Battery Operation Research Articles

Topological Design of Highly Conductive Weakly Solvating Electrolytes for Ultrastable Sodium Metal Batteries Operating at -60 °C and Below.

Weakly solvating electrolytes (WSE) can favor reversible Na batteries at -40 °C for some extreme applications because of the low desolvation energy. However, it is challenging to enable reversible Na batteries at lower temperatures. Herein, we uncover that the low ionic conductivity of WSE reduces reaction kinetics at -60 °C. Accordingly, a highly conductive weakly solvating electrolyte (HCWSE) is designed by introducing additives of strongly solvating solvents and the dilution of NaPF6. The additive can dominate the solvation sheath, increase the dissociation of NaPF6 and the fluidity of the electrolyte, and thus greatly improve the ionic conductivity. Furthermore, the binding energy between Na+ and solvents is proposed as a descriptor to determine the solvating power of solvents, based on which a series of ultralow-temperature HCWSEs have been topologically designed by facilely introducing strong-solvation ether additives into the weak-solvation solvents. As a demonstration, the HCWSE showcases the long cycling of Na||Na cell at -60 °C with an overpotential of 42 mV under 1 mA cm-2 for 1200 h. The Na||NNFM (Na0.75Ni0.25Fe0.25Mn0.5O2) cell exhibits a reversible capacity of 79.2 mAh g-1 after 160 cycles. The cells also achieve impressive performances at -70 °C.

Comparative Analysis of BTM Systems Made of a Fireproof Composite Material with Nano Boron Nitride

The paper presents a numerical analysis of the possibilities of replacing the aluminum serpentines in the current construction of battery thermal management systems (BTMS) with cooling serpentines made of fireproof composite materials with high heat transfer parameters (fireproof epoxy resin + nano boron nitride). This approach was given by the need to replace aluminum (which, in case of fire, maintains and accelerates the combustion process) with fireproof materials that reduce/eliminate the fire risk due to improper battery operation. Numerical analysis methods were used through simulation to identify the most efficient design among the single-channel, multichannel, multiflow and multiple coolant inlet–outlet solutions for cooling serpentine. In addition to these geometric constructive parameters, the variation of the coolant flow rate (9, 12, 15 and 18 L/min) and coolant inlet temperature (17, 20 and 25 °C) was also considered. The obtained results showed that the single-inlet nanocomposite resin cooling serpentine four-channel configuration presents the highest cooling efficiency of the cells that form the battery module while ensuring very good thermal uniformity as well. These findings are supported by the lowest average heat absorption by the batteries, of 34.44 kJ, as well as the lowest average internal resistance difference (caused by thermal gradients), of 5.23%. Future research is needed to identify the degree of structural resistance of serpentines made of fireproof composite material to external stresses (vibrations characteristic of the operation of electric vehicles).

In Situ TEM Characterization of Battery Materials.

Transmission electron microscopy (TEM) is an indispensable analytical technique in materials research as it probes material information down to the atomic level and can be utilized to examine dynamic phenomena during material transformations. In situ TEM resolves transient metastable states via direct observation of material dynamics under external stimuli. With innovative sample designs developed over the past decades, advanced in situ TEM has enabled emulation of battery operation conditions to unveil nanoscale changes within electrodes, at interfaces, and in electrolytes, rendering it a unique tool to offer unequivocal insights of battery materials that are beam-sensitive, air-sensitive, or that contain light elements, etc. In this review, we first briefly outline the history of advanced electron microscopy along with battery research, followed by an introduction to various in situ TEM sample cell configurations. We provide a comprehensive review on in situ TEM studies of battery materials for lithium batteries and beyond (e.g., sodium batteries and other battery chemistries) via open-cell and closed-cell in situ TEM approaches. At the end, we raise several unresolved points regarding sample preparation protocol, imaging conditions, etc., for in situ TEM experiments. We also provide an outlook on the next-stage development of in situ TEM for battery material study, aiming to foster closer collaboration between in situ TEM and battery research communities for mutual progress.

Battery Sentiment Analysis and Recommention System

- The increasing reliance on battery technology across industries such as renewable energy, electric vehicles, and portable electronics underscores the importance of robust analysis and optimization of battery systems. Battery-centric analysis focuses on understanding key parameters such as temperature, current, voltage, state of charge (SOC), and state of health (SOH) to enhance performance, safety, and longevity. These parameters are critical in identifying potential failures, improving efficiency, and predicting battery life cycles. This study employs advanced methodologies, including machine learning algorithms such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN), to analyse battery performance data. By training and testing these models on extensive datasets, the research provides accurate predictions of battery behavior under diverse operating conditions. Key metrics, including accuracy, root mean square error (RMSE), and mean absolute error (MAE), are used to evaluate the effectiveness of these algorithms, ensuring reliable insights into battery health and operation. The integration of real-time monitoring systems with predictive analytics enhances the safety and efficiency of batteries, reducing risks such as overheating, overcharging, and premature failure. Additionally, this approach supports the development of sustainable energy solutions by identifying areas for material optimization and improving the design of energy storage systems. Key Words: Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN).

