Utilization Of Lithium-ion Batteries Research Articles

Thermo-electric modeling and analysis of lithium-ion battery pack for E-mobility

AbstractElectric Vehicles (EVs) have emerged as a viable and environmentally sustainable alternative to traditional internal combustion vehicles by utilizing a clean energy source. The advancement and expansion of electric cars rely on the progress of electrochemical batteries. The utilization of Lithium-Ion Batteries is widespread primarily because of its notable energy density. Changes influence the performance of these batteries in temperature. The Thermal Management System of the battery is one of the very important systems in EVs to improve the performance and life of the battery. The geometrical spacing of the cell modules is considered identical for a more accurate comparison of temperature distribution. For better cooling and heat dissipation, the battery pack’s two sides are kept entirely open to facilitate the inflow of air. In this work, active BTMS solutions are selected and analyzed using the development of three-dimensional free, open-source OpenFOAM computational fluid dynamics simulations for accurate thermal modeling and hotspot zones in cylindrical battery packs. The outcome of the simulations is compared using parameters like temperature distribution in battery cells, battery modules, and heat generation. Among all the cell temperature zones, the temperature maximum is near the sixth cell of the module depth. OpenFOAM results validated with the existing literature’s experimental and Ansys results. Air cooling is utilized for cooling performance because of its relatively simple structure and lightweight.

Synthesis and electrochemical studies of 1,1’-binaphthyl-2,2’-diol for aqueous rechargeable lithium-ion battery applications

The constant increase in the utilization of lithium-ion batteries (LIBs) in various field applications, including electrical vehicles and electronic devices, has led researchers to focus on their multiple path developments to obtain new electrode materials. The practical development of these electrode materials, based on organic and inorganic moieties, is challenging for various groups of LIB scientists. The concept of organic electrode materials is highly competitive with inorganic electrode materials because of the accessibility of more active sites with structural diversity, high energy and power density, environmental friendliness potential sustainability, and low cost. Herein, 1,1’-binaphthyl-2,2’-diol (BINOL) is investigated as an organic electrode material that contains two hydroxyl groups that act as active centers. The oxidative coupling process is employed to synthesize BINOL and so obtained product was characterized by using FT-IR, 1H-NMR and MASS techniques. The electrochemical investigations were carried out using sat. Li2SO4 electrolytic medium at three-electrode cell system. The Cyclic voltammetry (CV) has provided information on the anodic behavior of the material and its stability studied at different scan rates. The battery performance of the cell BINOL | Sat. Li2SO4 | LiMn2O4 by galvanostatic charge-discharge potential limit (GCPL) shows 197/171mAhg−1 specific capacity and 90% columbic efficiency. The electrochemical kinetic obtained by potentiostatic electrochemical impedance spectroscopy (PEIS) shows a semi-infinite diffusion process.

Optimizing Remaining Useful Life Estimation of Lithium-Ion Batteries: A Particle Swarm Optimization-Based Grey Prediction Model

Accurate estimation of the age and condition of lithium-ion batteries (LIBs) is paramount for their safe and economically viable utilization. However, assessing the degradation of these power units proves challenging due to their dependence on various environmental and usage factors. In this study, we propose an efficient Particle Swarm Optimization (PSO)-based Grey Theory prediction model to determine the Remaining Useful Life (RUL) of lithium-ion batteries.
 The proposed model utilizes PSO to optimize the coefficients of a grey prediction model, enabling accurate forecasting of the remaining useful life of LIBs. Our results demonstrate that the presented model outperforms conventional grey prediction models in terms of both accuracy and stability. Furthermore, the proposed model offers simpler predictions compared to existing models in the literature.
 By introducing this promising technique, our study contributes to the precise forecasting of the RUL of lithium-ion batteries and holds potential for applications in similar domains. This research serves as a significant step towards ensuring effective management and utilization of LIBs, promoting their reliability and safety.

