Capacity fade modelling of lithium-ion battery under cyclic loading conditions

Ashwin, T.R.; Chung, Yongmann M.; Wang, Jihong

doi:10.1016/j.jpowsour.2016.08.054

Cited by 128 publications

(58 citation statements)

References 29 publications

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“…53,55,59,61,63 In these models, this non-linear aging behavior can be emulated by a high power fade, though, which shortens charging and discharging due to high overpotentials that decrease the usable capacity. 43,61 Measurements in literature ascribe this non-linear aging behavior to lithium plating 64,65 as well as to degradation mechanisms on the cathode. 5,48,50 For the here introduced model we chose to implement a cathode dissolution reaction as the responsible mechanism for the non-linear aging behavior.…”

Section: 62mentioning

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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Section: 62mentioning

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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“…[26][27][28][29][30] This modeling class was chosen for its accuracy in describing transport phenomena in the solid and liquid phase of a single electrode stack.…”

Section: Modelmentioning

confidence: 99%

“…[26][27][28][29][30] This modeling class was chosen for its accuracy in describing transport phenomena in the solid and liquid phase of a single electrode stack. 31 As the P2D model is extensively discussed in literature, we only show the modifications to the basic model and included a short summary with all relevant parameters in the Appendix.…”

Section: Modelmentioning

confidence: 99%

Reducing Inhomogeneous Current Density Distribution in Graphite Electrodes by Design Variation

Kindermann

Oßwald

Ehlert

et al. 2017

J. Electrochem. Soc.

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Inhomogeneous utilization of electrodes and consequent limitations in the operating conditions are a severe problem, reducing lifetime and safety. By using a previously developed laboratory cell setup, we are able to show an inhomogeneous retrieval of lithium-ions from a graphite electrode throughout the layer with spatial resolution for two different graphites. After provoking inhomogeneities via constant current operations, equilibration processes are recorded and are assigned to two different effects. One effect is an equilibration inside the particles (intra-particle) from surface to bulk whereas the second effect is an equalization between the particles (inter-particle) to reach a homogeneous degree of lithiation in each particle throughout the electrode layer. With the recorded data, we implemented a P2D model with multiple particle sizes and considered the electrode thickness in several separate domains. Using the relaxation data of intra-and inter-particle relaxation for parametrizing the model, we investigated the influence of different solid and liquid phase parameters. As the liquid phase parameters scaled via porosity and tortuosity showed the biggest impact, we performed a design variation study to achieve a more homogeneous utilization of the electrode. Structuring the electrode to lower tortuosity is identified as the most promising design variation for homogeneous utilization. Lithium-ion cells are the electrochemical power source of choice, not only for portable electronic devices but also for plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs). Despite significant improvements regarding energy density and cycle stability, drawbacks remain, preventing the acceptance of EVs as a coequal alternative to internal combustion engine vehicles.Resulting from advancements in the quality of manufacturing processes, the ratio between active and inactive components could be improved by realizing thicker electrode coatings and thinner current collector foils.1 This increase in the energy density of the cells, however, comes with longer charging times due to a reduced rate capability. While concepts such as intelligent charging strategies require a comprehensive framework to be implemented, 2 the most obvious approach is to increase the charging power. As presented by Tesla's Supercharger concept, the battery is charged up to 80% state of charge (SOC) within 40 min using a charging power of up to 120 kW. 3 The high charging power requires high charging currents due to current limitations for 400 V high voltage on-board power systems.Various publications address the variations in current density distribution and the resulting SOC inhomogeneities. The impact of the cell design and the resulting equalization processes along the electrodes are presented using experimental cells [4][5][6][7][8][9][10][11] or by a modeling approach.12,13 The resulting inhomogeneous utilization of the active material leads to undesired side reactions and accelerated degradation, especially lithium plating 14,15 and ...

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“…However, it will decrease with the cycle numbers rising, that is, battery aging effects. For example, the battery capacity will drop down, internal resistance will increase and the chemical reaction rate will become slow . Such consequences caused by aging process of batteries are the main reason for the drop of operating performance of large pored electrical machines and limit the development of equipment which applies batteries as the energy storage system.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the battery capacity will drop down, internal resistance will increase and the chemical reaction rate will become slow. [1][2][3] Such consequences caused by aging process of batteries are the main reason for the drop of operating performance of large pored electrical machines and limit the development of equipment which applies batteries as the energy storage system. Thus, it is important to predict the aging process, and the remaining useful lifetime (RUL) of lithium-ions batteries.…”

Section: Introductionmentioning

confidence: 99%

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

Garg

Gao

et al. 2019

Int J Energy Res

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Summary In this paper, a method composed of state of health (SOH) testing experiments and artificial intelligence simulation is proposed to carry out the study on the change of battery characteristic during its operation and generate mathematical models for the prediction of aging behaviour of battery. An experiment comprising of multidisciplinary parameters‐based SOH detection is conducted to study the battery aging characteristics from several aspects (ie, electrochemistry, electric, thermal behaviour and mechanics). In total, 200 sets of data (corresponding 200 charging/discharging cycles) are collected from the experiment. The data obtained from the first 150 cycles are employed in generation of the models. The result of sensitivity analysis based on the obtained genetic programming models shows that it is better to apply voltage value at the end of charging step, charging time and cycle number to predict the operational performance of the battery. The average predicted accuracy of model (without stress) is 94.52%, whereas the average predicted accuracy of model (with stress effect) is 99.42%. The proposed models could be useful for defining the optimised charging strategy, fault diagnosis and spent batteries disposal strategies.

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Capacity fade modelling of lithium-ion battery under cyclic loading conditions

Abstract: A pseudo two-dimensional (P2D) electro-chemical lithium-ion battery model is presented in this paper to study the capacity fade under cyclic charge-discharge conditions. The

Cited by 128 publications

References 29 publications

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

A SEI Modeling Approach Distinguishing between Capacity and Power Fade

Reducing Inhomogeneous Current Density Distribution in Graphite Electrodes by Design Variation

Aging model development based on multidisciplinary parameters for lithium‐ion batteries

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