A self-discharge model of Lithium-Sulfur batteries based on direct shuttle current measurement

In the group of post Lithium-ion batteries, Lithium-Sulfur (Li-S) batteries attract a high interest due to their high theoretical limits of the specific capacity of 1672 Ah kg−1 and specific energy of around 2600 Wh kg−1. However, they suffer from polysulfide shuttle, a specific phenomenon of this chemistry, which causes fast capacity fade, low coulombic efficiency, and high self-discharge. The high self-discharge of Li-S batteries is observed in the range of minutes to hours, especially at a high state of charge levels, and makes their use in practical applications and testing a challenging process. A simple but comprehensive mathematical model of the Li-S battery cell self-discharge based on the shuttle current was developed and is presented. The shuttle current values for the model parameterization were obtained from the direct shuttle current measurements. Furthermore, the battery cell depth-of-discharge values were recomputed in order to account for the influence of the self-discharge and provide a higher accuracy of the model. Finally, the derived model was successfully validated against laboratory experiments at various conditions

Effects of cycling on lithium-ion battery hysteresis and overvoltage

Currently, lithium-ion batteries are widely used as energy storage systems for mobile applications. However, a better understanding of their nature is still required to improve battery management systems (BMS). Overvoltages and open-circuit voltage (OCV) hysteresis provide valuable information regarding battery performance, but estimations of these parameters are generally inaccurate, leading to errors in BMS. Studies on hysteresis are commonly avoided because the hysteresis depends on the state of charge and degradation level and requires time-consuming measurements. We have investigated hysteresis and overvoltages in Li(NiMnCo)O2/graphite and LiFePO4/graphite commercial cells. Here we report a direct relationship between an increase in OCV hysteresis and an increase in charge overvoltage when the cells are degraded by cycling. We fnd that the hysteresis is related to difusion and increases with the formation of pure phases, being primarily related to the graphite electrode. These fndings indicate that the graphite electrode is a determining factor for cell efciency.Peer ReviewedPostprint (published version

A Comprehensive Study of Hydrolyzed Polyacrylamide as a Binder for Silicon Anodes.

Silicon anodes have a high theoretical capacity for lithium storage, but current composite electrode formulations are not sufficiently stable under long-term electrochemical cycling. The choice of polymeric binder has been shown to impact stability and capacity of silicon anodes for electrochemical energy storage. While several promising polymeric binders have been identified, there is a knowledge gap in how various physicochemical properties-including adhesion, mechanical integrity, and ion diffusion-impact electrochemical stability and performance. In this work, we comprehensively investigate the physical properties and performance of a molecular-weight series (3-20 × 106 g/mol) of partially hydrolyzed polyacrylamide (HPAM) in silicon anodes. We quantify the mechanical strength, electrolyte uptake, adhesion to silicon, copper, and carbon, as well as electrochemical performance and stability and find that HPAM satisfies many of the properties generally believed to be favorable, including good adhesion, high strength, and electrochemical stability. HPAM does not show any electrolyte uptake regardless of any molecular weight studied, and thin films of mid- and high-molecular-weight HPAM on silicon surfaces suppress lithiation of silicon. The resulting composite electrodes exhibit an electrochemical storage capacity greater than 3000 mAh/g initially and 1639 mAh/g after 100 cycles. We attribute capacity fade to failure of mechanical properties of the binder or an excess of the solid electrolyte interphase layer being formed at the Si surface. While the highest-molecular-weight sample was expected to perform the best given its stronger adhesion and bulk mechanical properties, we found that HPAM of moderate molecular weight performed the best. We attribute this to a trade-off in mechanical strength and uniformity of the resulting electrode. This work demonstrates promising performance of a low-cost polymer as a binder for Si anodes and provides insight into the physical and chemical properties that influence binder performance

A 3D Framework for Characterizing Microstructure Evolution of Li-Ion Batteries

Lithium-ion batteries are commonly found in many modern consumer devices, ranging from portable computers and mobile phones to hybrid- and fully-electric vehicles. While improving efficiencies and increasing reliabilities are of critical importance for increasing market adoption of the technology, research on these topics is, to date, largely restricted to empirical observations and computational simulations. In the present study, it is proposed to use the modern technique of X-ray microscopy to characterize a sample of commercial 18650 cylindrical Li-ion batteries in both their pristine and aged states. By coupling this approach with 3D and 4D data analysis techniques, the present study aimed to create a research framework for characterizing the microstructure evolution leading to capacity fade in a commercial battery. The results indicated the unique capabilities of the microscopy technique to observe the evolution of these batteries under aging conditions, successfully developing a workflow for future research studies

Structure-Induced Reversible Anionic Redox Activity in Na Layered Oxide Cathode

Anionic redox reaction (ARR) in lithium- and sodium-ion batteries is under hot discussion, mainly regarding how oxygen anion participates and to what extent oxygen can be reversibly oxidized and reduced. Here, a P3-type Na0.6[Li0.2Mn0.8]O2 with reversible capacity from pure ARR was studied. The interlayer O-O distance (peroxo-like O-O dimer, 2.506(3) Å), associated with oxidization of oxygen anions, was directly detected by using a neutron total scattering technique. Different from Li2RuO3 or Li2IrO3 with strong metal-oxygen (M-O) bonding, for P3-type Na0.6[Li0.2Mn0.8]O2 with relatively weak Mn-O covalent bonding, crystal structure factors might play an even more important role in stabilizing the oxidized species, as both Li and Mn ions are immobile in the structure and thus may inhibit the irreversible transformation of the oxidized species to O2 gas. Utilization of anionic redox reaction (ARR) on oxygen has been considered as an effective way to promote the charge-discharge capacity of the layered oxide cathodes for lithium- or sodium-ion batteries. The detailed mechanism of ARR, in particular how crystal structure affects and coordinates with the ARR, is not yet well understood. In the present work, a combination of X-ray and neutron total scattering measurements has been performed to study the structure of the prototype P3-type layered Na0.6[Li0.2Mn0.8]O2 with pure ARR. Unique structural characteristics, rather than prevailing knowledge of covalency of metal-oxygen, enable the stabilization of the crystal structure of Na0.6[Li0.2Mn0.8]O2 along with the ARR. This work suggests that reversible ARR can be manipulated by proper structure designs, thus to achieve high lithium or sodium storage in layered oxide cathodes. For P3-type Na0.6[Li0.2Mn0.8]O2 with relatively weak Mn-O covalent bonding, crystal structure factors play an important role in stabilizing the oxidized species, inhibiting the irreversible transformation of the oxidized species to O2 gas. The finding is important for better design of layered oxide positive materials with higher reversible capacity via the introduction of a reversible anionic redox reaction

Gaussian process regression for forecasting battery state of health

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201

Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial Informatic