Lithium Deposition Mechanism under Different Thermal Conditions Unraveled via an Optimized Phase Field Model.

As one of the most important physical fields for battery operation, the regulatory effect of temperature on the growth of lithium dendrites should be studied. In this paper, we develop an optimized phase field model to explore the effect of temperature on the growth of Li dendrites in Li metal batteries. We incorporated full lithium deposition kinetics, including atom diffusion and solid electrolyte interface restriction on interface kinetics, into the model and revealed their significance in determining the transformation of the lithium deposition morphology from moss-like to dendrite-like. We found that a high temperature or dispersed hot spots are more conducive to stable battery operation than a low temperature or concentrated hot spots due to the enhanced diffusion kinetics at the high temperature and the more uniform temperature distribution of dispersed hot spots. We believe our work can provide a useful tool for further exploring the thermal effect on stable lithium metal battery operation.

Dendrite formation in solid-state batteries arising from lithium plating and electrolyte reduction.

All-solid-state batteries offer high-energy-density and eco-friendly energy storage but face commercial hurdles due to dendrite formation, especially with lithium metal anodes. Here we report that dendrite formation in Li/Li7La3Zr2O12/Li batteries occurs via two distinct mechanisms, using non-invasive solid-state nuclear magnetic resonance and magnetic resonance imaging. Tracer-exchange nuclear magnetic resonance shows non-uniform Li plating at electrode-electrolyte interfaces and local Li+ reduction at Li7La3Zr2O12 grain boundaries. In situ magnetic resonance imaging reveals rapid dendrite formation via non-uniform Li plating, followed by sluggish bulk dendrite nucleation from Li+ reduction, with an intervening period of stalled growth. Formation of amorphous dendrites and subsequent crystallization, the defect chemistry of solid electrolytes and battery operating conditions play a critical role in shaping the complex interplay between the two mechanisms. Overall, this work deepens our understanding of dendrite formation in solid-state Li batteries and provides comprehensive insight that might be valuable for mitigating dendrite-related challenges.

Design and simulation of hybrid renewable energy system integration of micro grids using Simulink

This research explores the novel design and simulation of a hybrid renewable energy system integrating photovoltaic (PV) panels, wind turbines, and a diesel generator backup to address the energy demands of an independent hostel in Nottingham, United Kingdom. The system prioritizes minimizing long-term costs and emissions while maintaining energy reliability. A detailed load profile was developed to optimize battery performance, reduce reliance on diesel, and extend battery lifespan, thereby lowering operational costs. Voltage source control and load profile modeling were implemented and evaluated using MATLAB R2023b. Simulation outcomes demonstrated a significant reduction in the diesel generator's runtime, leading to lower emissions. Additionally, the integrated energy management system ensures safe battery operations, enhancing system efficiency, sustainability, and overall performance. This research underscores the potential of optimizing renewable energy integration within microgrids using Simulink.

Deep Reinforcement Learning Based Optimal Operation of Low-Carbon Island Microgrid with High Renewables and Hybrid Hydrogen–Energy Storage System

Hybrid hydrogen–energy storage systems play a significant role in the operation of islands microgrid with high renewable energy penetration: maintaining balance between the power supply and load demand. However, improper operation leads to undesirable costs and increases risks to voltage stability. Here, multi-time-scale scheduling is developed to reduce power costs and improve the operation performance of an island microgrid by integrating deep reinforcement learning with discrete wavelet transform to decompose and mitigate power fluctuations. Specifically, in the day-ahead stage, hydrogen production and the hydrogen blending ratio in gas turbines are optimized to minimize operational costs while satisfying the load demands of the island. In the first intraday stage, rolling adjustments are implemented to smooth renewable energy fluctuations and increase system stability by adjusting lithium battery and hydrogen production equipment operations. In the second intraday stage, real-time adjustments are applied to refine the first-stage plan and to compensate for real-time power imbalances. To verify the proposed multi-stage scheduling framework, real-world island data from Shanghai, China, are utilized in the case studies. The numerical simulation results demonstrate that the proposed innovative optimal operation strategy can simultaneously reduce both the costs and emissions of island microgrids.

Activated Graphite with Richly Oxygenated Surface from Spent Lithium-Ion Batteries for Microwave Absorption.

