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Comparison of simulated results of working voltage with experimental data during different ambient temperature (0°C, 25°C, and 35°C) under 1C discharge rate
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An electrochemical Lithium ion battery model was built taking into account the electrochemical reactions. The polarization was divided into parts which were related to the solid phase and the electrolyte mass transport of species, and the electrochemical reactions. The influence factors on battery polarization were studied, including the active mat...
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Rechargeable magnesium/sulfur (Mg/S) batteries are widely regarded as one of the alternatives to lithium‐ion batteries. However, a key factor restricting their application is the lack of suitable electrolyte. Herein, an electrolyte additive that can reduce the polarization voltage is developed and 98.7% coulombic efficiency is realized. The as‐prep...
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... This model was proposed by Newman, Doyle, and Fuller in [47] and it describes the mass (movement of lithium ions) and energy (heat generation and dissipation due to resistive losses and electrochemical reactions) of each species for each domain (anode, cathode, separator) of a battery cell [7]. 6 Li y QO 2 Figure 4. Schematic of 1D electrochemical model for lithium-ion cells [48]. ...
Continuous and accurate state-of-charge estimation is essential for optimal reliability and performance in electric vehicle battery management systems. This work reviews state-of-charge estimation strategies, from straightforward methods like lookup tables and ampere-hour counting to advanced mathematical models, such as electrochemical, observer-assisted equivalent circuit, and impedance-based models that capture cell dynamics. Additionally, data-driven models including fuzzy logic, neural networks, and support vector machines are explored for their ability to leverage large datasets. This review highlights the strengths and limitations of each method, emphasizing the specific contexts in which these strategies can be applied to achieve optimal effectiveness.
... Secondly, during discharge, there is a slight yet noticeable decrease in the discharge potential towards the end, a behavior traditionally associated with polarization by concentration resulting from a depletion of species near the electrodes, table S2 provide values for discharge potentials using both binders where the advantages of AMBB are evident. [40] The constant presence of mobile Li + in SLICPB helps mitigate polarization by concentration, providing an additional advantage; in this regard, it is important to highlight that although the beneficial impact of several binders in protective capability has been reported, no material has been reported to diminish polarization by concentration due to the SLICPBs. [41][42][43][44] At higher C-rates, the disparities between the binders are exacerbated, with a greater difference in capacity between the samples, being higher for AMBB. ...
High voltage spinel LiNi0.5 Mn1.5O4 (LNMO) is a promising material for next generation lithium‐Ion batteries. However, its reactivity near 5 V possess stability and cycling challenges. In this study, a novel integrated approach is employed using a single lithium‐ion conducting polymer binder (SLICPB) to prevent interactions with reactive anions and create a protective layer against electrolyte decomposition. The proposed SLICPB in‐situ polymerization in the LNMO electrodes simplifies the preparation process, reducing costs. SLICPB properties effectively decrease polarization by concentration. For instance, at a discharge capacity of 68 mAh g⁻¹, the voltage hysteresis difference is 0.31 V, enabling higher capacity at 1 C (a 38 % increase) compared to traditional binder electrodes. Notably, this integrated strategy completely replaces the traditional binder without any need for additives, thus avoiding any extra weight in the electrode preparation. Furthermore, SLICPB properties successfully decreasereactivity and diminish the leaching of Mn²⁺, as evaluated through differential electrochemical mass spectrometry and electron paramagnetic resonance, respectively.
... As such, appropriately it is commonly referred to as Newman, or Doyle-Fuller-Newman modelling after some of the other major contributors. [1][2][3][4][5][6][7][8] Given the widespread use of the Newman model, including its implementation in commercial 9 and academic 10 solvers, it is surprising that classical texts [11][12][13][14][15][16][17][18][19] and newer accounts of liquidphase electrostatic potential appear to have major inconsistencies. 20,21 A case in point is the formula commonly used to describe the liquid-phase electrostatic potential of the binary electrolyte in Newman modelling (see Table I for variable symbols) ...
