Li-ion Batteries Research Articles

A diffuse interface model for electro-chemo-mechanical systems

Electro-chemo-mechanics plays a critical role in the performance and longevity of energy storage systems, such as lithium-ion batteries and hydrogen energy storage. These systems involve multi-material composites comprising both liquid and solid phases, with their behavior influenced by processes in the bulk phases and at their interfaces.Traditional multi-phase theoretical and numerical models often adopt a discrete representation of material interfaces, introducing discontinuities in the problem’s fields. The numerical implementation is carried out using special interface elements, a methodology that requires conformal meshes and is not always supported by open-source computing platforms.This paper introduces a novel modeling framework that employs a diffuse representation of material interfaces, inspired by the phase-field method. From a modeling perspective, this approach allows for the consistent coupling of bulk and interface electro-chemo-mechanical processes, adhering to thermodynamic principles. Numerically, the proposed model is particularly suited for simulating real material microstructures using regular meshes, facilitating advanced numerical implementations.The methodology is detailed for a generic multi-material electro-chemo-mechanical system and applied specifically to Li-ion batteries. Numerical examples demonstrate the model’s effectiveness in simulating coupled interface processes without resorting to interface elements. This work provides a significant advancement in the simulation of electro-chemo-mechanical systems, offering a robust tool for studying the complex interplay of bulk and interface processes.

Influence of 3D Structural Design on the Electrochemical Performance of Aluminum Metal as Negative Electrode for Li-Ion Batteries.

Aluminum (Al) is one of the most promising active materials for producing next-generation negative electrodes for lithium (Li)-ion batteries. It features low density, high specific capacity, and low working potential, making it ideal for producing energy-dense cells. However, this material loses its electrochemical activity within 100 cycles, making it practically unusable. Several claims in the literature support the idea that a dual degradation mechanism is at play. First, the slow diffusion of Li in the Al matrix causes the electrochemical reactions to be partly irreversible, making the initial capacity of the cell drop. Second, the stress caused by cycling make the active material pulverize and lose activity. Recent work shows that shortening the diffusion path of Li by 3D structuring is an effective way to mitigate the first capacity loss mechanism, while alloying Al with other elements effectively mitigates the second one. In this work, we demonstrate that the benefits of 3D structuring and alloying are cumulative and that a mesh made of an Al-magnesium alloy performs better than both a pure Al foil and a foil of an Al-Mg alloy.

Aspects of Spodumene Lithium Extraction Techniques

Lithium (Li), a leading cathode material in rechargeable Li-ion batteries, is vital to modern energy storage technology, establishing it as one of the most impactful and strategical elements. Given the surge in the electric car market, it is crucial to improve lithium recovery from its rich mineral deposits using the most effective extraction technique. In recent years, both industry and academia have shown significant interest in Li recovery from various Li-bearing minerals. Of these, only extraction from spodumene has established a reliable industrial production of Li salts. The current approaches for cracking of the naturally occurring, stable α-spodumene structure into a more open structure—β-spodumene—involve the so-called decrepitation process that takes place at extreme temperatures of ~1100 °C. This conversion is necessary, as β-spodumene is more susceptible to chemical attacks facilitating Li extraction. In the last several decades, many techniques have been demonstrated and patented to process hard-rock mineral spodumene. The objective of this review is to present a thorough analysis of significant findings and the enhancement of process flowsheets over time that can be useful for both research endeavors and industrial process improvements. The review focuses on the following techniques: acid methods, alkali methods, carbonate roasting/autoclaving methods, sulfuric acid roasting/autoclaving methods, chlorinating methods, and mechanochemical activation. Recently, microwaves (MWs), as an energy source, have been employed to transform α-spodumene into β-spodumene. Considering its energy-efficient and short-duration aspects, the review discusses the interaction mechanism of MWs with solids, MW-assisted decrepitation, and Li extraction efficiencies. Finally, the merits and/or disadvantages, challenges, and prospects of the processes are summarized.