Designing spent graphite anodes from lithium-ion batteries (LIBs) for applications beyond regenerated batteries offers significant potential for promoting the recycling of spent LIBs. The battery-grade graphite, characterized by a highly graphitized structure, demonstrates excellent conductive loss capabilities, making it suitable for microwave absorption. During the Li-ion intercalation and deintercalation processes in battery operation, the surface layer of spent graphite (SG) becomes activated, forming oxygen-rich functional groups that enhance the polarization loss mechanism. To further control the polarization loss and achieve optimized impedance matching, reduced graphene oxide (rGO) is employed as a modifier. Herein, rGO serves as a binder, effectively combining individual SG particles. The matched Fermi levels of SG and rGO reduce the interfacial barrier, facilitating rapid electron transfer. Simultaneously, their combination forms a 3D conduction network, which not only enhances multiple scattering, reflection, and attenuation of electromagnetic waves but also provides abundant polarization centers for increased microwave absorption. As a result, the optimized SG/rGO aerogel achieves an impressive effective absorption bandwidth of 7.04GHz and accompanied by a minimum reflection loss of -51.1dB. This study broadens the scope of spent LIBs utilization and provides insights for wilder and more functional applications.

Solution-Processed MoS2-Expanded Graphite as a Fast-Charging Anode for Lithium-Ion Batteries.

The widespread demand for battery-powered technologies has propelled the search for efficient and commercially viable electrode materials with fast-charging abilities. Reported herein is an MoS2-expanded graphite (EG) composite as a stable and high-rate lithium-ion battery (LIB) anode, delivering specific capacities of 796 mAh g-1 at 0.5 A g-1 and 320 mAh g-1 at 20 A g-1 over 400 cycles. Cyclability at extreme rates up to 50 A g-1 (~103 mAh g-1) illustrates its scope for fast charging applications. As a result of the processing technique, the exfoliated nanosheets have good interfacial contact which aids in charge transfer and maintaining structural integrity during continuous battery operation. Further, analytical electrochemical methods suggest a predominance of pseudocapacitive charge storage mechanism, explaining the anode performance at high rates.

The Role of Long-Range Interactions Between High-Entropy Single-Atoms in Catalyzing Sulfur Conversion Reactions.

Sulfur conversion reactions are the foundation of lithium-sulfur batteries but usually possess sluggish kinetics during practical battery operation. Herein, a high-entropy single-atomcatalyst (HESAC) is synthesized for this process. In contrast to conventional dual-atom catalysts that form metal-metal bonds, the center metal atoms in HESAC are not bonded but exhibit long-range interactions at a sub-nanometer distance (<9 Å). The synergistic effect between the long-range interactions and entropy changes enables the regulation of d- and π-electron states. This alteration in the electronic structure improves the adsorption and electronic conductivity of intermediate polysulfides, thereby accelerating their conversion kinetics. Consequently, this leads to a significant enhancement in specific capacities by ≈40% at high rates compared to single-atom catalysts. The resulting lithium-sulfur battery with HESAC demonstrates a remarkable areal capacity of 3.4 mAh cm-2 at 10 C. These findings provide valuable insights into the design principle of metal atom catalysts for electrochemical reactions.

Controllable reconstruction of lignified biomass with molecular scissors to form carbon frameworks for highly stable Li metal batteries.

Lithium metal batteries (LMBs) promise high-energy-density storage but face safety issues due to dendrite-induced lithium deposition, irreversible electrolyte consumption, and large volume changes, which hinder their practical applications. To address these issues, tuning lithium deposition by structuring a host for the lithium metal anode has been recognized as an efficient method. Herein, we report a supercritical water molecular scissor-controlled strategy to form a carbon framework derived from biomass wood. Proximate-supercritical water treatment is used to selectively cleave the β-O-4 bonds in lignin, with the extent of degradation controlled by adjusting the treatment environment's acidity. The enhanced thermal power of supercritical water molecules significantly accelerates the etching rate of lignin, increasing the porosity and permeability of the transformed carbon framework. Experimental results and multi-physics simulations show that the interconnected carbon-based pores and inner skeletal multilevel hierarchical structure facilitate rapid electron and ion transfer during battery operation and enhance electrolyte infiltration. Impressively, the as-obtained lithium metal anode exhibits long-term cycling stability for over 2000 hours at 0.5 mA cm-2 with low voltage overpotential. The water-treated Pinus (WTP)-Li//LiCoO2 full cells maintain a high capacity retention rate of 93.3% and a specific capacity of 142 mA h g-1 at 0.5C for 100 cycles.

A triflate porous layer stabilizing Zn anodes for high-performance Zn-ion batteries.

We meticulously designed a triflate porous layer on Zn anodes to enhance the overall performance of Zn-ion batteries. During battery operation, it stabilizes the surface pH of Zn anodes and facilitates the formation of a ZnF2-containing interphase. Therefore, both the reversibility and reaction kinetics of Zn anodes are effectively promoted.