Lithium ion battery cell-level and sub-cell modelling has become almost synonymous with Newman’s Model. This paper sheds light on a specific part of the model, the liquid phase potential, clarifying its difference from the liquid phase potential found in the Butler-Volmer expression. Critically, we show that these two potentials should not be used interchangeably. In fact, the concentration potential gradient differs by a factor of six in standard LiPF6 electrolytes. Consequently, the Butler-Volmer current densities could be affected strongly if the definitions of electrostatic liquid phase potential are inadvertently interchanged.
... In recent years, a great deal of research has been focused on the modeling of Lithium-ion batteries including electrochemical models [3][4][5][6][7][8][9][10][11][12], mechanical models [13][14][15][16][17][18][19][20][21], Battery Thermal Management System/thermal models [22][23][24][25][26][27][28][29][30], and multiphysics models [31][32][33][34][35][36][37] at scales varying from nanolevel to cell level. Such efforts had immense contribution in improving the safety of lithium-ion battery cells. ...
Numerical simulations of heterogeneous structures like battery modules of electric vehicles are challenging due to the various length scales involved in it. Even with the latest computing technology, it is impossible to simulate the crash scene of the full vehicle resolving all length scales. Such hurdles have prevented manufacturers to understand the mechanical response of battery packs in vehicle crash scenarios. In this work, the problem of multiple length scales was solved using the RVE technique based on homogenization theory. An appropriate representative volume element was identified, and a 3D FE model was developed. Classical first-order boundary conditions were used in this research work. The RVE was subjected to several macroscopic deformations, and its response was obtained. The homogenized material properties were computed from the obtained responses, and a material model available in LS-Dyna’s material library was selected and calibrated to describe the nonlinear multiaxial behavior of the homogenized battery module at the macroscale. For validation, the USABC Crush and Drop Tests were simulated for the detailed and homogenized battery modules. The main output of this research is a robust and computationally efficient tool enabling satisfactory integration of a battery pack model to the vehicle for crash simulations, eliminating the need to simulate micro details at macro length scales. This approach significantly reduced the computational cost. For example, for a Drop Test simulation, the homogenized model reduced the simulation time from 40 hours (detailed model) to about 3 minutes, while maintaining a high precision ( R 2 = 0.9871 ) in predicting the load-displacement response. The system level modeling will enable the stakeholders to perform efficient optimization and safety evaluation for full-scale crashworthiness of electric vehicles.
... Design and behavior prediction on electrodes have been concerned extensively since they determine battery performance to a large extent. Chen et al. 6 designed an electrochemical-thermal coupling model for an 18.5 Ah lithium-ion battery and verified the accuracy of the model through experiments with four discharge rates. On the basis of this model, it is found that as the electrode thickness and volume fraction increase, the battery polarization and energy density increase, while the power density decreases. ...
Lithium ion battery is a complex system, and any change in device parameters may significantly affect the overall performance. The prediction of battery behavior based on theoretical simulation is of great significance. In this work, the battery performance with LiNi 1/3 Co 1/3 Mn 1/3 O 2 electrodes of different active material loading amounts was theoretically investigated, such as battery rate performance, capacity decay rate, energy and power density, SOC (State of Charge) change, temperature response, and heat source distribution. A 1D electrochemical–3D thermal coupling model was established, and the accuracy and predictability of the model were verified by experiments. An in-depth analysis of batteries under different discharge rates and batteries with different electrode loading amounts is carried out. The results show that the rate performance and capacity change with different electrode loading amounts, which is attributed to the variation in lithium ion diffusion. At the same time, the change in load affects the growth of lithium dendrites and the distribution of SOC, resulting in the thermal runaway of the battery and shortening the battery life.
... Polarization studies are crucial in determining the longevity of the electrodes (Tian et al., 2019), especially in presence of the two binders used versus their electrochemical efficiency. Polarization results from mechanical side-effects (of electrochemical process) that develop at the EDL interphase (Chen et al., 2018). The process is associated with overpotential, where a reverse electrochemical reaction takes place, reducing efficient cell performance and health (Armstrong and Hirst, 2011). ...