Modeling of Li-ion Battery Management System for Unmanned Aerial Vehicles

Nowadays, systems that use more electricity in aircraft are increasing due to harmful gas emissions. This increase has created the need to store electrical energy and accelerated the trend towards battery technologies. Since energy storage in batteries occurs as a result of chemical reactions, problems may occur that will damage the battery group or the entire system. These problems are caused by high current, voltage, and temperature, which affect the reaction rate during battery charging/discharging. A Battery Management System (BMS) is needed to prevent problems and to use the required electrical energy safely. In this study, it is aimed to meet the energy needs of the system in a controlled manner by disabling only the damaged cell in case of problems that may occur in the cells in the battery. For this purpose, a model of an Unmanned Aerial Vehicle (UAV) system been created by adding a BMS block to each cell to control the battery cells. The BMS model is realized based on the cell temperature, the State of Charge (SoC) value of the cell, and the output voltage values of the cell. The UAV system is modeled using MATLAB/Simulink environment. Thanks to the proposed BMS, in case of a problem that may occur in any cell in the battery, that cell will be disabled and the required energy will be met through the remaining cells. It is observed from the results obtained that when the cell parameters become normal, it continues to feed the system again.

Uncertainty Quantification In The Prediction of Remaining Useful Life Considering Multiple Failure Modes

Abstract Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective was from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

Supramolecular-driven construction of multilayered structure by modified hummers method for robust silicon anode

Rational design of nano-structured silicon (Si)-based composites to enhance the structural stability and electrical conductivity is key to developing high-performance lithium-ion batteries. Here, a supramolecular self-assembly strategy is first adopted to prepare Si@rGO composites with multilayered structure through the modified Hummers method. Experimental results demonstrate that Si@SiOx NPs are uniformly distributed in rGO interlayers by supramolecular interaction. In situ Raman results suggest that the multilayer embedded structure can effectively confine the Si NPs and preserve the integrity of the electrode. Theoretical calculations indicate that the imbalanced charge distribution in inner interface shows enhanced dual-interfacial bonding and charge interactions in the Si@SiOx⊂rGO composite, resulting in an interfacial electric-field between Si NPs and rGO for fast ionic and charge migration. The fabricated MSi@SiOx⊂rGO anode with high Si contents (> 60 wt.% Si) exhibits superior Li-ion reaction kinetics (882.4 mAh g−1 at 8 A g−1), durable structural stability (0.031% per cycle capacity attenuation with an average Coulombic efficiency > 99.7%), and low expansion rate. High-mass-loading electrodes show great potential for commercial applications. Full cells assembled with LiCoO2 cathodes also demonstrate outstanding Li-ion storage properties. This work provides insights into the interfacial design of Si@C anodes for advanced Li-ion battery.

Removal and recovery of phosphorus and fluorine in process water from water based direct physical lithium-ion battery recycling

In the present work, the recovery of phosphorus and fluorine from process water generated in a water based direct physical recycling process of Li-ion batteries has been studied. The recycling process considered in this work produces significant amounts of process water, which is generated during the opening of the batteries by means of electro-hydraulic fragmentation and the subsequent sorting of the components in aqueous solution. This process produces between 21.6 L and 30.3 L of process water per kg of batteries with a total phosphorus and a total fluorine concentration of 60–85 mg/L and 120–470 mg/L, respectively. Currently, the process water has to be disposed of as hazardous waste. The goal is to discharge the wastewater into the sewer system. For this the total phosphorus and total fluorine concentration must be reduced. The process water is mainly contaminated by the released electrolyte consisting of organic carbonates and conducting salts. 31-P and 19-F NMR shows conclusively that no hydrolysis takes place in this process water. The phosphorus is present exclusively in the form of the complex anion PF6- and fluorine as F-, namely as FSI- from the conducting salt LiFSI and PF6- from the conducting salt LiPF6. In order to meet the regulatory requirements for discharge into the sewage system, 70.4% of the phosphorus and 89.3% of the fluorine must be removed. The conducting salts are hydrolyzed by adding acid and thereby phosphate and fluoride are precipitated. After critical and valuable materials are recovered the process water can be discharged into the sewer system.

Molecular modeling and simulation of organic electrolyte solutions for lithium ion batteries.

