Analysis of pulse and relaxation behavior in lithium-ion batteries

Bernardi, Dawn M.; Go, Joo-Young

doi:10.1016/j.jpowsour.2010.06.107

Cited by 109 publications

(104 citation statements)

References 25 publications

(56 reference statements)

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“…Indications of the varying Li concentration along the radial direction of graphite particles during charge and discharge pulses are provided by dynamic battery models. [22][23][24] In this case, an initial LiC 12 /C phase boundary ( Figure 15b) followed by a LiC 6 /LiC 12 phase boundary (Figure 15c) will move from the surface to the center of graphite particles. Again since SANS is sensitive only to surface layers of ∼500 nm thickness or to phase boundaries in domains of this size, the relevant scattering contributions will thus only be from electrolyte vs. surface layer and from phase boundaries inside the particle.…”

Section: Figure 14 (Color Online): A) Cell Potential and B) Integratmentioning

confidence: 99%

In Operando Small-Angle Neutron Scattering (SANS) on Li-Ion Batteries

Seidlmayer

Hattendorff

Buchberger

et al. 2015

J. Electrochem. Soc.

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Small-angle neutron scattering is a tool providing information on nanostructures of objects in the order of 1-300 nm. In this experiment a pouch bag lithium ion battery cell was investigated with SANS ex situ, in situ and in operando during charging and discharging. LiNi 0.33 Mn 0.33 Co 0.33 O 2 was used as cathode and graphite as anode material. The small-angle neutron scattering measurements were performed on the SANS-1 instrument at the FRM II neutron source of the Heinz Maier-Leibnitz Zentrum (MLZ) in Garching, Germany. Ex situ measurements of components of the cell as well as static in situ and dynamic in operando SANS experiments were performed with a complete Li-ion pouch bag cell. The cell was charged and discharged twice with C/3 and small-angle neutron scattering data were collected during the measurements. The observed intensity data were then evaluated and changes of the total scattering in the measured Q-range are correlated to the lithiation processes occurring inside the cell. Thus we can show that SANS can be used as a tool to monitor kinetic processes in Li-ion batteries in operando and non-destructively. Li-ion batteries have been used widely as power sources in transportable electronic devices and new markets such as hybrid and all battery electric vehicles are developing.1 This has also raised an enhanced interest in the development of analytical methods to study batteries during operation ("in operando"). Besides the improvement in energy and power density, researchers are trying to prolong the lifetime of lithium ion batteries. Cycle and storage life of Li-ion batteries are critical for electric vehicle or stationary power storage applications. A major degradation effect is the continuous decomposition of electrolyte -leading also to a growing solid electrolyte interface (SEI), a passivating layer on typically the anode active material (graphite), thus resulting in loss of conductivity and higher cell resistance. For the cathode phase transitions, structural disorder and metal dissolution are major aging effects. Corrosion of various materials in the battery and mechanical contact loss of active particles or current collectors are also an issue.2-4 One major goal is to understand the general reaction mechanism of the Li-intercalation process in the anode respectively cathode materials. The fundamental understanding of battery processes is a key for improving battery performance (e.g., in terms of energy density and power density) and lifetime.The small-angle scattering method is commonly used to gain information about the nanostructure of the investigated materials (i.e., size, volume and shape of particles). In combination with the special properties of neutrons, like the high penetration depth in materials, small-angle neutron scattering (SANS) can be used as a powerful tool for in situ investigations of Li-ion batteries. SANS can help to understand changes on the nanoscale of particles in cycled cells and during cell cycling. Our study's primary goal is to adapt, develop and extend this ...

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Section: Figure 14 (Color Online): A) Cell Potential and B) Integratmentioning

confidence: 99%

In Operando Small-Angle Neutron Scattering (SANS) on Li-Ion Batteries

Seidlmayer

Hattendorff

Buchberger

et al. 2015

J. Electrochem. Soc.

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“…1 In this paper, we use the Newman model 2,3 and COMSOL Multiphysics 4,5 to expand predictions about PPC behavior as pulse times increase reaching pseudo steady state polarization. Bernardi and Go 6 also utilized a pulse discharge method to gather data and a similar model to analyze the performance and voltage evolution, and the modeled results matched very well with their experimental data.…”

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confidence: 67%

“…Figure 2 also shows the measured half cell data for the negative electrode. The plateau 6 at 88 mV corresponds with LiC 6 and LiC 12 , the 125 mV plateau 6 corresponds with LiC 12 , LiC 18 , and LiC 27 , with the 200 and up mV 6 associated with LiC 36 and LiC 72 . Figure 2 also shows the active use range for 0% to 100% SOC in regard to the half cell data for the large pouch battery.…”

Section: Mathematical Breakdown Of Individual Overvoltages-utilizingmentioning

confidence: 97%

“…As mentioned 1 studying battery voltage vs current density [1][2][3][4][5][6] remains difficult, in part since the SOC changes as the battery is used. Previous work experimentally evaluates a voltage vs current density relationship at a constant SOC.…”

mentioning

confidence: 99%

“…This allowed us to expand the SOC testing to 10%, 40%, and 70%, from previously reported 6 range of 35% and 65%. Additionally, this model relied on fewer calculated parameters, and could only fit the kinetic rate constant and diffusion in the active particles of the electrode materials.…”