Aluminium waste accumulated in landfills is a solid waste in abundance. Various methods have been employed to alleviate the waste only to yield secondary pollution effects. This study seeks to provide an alternative greener recycling procedure that is beneficial to society in terms of health and economics through energy storage materials. The study aimed to synthesize and characterize aluminium oxide nanoparticles from waste aluminium foil and its potential applications in fabricating aluminium-ion cell, FAIC.¹ Aluminium oxide nanoparticles were obtained by co-precipitation of waste aluminium foils at constant annealing room temperature followed by mechanical milling to nanoparticulate range. The particles were then characterized for particle size and phases (X-ray diffraction), functional groups and optical activity (infra-red and ultra-violet-visible spectroscopy respectively). Cell assembling of FAIC was done using a graphite anode while the cathode had a standard and the synthesized aluminium oxide nanoparticles. Sulfuric acid and magnesium sulfate electrolytes were used with two binders; polyacrylate and silicone adhesives. The average synthesis yield was 40.64 ± 19.69%. Most of the particles had a α-Al2O3 and γ-Al2O3 phase with an average size of 63.763 nm and 66.5144 nm for the two polymorphs respectively. There were several OH-groups coupled to Al–O bonds. The optimal absorption peak was λmax = 237 nm corresponding to a band gap of 5.25eV. The synthesized nanoparticles exhibited great electrochemical potential, nearing the standard one in most of the parameters. The FAIC potential, current, power densities and polarization curves from sulfuric acid electrolyte and polyacrylate binder were significantly higher to those of magnesium sulfate and silicone binder (P > 0.05).
... On the other hand, diffusion polarization develops from changes in the electrolyte concentration due to the current flow through the electrode and electrolyte interface. The electrochemical reaction rate and mass non uniformity can also lead to diffusion polarization [28]. ...
... Solid and liquid phase diffusion polarization increases as the discharge process progresses, and the active material particle size affects solid phase diffusion [28]. Consistent with this, Figures 2 and 6 show that smaller Si anode particle sizes minimize the overall capacity fade effects. ...
... A larger specific surface area leads to improvement in the electrochemical reaction rate, a decrease of the polarization potential and reduction in the activation polarization [28]. There is a high rate of formation of the SEI layer closer to the separator, therefore the electrolyte volume fraction change is more significant in this region. ...
While silicon anodes hold promise for use in lithium-ion batteries owing to their very high theoretical storage capacity and relatively low discharge potential, they possess a major problem related to their large volume expansion that occurs with battery aging. The resulting stress and strain can lead to mechanical separation of the anode from the current collector and an unstable solid electrolyte interphase (SEI), resulting in capacity fade. Since capacity loss is in part dependent on the cell materials, two different electrodes, Lithium Nickel Oxide or LiNi0.8Co0.15Al0.05O2 (NCA) and LiNi1/3Mn1/3Co1/3O2 (NMC 111), were used in combination with silicon to study capacity fade effects using simulations in COMSOL version 5.5. The results of these studies provide insight into the effects of anode particle size and electrolyte volume fraction on the behavior of silicon anode-based batteries with different positive electrodes. It was observed that the performance of a porous matrix of solid active particles of silicon anode could be improved when the active particles were 150 nm or smaller. The range of optimized values of volume fraction of the electrolyte in the silicon anode were determined to be between 0.55 and 0.40. The silicon anode behaved differently in terms of cell time with NCA and NMC. However, NMC111 gave a high relative capacity in comparison to NCA and proved to be a better working electrode for the proposed silicon anode structure.
... All models can be divided into analytical ones, which include electrochemical models [6][7][8][9][10][11] and models based on electrical replacement circuits [12][13][14][15][16][17][18][19], and datadriven ones [20][21][22][23][24][25][26][27][28][29][30][31]. ...