ANN-Fuzzy Hybrid Control Strategy for MPPT of Grid-connected PV System with Battery Storage under Fast-charging Atmospheric Condition

Abstract: The use of solar energy through photovoltaic arrays is continually expanding and has recently been regarded as one of the cleanest sources of energy. Increasing the amount of electricity provided to the load is one method for lowering the cost of solar systems. Contrarily, altering the load creates a divergence from the maximum power point (MPP) and changes the operating point of the solar conversion system. Due to this, attention has been given to MPPT techniques that can be used with solar systems in numerous research investigations. In this paper, we implement an MPPT method based on an Artificial Neural Network (ANN). This method combined two controllers, ANN and fuzzy logic controller, under the name ANNFuzzy logic hybrid. In the first stage, ANN can determine Vmpp from irradiation and temperature, and both of them are variable. In the second stage, this Vmpp has been corrected by implementing Fuzzy logic in order to minimize the error of the voltage and VMPP. ANN-fuzzy hybrid has been simulated in Matlab-Simulink and was found to be the best solution to follow the maximum power point when irradiation and temperature are varied. The energy extracted from PV is delivered to a battery in order to inject this power into the smart grid by using an inverter controlled by PID. Finally, a LC filter has been used to eliminate the harmonics and compensate for the reactive power. For energy storage, we consider the utilization of lithiumion batteries, which are recognized as an optimal solution for storing energy efficiently. To manage the charge and discharge of the battery, we employ a PID controller and a buck-boost converter. Through this research, we aim to explore the performance and effectiveness of the hybrid ANN-fuzzy simulation for the boost converter system. By combining the capabilities of ANN and fuzzy logic, we expect to achieve improved control and optimization of the power conversion processes in the system. Additionally, the integration of lithium-ion batteries and the use of the PID controller and buck-boost converter allow for efficient management of energy storage and retrieval. Overall, this study investigates the hybrid ANN-fuzzy simulation for the boost converter system, highlighting the integration of various components and control techniques to enhance the performance and efficiency of the system.

Analyzing the global warming potential of the production and utilization of lithium-ion batteries with nickel-manganese-cobalt cathode chemistries in European Gigafactories

This study evaluates the global warming potential (GWP) impact of producing lithium-ion batteries (LIBs) in emerging European Gigafactories. The paper presents a cradle-to-gate (CTG) life cycle assessment (LCA) of nickel-manganese-cobalt (NMC) chemistries for battery electric vehicle (BEV) applications. We consider three scenarios to cover the most probable production routes in Germany, France, and Italy, foreseen as the largest European LIB producers by 2030. The energy demand for manufacturing considers two cases: electricity only and a mix of heat and electricity. The results show that European Gigafactories can reduce the overall GWP relative to 1 kWh of NMC battery, with respect to Chinese NMC LIBs, in a range of 32–60%. This corresponds to a decrease in equivalent CO2 emission of 32–81 kg CO2 eq., depending on the location, the energy demand and the NMC chemistry, if the whole production takes place in the facility. French Gigafactories obtain the upper bound of this reduction. A sensitivity analysis of the source of the lithium compound, used to produce the active cathode material, shows that increasing the nickel content decreases the GWP impact per kWh of battery capacity. However, NMC622 generates less equivalent CO2 than NMC811, for lithium compound produced from Chilean brine. In addition, a simplified analysis of the utilization phase of two different classes of BEVs shows the positive effects of the regional LIB production and of the low carbon intensity of the electricity mix.