Multi-criteria optimization is used for developing molecular models for ethylene carbonate (EC) and propylene carbonate (PC), organic solvents commonly used in Li-ion batteries. The molecular geometry and partial charges of the solvents are obtained from quantum mechanical calculations. Using a novel optimization strategy that combines systematic variations of the Lennard-Jones parameters with a reduced units approach, the models are fitted to experimental data on the liquid density, vapor pressure, relative permittivity, and self-diffusion coefficient. Since no experimental data for the self-diffusion coefficient of pure EC were available in the literature, they are measured in this work using a gradient-based nuclear magnetic resonance technique. For all pure component properties, excellent agreement between experiment and simulation is obtained. Moreover, the predictive capabilities of the new solvent models are assessed by comparison to experimental data for the liquid density and relative permittivity of mixtures of EC and PC. In addition, molecular models for the anions PF6-, BF4-, and ClO4- in solutions of their lithium electrolytes in PC are developed using experimental data on the solution densities. Finally, the self-diffusion coefficients of LiPF6 in PC and in aqueous solution are predicted and compared, showing that diffusion is much slower in the organic solution due to the formation of larger solvent shells around the ions. Furthermore, an analysis of the radial distribution functions in these solutions suggests that the ions have much less impact on the structure of the solvent PC than on water.

DFT investigation on effectiveness of Beryllium-doped and defective graphene for anode material in lithium-ion batteries

Lithium-ion batteries employing graphite as the anode material are now widely used in powering electronic gadgets like, mobile phones, laptops, electronic watches and calculators and also to provide required power to run engine of electric vehicles. However, storage capacity, open circuit voltage, energy density and battery life are the major considerations on which researchers are trying different alternatives to achieve better performance of the battery. In this research, Beryllium-doped and defective graphenes have been investigated by employing the ab initio DFT method. It has been found that graphene with a defect and Be–doped graphene with defects can serve as an efficient materials for anode in Li-ion batteries.

Dry carbothermal reaction-enabled ultra-dense nanoparticle coatings for high-performance Li-S batteries

Electrocatalysts are crucial for facilitating the sluggish conversion between S and sulfide in Li-S batteries. Despite recent exploration of advanced materials, there remains a significant gap in the morphology design rationale oriented towards practical operation conditions. In this study, we introduce a dry process based on the carbothermal reaction of oxides to achieve nanoparticle (NP) electrocatalyst coatings. This process enables us to produce ultra-high-density NPs with several tens of particles per 100 nm x 100 nm area. We unveil that this high coating density boosts the conversion kinetics by dominating a solution-phase disproportionation reaction, thereby promoting the growth of non-passivating particulate sulfide. With this NP-coated cathode, we achieve excellent kinetic performance, maintaining 53 % of cell capacity retention even with a 50-fold increase in C-rate, and exhibiting only a 0.04 % capacity decay per cycle over 300 cycles at 10C. Remarkably, our cathode achieves an initial areal capacity of 7.51 mAh cm−2 in a full cell, significantly outperforming cells reported in previous studies as well as conventional Li-ion batteries.

Comparative environmental and economic assessment of emerging hydrometallurgical recycling technologies for Li-ion battery cathodes

The growing demand for electric vehicles has led to a growing concern for battery recycling, particularly for critical raw materials. However, there is insufficient investigation into the environmental and economic impacts of hydrometallurgical recycling methods. In this study we explored emerging hydrometallurgical technologies in economic and environmental perspective to establish conceptual routes to recover Co, Ni and Mn oxides from waste LiNi0.33Mn0.33Co0.33O2 cathode materials from spent Li-ion batteries. After, life cycle assessment and costing techniques were utilized to compare the environmental and economical performances of each conceptual route. Recovery efficiency of metal oxides through each route was also considered as a key factor. Results suggested that deep eutectic solvent-based leaching produces the highest impact under many impact categories while electrolysis-based leaching showed the least. Under purification technologies assessed, ion-exchange based purification showed significantly lower impact under many categories except stratospheric ozone depletion. Solvent based purification has been identified as the worst technology for purification. Hydroxide based calcination has been identified as the most environmentally sustainable calcination method compared to oxalate calcination. The route consists with inorganic leaching, ion-exchange based purification and hydroxide calcination showed the lowest environmental impact (emission effect at 33.8 kg CO2 eq), with lower economic impact ($ 119) and the highest recovery efficiency (78 %) per 1 kg of cathode active materials. However, using electrolysis-based leaching can slightly increase the impacts with lower recovery efficiency (75 %) and better economic performance ($104/kg of cathode active materials). Terrestrial ecotoxicity was identified to be the most affected impact category for the recovery processes. It is recommended that technologies like deep eutectic solvent-based leaching, solvent extraction and environmentally sustainable technologies like supercritical fluid extraction need further studies prior to industrial applications.