The article deals with the research of the efficiency of modelling the dynamics of voltage change in lithium-ion rechargeable batteries in charging/discharging modes using nonlinear block-oriented systems. Drawing on experimental data, a structural and parametric identification of the Hammerstein, Wiener and Hammerstein-Wiener models with a polynomial structure of the linear dynamic block and piecewise linear static nonlinearities was performed. It has been established that the best modelling accuracy was ensured by using the Hammerstein-Wiener system with a linear model having the 6 th order of the numerator and denominator polynomials and an input delay of 3 samples. It showed 15.67% and 6.2% higher accuracy compared to the Wiener and Hammerstein systems, respectively. The application of those models in battery management systems will make it possible to improve the control quality for battery assemblies of solar and wind power plants in the context of the variable nature of the charging/discharging processes due to the variability of weather conditions and fluctuations in power consumption during a 24-hour period. This will ensure a wider introduction of renewable power generation into existing power systems, which is currently the leading way to ensure sustainable development of the energy sector.
... Thus, many models have been developed in the literature for the prediction of the thermal characteristics of the Li-ion batteries [4,5]. These models can be classified in three main categories including; physics-based electrochemical models [6][7][8][9][10][11][12][13][14][15][16] with high complexity that investigate the battery mechanism in detail, data-based models [3,[17][18][19][20][21][22][23][24][25][26] with relatively low accuracy that utilize empirical relationships to predict the battery dynamics, and equivalent circuit-based models [2,4,5,27,28] with acceptable accuracy and complexity that utilize active and passive electrical components to estimate the battery states [5]. ...
... It is expected that a battery thermal model that should work accurately under standard test conditions such as galvanostatic charging/discharging processes. In literature, there are many studies that investigate the galvanostatic discharging processes at various Crates from 0.5 to 5C [1,[6][7][8][9][10][17][18][19][20][21][22][29][30][31]. However, the number of charging and the driving cycle models of Li-ion batteries under different operating conditions is limited. ...
In this study, a coupled battery model was developed by employing a novel entropic term estimation procedure to predict the thermal behavior of a Li-Ion battery for varying charge and discharge rates under different operating temperatures. In the experimental part, a battery testing system was first used to determine the fundamental thermal and electrical operational behavior of a Li-Ion battery at various galvanostatic charge and discharge rates. Besides, battery tests for two different driving cycle loads based on a prototype electrical vehicle model have been conducted to simulate dynamic conditions. The first set of experiments consists of three different galvanostatic charge and discharge rates (0.5C, 1.0C and 1.5C) for three different ambient temperatures and it is used to predict the reversible heat generation term in the model. It is shown that the difference between the model and experimental temperatures are less than 2°C for all galvanostatic charge and discharge rates at different ambient conditions. In the case of dynamic conditions, the maximum temperature difference between the measured and predicted values is found to be less than 2.5°C for the tested highway-FTP driving cycle and less than 1°C for the tested CITY-I USA driving cycle at two different ambient temperatures. In summary, the predictions of the developed model that includes a novel entropic term estimation procedure are found to be accurate enough to simulate the transient thermal characteristics of the battery cell under dynamic conditions.
... This is the same equation that was analyzed in the previous models (Equations (6) and (18)), and whose initial condition and boundary conditions are also the same. A solution is proposed in parameters of the following type: ...
This paper introduces a physical–chemical model that governs the lithium ion (Li-ion) battery performance. It starts from the model of battery life and moves forward with simplifications based on the single-particle model (SPM), until arriving at a more simplified and computationally fast model. On the other hand, the implementation of this model is developed through MATLAB. The goal is to characterize an Li-ion cell and obtain its charging and discharging curves with different current rates and different cycle depths, as well as its transitory response. In addition, the results provided are represented and compared, and different methods of estimating the state of the batteries are applied. They include the dynamics of the electrolyte and the effects of aging caused by a high number of charging and discharging cycles of the batteries. A complete comparison with the three-parameter method (TPM) is represented in order to demonstrate the superiority of the applied methodology.