Modeling of the Battery Pack and Battery Management System towards an Integrated Electric Vehicle Application

The transportation sector is under increasing pressure to reduce greenhouse gas emissions by decarbonizing its operations. One prominent solution that has emerged is the adoption of electric vehicles (EVs). As the electric vehicles market experiences rapid growth, the utilization of lithium-ion batteries (LiB) has become the predominant choice for energy storage. However, it is important to note that lithium-ion battery technology is sensitive to factors, like excessive voltage and temperature. Therefore, the development of an accurate battery model and a reliable state of charge (SOC) estimator is crucial to safeguard against the overcharging and over-discharging of the battery. Numerous studies have been conducted to address lithium-ion battery cell modeling and SOC estimations. These studies have explored variations in the number of RC networks within the model and different estimation methods. However, it is worth mentioning that the capacity of a single lithium-ion battery cell is relatively low and cannot be directly employed in electric vehicles. To meet the total capacity and voltage requirements for electric vehicles, multiple cells are typically connected in series or parallel configurations to form a battery pack. Surprisingly, this aspect has often been overlooked in previous research. To tackle this overlooked challenge, our study introduces a comprehensive battery pack model and an advanced Battery Management System (BMS). We then integrate these components into an electric vehicle model. Subsequently, we simulate the integrated EV-BMS model under the conditions of four different urban driving scenarios to replicate real-world driving conditions. The BMS that we have developed includes an Extended Kalman Filter (EKF)-based SOC estimation system, a mechanism for controlling coolant flow, and a passive cell-balancing algorithm. These components work together to ensure the safe and efficient operation of the battery pack within the electric vehicles.

Investigation of the Electrochemical and Thermal Characteristics of NCM811-21700 Cylindrical Lithium-Ion Battery: A Numerical Study and Model Validation

Recently, there has been growing recognition of the significance of energy and environmental challenges. Utilization of lithium-ion batteries in electric vehicles has shown considerable potential and benefits for tackling these issues. The effective management of battery temperature has become a crucial factor in the advancement and widespread adoption of lithium-ion batteries in electric vehicles. In this study, a thermo-coupled pseudo-two-dimensional (P2D) electrochemical model is employed to simulate the heat generation of the NCM811-21700 cylindrical battery cell at various discharge rates at an ambient temperature of 25 °C, and is validated by experimental data. The validation results demonstrate that the thermo-coupled P2D model can effectively predict the battery voltage curve during the discharge process with less than 4% errors. Although there is a slightly larger error in the temperature prediction during the battery 2C and 3C discharge processes, the maximum error approaches 10%, which is still generally within an acceptable range. In addition, the battery’s electrochemical and thermal characteristics during discharge are presented. The suggested thermo-coupled electrochemical model can be used for applications in the thermal management system of the NCM811-21700 battery.

Chain length dependence of structural and transport properties of single lithium-ion conducting polymer electrolytes: A molecular dynamics simulation study

In this work, the parameters affecting the performance of polymer electrolytes, for utilization in lithium-ion batteries (LiBs) are investigated. For this purpose, by using density functional theory (DFT) and molecular dynamics (MD) simulation, the structural and transport properties of single lithium-ion conducting polymer electrolytes (SLICPEs) based on boron ethylene glycol (BEG), as the anion, with the molecular structure ofBO3−−C2H4On−BO4− are evaluated. The calculated structural properties, include density, glass transition temperature, and radial distribution function (RDF). The simulated transport properties are diffusion coefficient, transference number, and ionic conductivity. In the first step, the optimization of molecular structures is obtained by DFT calculations with B3LYP functional approximation and Mullikan partial charges for the polymer chain lengths of 1, 4, 8, and 15 monomers. Then, by using MD simulations and an ten-stages compression-expansion procedure, the studied electrolyte systems are equilibrated. The results indicate that the transference numbers of these electrolytes are significantly higher than those of commonly used electrolytes based on polyethylene oxide (PEO). Inspection of the obtained results indicates that the diffusion and ionic conductivity coefficients are influenced by the interaction of Li+ with oxygen atoms in the polymer chain. Analysis of the transport properties of Li+ along the polymer chain structure reveals that the presence of a boron atom in the polymer chain backbone induces a significant non–uniform charge distribution along the polymer chain and as a result weakens the interaction between Li+ with an oxygen atom in the polymer chain. Therefore compared to the conventional electrolytes, Li+ can move more freely along the polymer chain, and as a result, the performance of LiBs is promoted significantly.

