get a thorough understanding of the system "lithium-ion cell" and to further develop it for higher energy and power density, higher safety standards as well as longer cycle life. 4 The aging behavior of lithium-ion batteries has been a focus issue of battery research since the introduction of lithium-ion cells by Sony in 1991. 5 Reviews by Agubra et al., 6,7 Arora et al., 8 Aurbach et al., 9,10 Birkl et al., 11 Broussely et al., 12 Verma et al. 13 and Vetter et al. 14 are just a few examples of the extensive literature regarding aging behavior. Commonly accepted and experimentally verified aging phenomena as mentioned in the previously cited literature are electrolyte decomposition leading to solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI) growth, solvent co-intercalation, gas evolution with subsequent cracking of particles, a decrease of accessible surface area and porosity due to SEI growth, contact loss of active material particles due to volume changes during cycling, binder decomposition, current collector corrosion, metallic lithium plating and transition-metal dissolution from the cathode. The listed aging mechanisms can be assigned to three different categories that are a loss of lithium-ions (LLI), an impedance increase and a loss of active material (LAM). 12,[15][16][17][18] The LLI is synonymous to a decrease in the amount of cyclable lithium-ions as they are trapped in a passivating film on either of the electrodes or in plated metallic lithium. Due to the growth of the passivating layers and/or the formation of rock-salt in the cathode (residue of the cathode active material after transition-metal dissolution), kinetic transport of lithium-ions through those inactive areas is limited and results in an impedance rise. An LAM can be caused by the dissolution of transition-metal-ions from the cathode bulk material, changes in the electrode composition and/or changes in crystal structure of the a...

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“…No experimental data are available for the identification of such relation, and the empirical equation described in Ref. 37 is here adopted. The side reaction overpotential is defined as…”

Section: Model For Simulations: Li-ion Pseudo Two-dimensional (P2d) Mmentioning

confidence: 99%

Design of Piecewise Affine and Linear Time-Varying Model Predictive Control Strategies for Advanced Battery Management Systems

Torchio¹,

Magni²,

Braatz³

et al. 2017

J. Electrochem. Soc.

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Advanced Battery Management Systems (ABMSs) are necessary for the optimal and safe operation of Li-ion batteries. This article proposes the design of ABMSs based on Model Predictive Control (MPC). In particular, we consider MPC strategies based on piecewise affine approximations (PWAs) of a first-principles electrochemical battery model known in the literature as the pseudo two-dimensional (P2D) model. The use of model approximations is necessary since the P2D model is too complex to be included in the real-time calculations required by MPC. PWAs allow to well describe the electrochemical phenomena occurring inside the battery. On the other side, the accuracy of such models increases with the number of considered partitions, which also increases the model complexity and the online computational cost of MPC. Linear time-varying (LTV) approximations, which are obtained by linearizing accurate PWAs around a nominal trajectory, are proposed as a way to further reduce online computational costs. The obtained results demonstrate the suitability of MPC based on PWARX and LTV model approximations to provide ABMSs with high performance.

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Mathematical Model for Combined Effect of SEI Formation and Gas Evolution in Li-Ion Batteries

Cited by 34 publications

References 22 publications

Three-Dimensional Visualization of Gas Evolution and Channel Formation inside a Lithium-Ion Battery

Three-Dimensional Visualization of Gas Evolution and Channel Formation inside a Lithium-Ion Battery

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

Design of Piecewise Affine and Linear Time-Varying Model Predictive Control Strategies for Advanced Battery Management Systems

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