Thermal runaway prevention through scalable fabrication of safety reinforced layer in practical Li-ion batteries

Integrating safety features to cut off excessive current during accidental internal short circuits in Li-ion batteries (LIBs) can reduce the risk of thermal runaway. However, making this concept practical requires overcoming challenges in both material development and scalable manufacturing. Here, we demonstrate the roll-to-roll production of a safety reinforced layer (SRL) on current collectors at a rate of 5 km per day. The SRL, made of molecularly engineered polythiophene (PTh) and carbon additives, interrupts current flow during voltage drops or overheating without adversely affecting battery performance. Impact testing on 3.4-Ah pouch cells shows that the SRL reduces battery explosions from 63% to 10%. This work underscores the potential of integrating material science with manufacturing technology to enhance battery safety.

Li4Ti5O12-TiO2 composite anode for high performance full-cell Li-ion battery and Li-ion capacitor applications

Lithium titanate (Li4Ti5O12-LTO) having high chemical stability and zero strain property has attracted significant research interest as negative electrode material for Li-ion storage applications. However, its poor conductivity mainly owing to the presence of vacant Ti-3d states results in poor electrochemical performance especially at high rates. This study investigates the incorporation of titania (TiO2) with LTO in the form of LTO-TiO2 composite nanoparticles in order to overcome the limitations of LTO. The nanoparticle composite electrode exhibits 150 mAh/g specific capacity at a specific current of 2000 mA/g with superior cycling stability for 5000 cycles and about 77% capacity retention. This high capacity, ultra-long cyclability features could be attributed to the synergistic combination of LTO and TiO2 and the percolated conductive pathway that facilitate both electron and lithium transport. Its practical use is further demonstrated by fabricating Li-ion storage devices using LTO-TiO2 composite as negative electrode against LiMn2O4 (for battery) and activated carbon (for capacitor) as positive electrodes. Li-ion battery delivered excellent capacity (126 mAh/g) and cycling stability (1000 cycles) at high specific current of 1770 mA/g while the Li-ion capacitor delivered capacitance over 200 F/g and ultra-long cycling stability for 10,000 cycles at specific current of 10,000 mA/g.

Numerical simulation for comparison of cold plate cooling and HFE-7000 immersion cooling in lithium-ion battery thermal management

In this work, fluorinated liquids can be used as a new immersion coolant for effective battery thermal management (BTM) because of their insulating and non-flammable inert characteristics. In this research, the liquid chosen for immersion cooling is HFE-7000, characterized by a boiling point of 34 °C. The analysis of bubble kinetics is employed to investigate the heat transfer mechanism in two-phase immersion cooling by examining the behavior of bubbles during the boiling process. The square ternary Li-ion batteries are subjected under loads of 2C and 3C cooled by immersion cooling and a mini-channel cold plate as a comparison. It turns out that immersion liquid cooling modules offer superior thermal performance. Under constant current (CC), the peak temperature is reduced by 4.89 °C and 9.31 °C compared with the mini-channel cold plate cooling (CPC). Under dynamic load conditions, the maximum temperature rise of the battery is reduced by 29.89 % and 43.89 % compared to CPC. As a passive heat dissipation method, two-phase immersion cooling reduces the design requirements for active control. Also, it delivers an efficient battery thermal management system (BTMS) designed for upcoming electric vehicles (EVs).

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Abstract The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery’s performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80 % nickel, 10 % manganese, and 10 % cobalt, and a lithium metal foil anode - a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4 % over the nominal charge rate, demonstrating the potential of the developed approach.