Noble metal single-atoms for lithium-ion batteries: A booster for ultrafast charging/discharging in carbon electrodes

Despite the popularity of single metal atoms (SMAs) in many applications, their utilization in lithium-ion batteries (LIBs) has seldom been explored to date. To examine the potential benefits of SMAs in LIBs, we prepared porous carbons where one of three different noble metals (Au, Ag, and Pt) was embedded in the form of SMAs via a simple galvanic replacement reaction. When exploited as an anode material for LIBs, these SMA-incorporated carbons showed an enhanced capacity compared to control samples that had no SMAs in the carbon network. Our investigation also revealed that the effect of SMAs on the LIB performance depends on the types of metal incorporated, among which Ag SMAs stand out in terms of rate capability and long-term stability. In particular, the Ag SMA-embedded carbon exhibited superior performance even at ultrafast charging/discharging rates, highlighting the unprecedented effects of SMAs as capacity boosters. The reasons for this newly found SMA effect are attributed to the lower interfacial resistances that are induced by delicate modulation of the electronic structure of carbon in the presence of Ag SMAs. This is the first report to harness SMAs for LIBs, which we speculate will open a new frontier in the applications of SMAs.

Lithium-ion battery state of charge prediction based on machine learning approach

With the extensive utilization of lithium ion batteries as renewable energy source in electronics devices, smart network and electric vehicles, supplementary enhancements in the performance of lithium-ion batteries and accurate prediction of state of charge (SOC) are still a great challenge to battery research and innovation community. Machine learning (ML), which is one of the essential tools of artificial intelligence, is promptly changing many areas with its capability to learn from provided data and solve multifaceted tasks, and it has emerged as a new method used to solve research issues in the area of lithium ion batteries. In this paper, we investigate the relationship between input factors including current, voltage and temperature, and predicted SOC of lithium ion battery. The effectiveness of three ML models — linear regression, Gaussian process regression (GPR) and support vector machine (SVM) were assessed and compared. It was found that the predictions made by these models accurately matched the data from experiments.

Applications of Graphene Materials in Lithium-ion Batteries

In recent years, the escalating global energy crisis and the ever-increasing concern over environmental pollution have stimulated extensive research efforts towards exploring new green energy sources and accelerating the transformation and upgrading of traditional energy structures. Lithium-ion batteries (LIBs), known for their exceptional properties including high energy density, excellent discharge performance, long lifespan, non-toxicity, and environmental friendliness, have been regarded as the backbone of new energy systems. Additionally, graphene, a two-dimensional novel nanomaterial, has recently gained attention as a kind of electrode material exhibiting great promise for utilization in LIBs due to exceptional performance attributes. Notably, significant evidence from current studies has revealed that the integration of graphene into LIBs leads to a substantial improvement in their electrochemical performance. However, advancements in the performance during cycling and the capacity for charging of LIBs remain imperative. Therefore, the investigation of materials based on graphene for enhancing the performance of LIBs holds substantial importance. This paper has various aspects related to the utilization of graphene and graphene-based composites materials in electrodes in LIBs. A comprehensive analysis is conducted on the current literature and recent progress in the respective field, including the modification of composites formed by integrating metal oxides with graphene, the usage of graphene as a conductive agent, etc. Furthermore, this paper provides an outlook on the future enhancements and developments concerning the utilization of graphene in LIBs. The findings and analyses presented in this study hope to contribute novel insights for researchers and encourage the development of LIBs with larger capacity and longerlasting operational capabilities.