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confidence: 99%

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Pulse Polarization for Li-Ion Battery under Constant State-of-Charge: Part II. Modeling of Individual Voltage Losses and SOC Prediction

DuBeshter¹,

Jorné²

2017

J. Electrochem. Soc.

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A Li-Ion battery, as most batteries, is an unsteady state system. In order to generate polarization curves under constant state of charge (SOC), an experimentally tedious task, we used a pulse discharge method where the initial SOC was adjusted such that at the end of the pulse and during the relaxation the SOC is pre-determined and constant. In this paper the pulse discharge data were simulated using Newman's model, allowing the identification of individual overpotentials under various discharge times, C-rates, and SOC. The agreement between the model and experimental results allowed the construction of pulse polarization curves (PPC) and the identification of individual overvoltages: charge transfer kinetics, ionic mass transport, and solid-state diffusion. The pulse polarization curves converged by the 240 seconds pulse discharge, forming a quasi steady-state polarization curve for the battery, which can be used to determine the SOC of a battery by measuring its steady voltage and current density. In electric cars this method can resolve the driver's range anxiety associated in accurately determining the SOC, especially at low SOC. In a previous paper, 1 we experimentally explored the pulse discharge method in Lithium Ion Battery (LIB) and the ability to generate pulse polarization curves (PPC) from that data at discrete states of charge (SOC) and various pulse durations. As mentioned 1 studying battery voltage vs current density 1-6 remains difficult, in part since the SOC changes as the battery is used. Previous work experimentally evaluates a voltage vs current density relationship at a constant SOC.1 In this paper, we use the Newman model 2,3 and COMSOL Multiphysics 4,5 to expand predictions about PPC behavior as pulse times increase reaching pseudo steady state polarization. Bernardi and Go 6 also utilized a pulse discharge method to gather data and a similar model to analyze the performance and voltage evolution, and the modeled results matched very well with their experimental data.In contrast, this work compared the voltages at the end of the pulse discharge and after the battery had returned to an equilibrium at rest state. This allowed us to expand the SOC testing to 10%, 40%, and 70%, from previously reported 6 range of 35% and 65%. Additionally, this model relied on fewer calculated parameters, and could only fit the kinetic rate constant and diffusion in the active particles of the electrode materials. While this involved more experimental results for a battery archetype, the resulting model was usable over a wider range of SOC and performance scenarios than previously demonstrated. This allowed the study of what conditions would allow a battery to achieve a quasi steady state, which is useful for performance analysis and SOC prediction.From a conceptual point of view, a short pulse discharge exhibits voltage losses most attributed to charge transfer kinetic and ohmic losses. As the pulse discharge times increase, the voltage losses increasingly depend on the Li + concentration gradient forming f...

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Recycling of Lithium‐Ion Batteries

Hanisch

Diekmann

Stieger

et al. 2015

Handbook of Clean Energy Systems

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The use of lithium‐ion batteries ( LIB s) has grown since the market entry of portable power tools and consumer electronic devices. Soon, the need for LIB will rise, when they are used in hybrid and full electric vehicles as well as in energy storage systems to enable the use of renewable energies. To prevent a future shortage of cobalt, nickel, and lithium and to enable a sustainable life cycle of these technologies, new recycling processes for LIBs are needed. These new processes have to regain not only cobalt, nickel, copper, and aluminum from spent battery cells but also a significant share of lithium. Therefore, this article approaches unit operations and their combination to set up for efficient LIB recycling processes, especially considering the task to recover high rates of valuable materials with regard to involved safety issues. Further discussed unit operations are Deactivation/discharging of the battery Disassembly of battery systems (specifically for EV‐battery systems ) Mechanical processes (including inert crushing, sorting, and sieving processes and a special case: thermomechanical separation) Hydrometallurgical processes Pyrometallurgical processes Specific dangers are associated with LIB recycling processes: electrical dangers, chemical dangers, burning reactions, and potential interactions of the single dangers. Furthermore, industrial process chains, already in use, as well as research approaches are summarized. The processes of the companies Retriev Technologies , Recupyl , Batrec , Inmetco , Xstrata , Umicore , Accurec , AEA Technology , OnTo T echnology, and Lion Engineering are discussed and illustrated briefly. A closer look is given to some results of the research project LithoRec .

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Analysis of pulse and relaxation behavior in lithium-ion batteries

Cited by 109 publications

References 25 publications

In Operando Small-Angle Neutron Scattering (SANS) on Li-Ion Batteries

In Operando Small-Angle Neutron Scattering (SANS) on Li-Ion Batteries

Pulse Polarization for Li-Ion Battery under Constant State-of-Charge: Part II. Modeling of Individual Voltage Losses and SOC Prediction

Recycling of Lithium‐Ion Batteries

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