Low error estimation of half-cell thermodynamic parameters from whole-cell Li-ion battery experiments: Physics-based model formulation, experimental demonstration, and an open software tool

Low C-rate charge and discharge experiments, plus complementary differential voltage or differential capacity analysis, are among the most common battery characterization methods. Here, we adapt the multi-species, multi-reaction (MSMR) half-cell thermodynamic model to low C-rate cycling of whole-cell Li-ion batteries. MSMR models for the anode and cathode are coupled through whole-cell charge balances and cell-cycling voltage constraint equations, forming the basis for model-based estimation of MSMR half-cell parameters from whole-cell experimental data. Emergent properties of the whole-cell, like slippage of the anode and cathode lithiation windows, are also computed as cells cycle and degrade. A sequential least-square optimization scheme is used for parameter estimation from low-C cycling data of Samsung 18650 NMC|C cells. Low-error fits of the open-circuit cell voltage (e.g., under 5 mV mean absolute error for charge or discharge curves) and differential voltage curves for fresh and aged cells are achieved. We explore the features (and limitations) of using literature reference values for the MSMR half-cell thermodynamic parameters (reducing our whole-cell formulation to a 1-degree-of-freedom fit) and demonstrate the benefits of expanding the degrees of freedom by letting the MSMR parameters be tailored to the cell under test, within a constrained neighborhood of the half-cell reference values. Bootstrap analysis is performed on each dataset to show the robustness of our fitting to experimental noise and data sampling over the course of 600 cell cycles. The results show which specific MSMR insertion reactions are most responsible for capacity loss in each half-cell and the collective interactions that lead to whole-cell slippage and changes in useable capacity. Open-source software is made available to easily extend this model-based analysis to other labs and battery chemistries.

Starch as the Flame Retardant for Electrolytes in Lithium-Ion Cells

The main purpose of this work is to illustrate the flame retardant properties of corn starch that is used as an additive to the classic electrolytes in lithium-ion cells. The advantages of using natural biomass include the increased biodegradability of the cell, compliance with the slogan of green chemistry, as well as the widespread availability and easy isolation of this ingredient. Due to the non-Newtonian properties of starch, it increases work safety and prevents the occurrence of thermal runaway as a shear-thinning fluid in the event of a collision. Thus, its use may, in the future, prevent explosions that affect electric cars with lithium-ion batteries without significantly degrading the electrochemical parameters of the cell. In the manuscript, the viscosity test, flash point measurements, the SET (self-extinguishing time) test and conductivity measurements were performed, in addition to the determination of electrochemical impedance spectroscopy (EIS) for the anode system. Additionally, the kinetic and thermodynamic parameters, for both flow and conductivity, were determined for a deeper analysis; this constitutes the scientific novelty of this study. Through mathematical analysis, it was shown that the optimal amount of added starch is 5%. This is supported primarily by the determined kinetic and thermodynamic parameters and the fact that the system did not gel during heating.

Effects of mustard husk, wheat straw and flaxseed residue blending on combustion behavior of high ash coal and petroleum coke blends

This present work reports the combustion studies of coal, petroleum coke (PC) and biomass blends to assess the effects of the mustard husk (MH), wheat straw (WS) and flaxseed residue (FR) blending towards improvement of coal combustion characteristics. Ignition temperature (TS), maximum temperature (TP), burnout temperature (TC), activation energy (AE) and thermodynamic parameters (ΔH, ΔG and ΔS) were analyzed to evaluate the impact of biomass and PC blending on coal combustion. Experimental results indicate that coal and PC have inferior combustion characteristics compared to MH, WS and FR. With the increase in WS content in blends from 10 to 30%, TS reduced from 371 to 258OC, TP decreased from 487 to 481OC, inferring substantial enhancements in combustion properties. Kinetic analysis inferred that blended fuel combustion could be explained mostly using reaction models, followed by diffusion-controlled and contracting sphere models. Overall, with the increase in FR mass in blends from 10 to 30%, AE decreased from 108.97 kJ/mol to 70.15 kJ/mol signifying ease of combustion. Analysis of synergistic effects infers that higher biomass addition improves coal and PC blends' combustion behavior through catalytic effects of alkali mineral matters present in biomass. Calculation of thermodynamic parameters signified that combustion of coal and PC is challenging than biomasses, however, blending of biomass makes the combustion process easier.

Machine Learning Enhanced Dynamic Response Modelling of Superelastic Shape Memory Alloy Wires

Superelastic shape memory alloy (SMA) wires exhibit superb hysteretic energy dissipation and deformation capabilities. Therefore, they are increasingly used for the vibration control of civil engineering structures. The efficient design of SMA-based control devices requires accurate material models. However, the thermodynamically coupled SMA behavior is highly sensitive to strain rate. For an accurate modelling of the material behavior, a wide range of parameters needs to be determined by experiments, where the identification of thermodynamic parameters is particularly challenging due to required technical instruments and expert knowledge. For an efficient identification of thermodynamic parameters, this study proposes a machine-learning-based approach, which was specifically designed considering the dynamic SMA behavior. For this purpose, a feedforward artificial neural network (ANN) architecture was developed. For the generation of training data, a macroscopic constitutive SMA model was adapted considering strain rate effects. After training, the ANN can identify the searched model parameters from cyclic tensile stress–strain tests. The proposed approach is applied on superelastic SMA wires and validated by experiments.

Thermodynamics of Generalized Fermi Systems in a Harmonic Trap

Thermodynamics of the generalized ideal Fermi systems in the two-and three-dimensional harmonic traps are respectively calculated by the Tsallis entropy in this paper. The influences of the trap and q-number on the thermodynamic parameters (epically the heat capacity) are analysed in detail. The results yield a well agreement with the classical cases.

Численное моделирование поведения карбидов при высокоэнергетическом воздействии

The results of research on modeling thermodynamic parameters of shock-wave loading of carbides with different stoichiometric ratios are presented. The carbides are considered as a mixture of carbon with the corresponding component. The calculations of pressure, compression and temperature values under shock-wave loading for solid and porous carbides in the range of pressure values above 3 GPa are performed. The model calculations are compared with the known experimental results on the shock-wave loading of carbides with different porosity values. The possibility of modeling the behavior according to the proposed method for carbides for which there are no experimental data at high dynamic loads is shown.

Mechanical Properties and Thermodynamic Parameters of Sr2 RuO4 and Sr2 RuO2 F2 Compounds under Pressure and Temperature Effects: Voigt–Reuss–Hill Approximations and Debye Model

We present a first-principles study of the elastic and thermodynamic properties of the Sr2 RuO4 -xFx alloy (x = 0, 2). Computations are carried out using the WIEN2K code based on a non-relativistic full–potential linearized augmented plane wave (FP-LAPW) method within the density functional theory (DFT). The Voigt–Reuss–Hill approximation method is applied to analyze the elastic constants, Poisson ratio, bulk, shear, and Young modulus at zero pressure and temperature using ELASTIC 1.0 software. The Sr2 RuO4 and Sr2 RuO2 F2 tetragonal phases are mechanically stable because the elastic constants satisfy Born’s mechanical stability condition. In addition, we performed a quasi-harmonic Debye model calculation using the GIBBS2 package to predict the thermodynamic properties and their temperature and pressure dependencies. Thermodynamic parameters such as the Gibbs free energy, heat capacity, Grüneisen parameter, and Debye temperature are successfully obtained and discussed