Facile Preparation of High Performance Low Concentration HCHO Degradation Catalyst from Waste Li-MnO2 Batteries

The recycling and utilization of lithium-ion batteries has received a lot of attention. The use of recycled waste lithium-manganese batteries to degrade formaldehyde contaminated gas by adsorption is certainly killing two birds with one stone. In this paper, efficient catalysts capable of degrading formaldehyde were obtained using lithium-manganese button batteries being discharged to different levels and then recovering the cathode material by a simple method and labelled as LixMnO2 (x = 0.00; 0.25; 0.50; 0.75; 1.00). The fully discharged cathode material Li1.00-MnO2 degraded formaldehyde at nearly 100% (less than 0.1 ppm) within 24 h at room temperature, which is twice the degradation rate of the undischarged cathode material. The high degradation efficiency is attributed to the continuous doping of Li+ as the discharge proceeds and the conversion of Mn(IV) to Mn(III), so the lattice gap, defects, surface oxygen species and specific surface area of the catalyst increase. And the surface oxygen involved in the degradation of formaldehyde increases. The catalytic activity of the catalyst for formaldehyde gradually increased with the discharge, promoting the catalytic degradation effect. The degradation rate of formaldehyde at low concentrations was close to 100% within 24 h. This study provides an attractive approach for converting lithium battery electrode materials into formaldehyde degradation catalysts to improve the indoor environment.

A practical method of developing cooling control strategy for thermal runaway propagation prevention in lithium ion battery modules

The thermal runway (TR) has become one of the most concerned issues during the utilization of lithium ion batteries (LIBs), and the hazard behaviors can be different under various abusive conditions. However, existing mitigation methods are developed under only specific conditions, and the universality is doubtable. In this study, a novel research idea has been proposed, where the underlying characteristics of the TR propagation have been identified under various abusive conditions. And further discussion on the mitigation strategy has also been conducted. The results show that even though the temperature curve has been distorted during TR propagation, the critical temperature data, namely the TR onset temperature and the maximum temperature, still exhibit acceptable deviations. Accordingly, the maximum heat transfer rate and heat accumulation can be determined, which are identified as the underlying parameters to characterize the TR propagation. Based on the identified underlying parameters, key factors relating to the hazard mitigation system, for instance activation/termination time, cooling power, and total heat absorption, have been demonstrated. Finally, a practical method of developing the cooling control strategy is proposed. Based on the proposed research idea, it is believed to improve the applicability of the developed mitigation method.

Full Concentration Gradient-Tailored Li-Rich Layered Oxides for High-Energy Lithium-Ion Batteries.

Lithium-rich layered oxides (LLOs) are prospective cathode materials for next-generation lithium-ion batteries (LIBs), but severe voltage decay and energy attenuation with cycling still hinder their practical applications. Herein, a series of full concentration gradient-tailored agglomerated-sphere LLOs are designed with linearly decreasing Mn and linearly increasing Ni and Co from the particle center to the surface. The gradient-tailored LLOs exhibit noticeably reduced voltage decay, enhanced rate performance, improved cycle stability, and thermal stability. Without any material modifications or electrolyte optimizations, the gradient-tailored LLO with medium-slope shows the best electrochemical performance, with a very low average voltage decay of 0.8mV per cycle as well as a capacity retention of 88.4% within 200 cycles at 200mA g-1 . These excellent findings are due to spinel structure suppression, electrochemical stress optimization, and Jahn-Teller effect inhibition. Further investigation shows that the gradient-tailored LLO reduces the thermal release percentage by as much as about 41% when the battery is charged to 4.4V. This study provides an effective method to suppress the voltage decay of LLOs for further practical utilization in LIBs and also puts forward a bulk-structure design strategy to prepare better electrode materials for different rechargeable batteries.

Thermal Runaway Triggered by Plated Lithium on the Anode after Fast Charging.