Bio-inspired 3D-Printed supercapacitors for sustainable energy storage

This study presents a significant enhancement of the electrochemical properties of commercially available high-surface-area bare activated carbon (BAC) through the grafting of catechin hydrate (CH) redox active molecules. The grafting of CH onto the BAC was confirmed by the results from the Thermogravimetric analysis, X-ray photoelectron spectroscopy and Brunauer-Emmett-Teller. Moreover, this study introduces 3D printed deep eutectic solvent electrolytes, composed of choline chloride and urea, highlighting the potential of sustainable and greener materials in energy storage. A 3D-printed fully bio-inspired supercapacitor achieved a maximum specific capacitance of 75 F g−1 at a scan rate of 1 mV s−1 (37 F g−1 at a current density of 0.2 A g−1), demonstrating performance comparable to that of previously reported state-of-the-art fully 3D-printed and disposable paper supercapacitors (25.6 F g−1 at a scan rate of 1 mV s−1 for 1.2 V). Furthermore, the device exhibited electrochemical performance within an extended potential window of 2.0 V, coupled with cycling stability of approximately 90.2 % after 10,000 cycles, offering a specific energy of 20.6 W h kg−1 at a specific power of 560 W kg−1. The developed bio-inspired, eco-friendly, sustainable, 3D-printed supercapacitors can replace the harmful Li-ion batteries currently used in low-power wireless sensor applications.

Advancements in Lithium-Ion Battery Technology

Lithium-ion (Li-ion) batteries are at the forefront of modern energy storage technologies due to their high energy density, long cycle life, and relatively low self-discharge rate. Recent advancements in materials science, battery management systems, and fabrication techniques have significantly improved the performance, safety, and sustainability of Li-ion batteries. This paper explores the latest developments in lithium-ion technology, including high-capacity anode and cathode materials, solid-state electrolytes, and next-generation designs, while also addressing the challenges and future directions for research and application.

Upcycling of Spent LiFePO4 Cathodes to Heterostructured Electrocatalysts for Stable Direct Seawater Splitting

AbstractThe pursuit of carbon‐neutral energy has intensified the interest in green hydrogen production from direct seawater electrolysis, given the scarcity of freshwater resources. While Ni‐based catalysts are known for their robust activity in alkaline water oxidation, their catalytic sites are prone to rapid degradation in the chlorine‐rich environments of seawater, leading to limited operation time. Herein, we report a Ni(OH)2 catalyst interfaced with laser‐ablated LiFePO4 (Ni(OH)2/L‐LFP), derived from spent Li‐ion batteries (LIBs), as an effective and stable electrocatalyst for direct seawater oxidation. Our comprehensive analyses reveal that the PO43− species, formed around L‐LFP, effectively repels Cl− ions during seawater oxidation, mitigating corrosion. Simultaneously, the interface between in situ generated NiOOH and Fe3(PO4)2 enhances OH− adsorption and electron transfer during the oxygen evolution reaction. This synergistic effect leads to a low overpotential of 237 mV to attain a current density of 10 mA cm−2 and remarkable durability, with only a 3.3 % activity loss after 600 h at 100 mA cm−2 in alkaline seawater. Our findings present a viable strategy for repurposing spent LIBs into high‐performance catalysts for sustainable seawater electrolysis, contributing to the advancement of green hydrogen production technologies.

Synergistically Boosting Li Storage Performance of MnWO4 Nanorods Anode via Carbon Coating and Additives

Polyanionic structures, (MO4)n−, can be beneficial to the transport of lithium ions by virtue of the open three-dimensional frame structure. However, an unstable interface suppresses the life of the (MO4)n−-based anode. In this study, MnWO4@C nanorods with dense nanocavities have been synthesized through a hydrothermal route, followed by a chemical deposition method. As a result, the MnWO4@C anode exhibits better cycle and rate performance than MnWO4 as a Li-ion battery; the capacity is maintained at 718 mAh g−1 at 1000 mA g−1 after 400 cycles because the transport of lithium ions and the contribution of pseudo-capacitance are increased. Generally, benefiting from the carbon shell and electrolyte additive (e.g., FEC), the cycle performance of the MnWO4@C electrode is also effectively improved for lithium storage.