Battery safety, at the foundation of fast charging, is critical to the application of lithium-ion batteries, especially for high energy density cells applied in electric vehicles. In this paper, an earlier thermal runaway of cells after fast charging application is illustrated. Under this condition, the reaction between the plated lithium and electrolyte is revealed to be the mechanism of thermal runaway triggering. The mechanism is proved by the accelerated rate calorimetry tests for partial cells, which determine the triggering reactions of thermal runaway in the anode-electrolyte thermodynamic system. The reactants in this system are analyzed by nuclear magnetic resonance and differential scanning calorimetry, proving that the vigorous exothermic reaction is induced by the interaction between the plated lithium and electrolyte. As a result, the finding of thermal runaway triggered by the plated lithium on anode surface of cells after fast charging promotes the understanding of thermal runaway mechanisms, which warns of the danger of plated lithium in the utilization of lithium-ion batteries.

Online Fault Diagnosis of External Short Circuit for Lithium-Ion Battery Pack

Battery safety is one of the most crucial issues in the utilization of lithium-ion batteries (LiBs) for all-climate electric vehicles. Short circuit, overcharge, and overheat are three common field failures of LiBs. In this paper, online fault diagnosis for external short circuit (ESC) of LiB packs is investigated. The experiments are carried out to obtain and compare ESC characteristics of 18650-type NMC battery pack and single cell. Based on the analysis of experimental results, a two-step equivalent circuit model is established to describe the ESC process and an online model-based scheme is proposed to diagnose ESC faults of battery packs. The proposed scheme is evaluated by experimental data. The results show that it can effectively diagnose ESC faults in 3.5 s after their occurrences with the terminal voltage error less than 25 mV. The proposed scheme has shown great generalization ability. ESC faults of battery packs under different number of cells connected in series and unavailable current information can also be diagnosed at the terminal voltage error less than 48 and 60 mV, respectively.

Atomic Layer Deposition of Alumina Coatings onto SnS2 for Lithium-Ion Battery Applications

Tin disulfide (SnS2) hierarchical structures have been synthesized via a simple hydrothermal route for their utilization in lithium-ion batteries. The issue of fast capacity degradation over cycling because of the dramatic volume change that causes particle pulverization and poor contact between the active material and current collector is explored and alleviated for the first time by surface modification of the whole SnS2 electrode via a facile atomic layer deposition (ALD). ALD allows the uniform formation of conformal Al2O3 coatings onto the porous electrode, and thereby succeeds to enhance their electrochemical performance. As compared, within fifty battery cycles the bare SnS2 electrode exhibits a final capacity of 219.2mAhg−1 at 100mAg−1, while the coated electrode can deliver the final capacity of 351.1mAhg−1 (60% larger). In addition, the capacity retention and rate capability of the coated electrode is found much better than that of the bare one. Electrochemical impedance spectroscopy analyses indicate that the resistance derived from solid-electrolyte interface (SEI) and charge transfer process remains fairly smaller in the coated electrode than in the bare one. The better electrode kinetics accounts for the strengthened Li-ion intercalation behaviors of the modified electrodes, and demonstrates that ALD oxide coatings are able to enhance the mechanical integrity and structural stability of SnS2 electrodes, thus effectively impeding the pulverization of active particles and maintaining good electronic conduction paths and efficient charge transfer. Moreover, ALD coatings cover the entire electrode and reduce electrolyte decomposition to attenuate the SEI layer, which also contributes to the cyclability of SnS2.

Vanadium Based Nanoelectrode Materials in Energy Storage Systems

The performance and reliability of energy storage systems largely depend on the characteristics and properties of the electrode materials that constitute the system. Nanostructured materials have greatly influenced the overall performance of these systems by enhancing surface processes and improving the transport kinetics of the ions/molecules involved. Vanadium based materials have received considerable attention, particularly as electrode materials in high energy density Lithium ion batteries (LIB) and supercapacitors because of their cost effectiveness, efficient energy utilization and special structural characteristics. In this review, the applications of vanadium based materials as electrode components in energy storage systems have been described; their utilization in LIB and supercapacitors has been particularly highlighted. Recent developments in the field of vanadium based nanoelectrode materials are also discussed. Keywords: Vanadium, Energy storage, Electrodes, Lithium ion Batteries, Supercapacitors.