An image analysis-based method to determine the vanadium electrolyte contents during the capacity recovery process with a facile chemical oxidation strategy

Repairing and regenerating the unbalanced electrolytes is critical for the long-term operation of vanadium redox flow batteries (VRFBs). In this work, we propose a simple strategy to repair the unbalanced electrolytes for capacity recovery through chemical oxidation with the V(V) electrolyte and develop a method based on image analysis to obtain the electrolytes’ V(IV) ion contents. When the V(V) electrolyte with the same concentration as that of the unbalanced electrolyte is added to the unbalanced electrolyte, V(V) ions gradually oxidize V(III) ions to V(IV) ions, and the color of the electrolyte changes significantly. By extracting the RGB information of the electrolyte image, it is found that the B channel value presents the highest sensitivity to the V(IV) ion content. Thus, the relationship between them is fitted, and the R2 of the fitted curve reaches 0.9978. Furthermore, the image analysis-based method is applied to predict the V(V) electrolyte volumes needed to repair the unbalanced electrolytes. Results show that the deviation of the B channel values between the recovered and standard V(IV) electrolytes is within 5%, demonstrating the effectiveness of the proposed repair strategy and image analysis-based method, which show great promise for practical VRFB applications.

Dual functional coordination interactions enable fast polysulfide conversion and robust interphase for high-loading lithium-sulfur batteries.

The stable operation of high-capacity lithium-sulfur batteries (LSBs) has been hampered by slow conversion kinetics of lithium polysulfides (LiPSs) and instability of the lithium metal anodes. Herein, 6-(dibutylamino)-1,3,5-triazine-2,4-thiol (DTD) is introduced as a functional additive for accelerating the kinetics of cathodic conversion and modulating the anode interface. We proposed that a coordination interaction mechanism drives the polysulfide conversion and modulates the Li+ solvated structure during the binding of the N-active site of DTD to LiPSs and lithium salts. The results show that DTD effectively promotes the redox of LiPSs and the formation of an inorganic-organic synergistic solid electrolyte interface (SEI). This suppresses the parasitic reaction of LiPSs and confers uniform lithium deposition. Therefore, the capacity decay rate per cycle of the DTD-added LSBs is only 0.066% after 600 cycles at 1C. Moreover, Li-Li symmetric batteries exhibited smaller overpotentials during long cycling and a 41% increment in cycle life. Even with high sulfur loading (5.38 mg cm-2) and a depleted electrolyte sulfur ratio (E/S = 5 μL mg-1), the capacity retention of the battery is 71.5%. This work provides a new reference for elucidating the mechanisms of polysulfide conversion and SEI interface regulation for high-energy-density lithium-sulfur batteries.

Synergistically Inducing Ultrafast Ion Diffusion and Reversible Charge Transfer in Lithium Metal Batteries Using Bimetallic Molybdenum-Titanium MXenes.

Metal batteries have captured significant attention for high-energy applications, owing to their superior theoretical energy densities. However, their practical viability is impeded by severe dendrite formation and poor cycling stability. To alleviate these issues, a 3D-structured bimetallic-Mo2Ti2C3Tx based fiber electrode was fabricated in this study and analyzed experimentally and computationally. The bimetallic Mo-Ti composition of MXenes synergistically achieved low binding and formation energies with lithium. In particular, the minimal lattice mismatch between the deposited Li metal and the Mo2Ti2C3Tx MXene anode substrate led to improved Li formation energy with respect to the MXene surface. Moreover, the synergy of the bimetallic Mo-Ti composition of the Mo2Ti2C3Tx MXene fiber substrate helped to amplify ion diffusion and reversible charge transfer. Consequently, the bimetallic MXene electrode exhibited an impressive Coulombic efficiency (99.08%) even at a high current density (5 mA cm-2) and a fixed cutoff capacity of 1 mA h cm-2 with prolonged cycle life (650 cycles). This report highlights a promising advancement in addressing the critical challenges facing metal battery operation, thereby offering an approach to improving performance for high-energy applications.

Towards Rest Period based Li-Ion Battery Analysis: Estimating Solid Phase Diffusivity

Accurate estimation of solid phase diffusivity, a rate-limiting step in Li-Ion batteries, is extremely important for battery management system (BMS) algorithms for precise state estimations and safe, stable battery operation. The present state-of-the-art diffusivity estimation methods give erroneous results, due to inherent limitations and invalid assumptions. Here, a new method is proposed, diffusivity estimation from long-term voltage relaxation technique (DELVRT), to estimate solid diffusivity using battery rest period voltage relaxation. As solid phase diffusion dominates rest period voltage relaxation, the derivation of the spatio-temporal evolution of the concentration profile is derived and used to demonstrate accurate estimation of diffusivity. For times greater than the diffusion time scale, the temporal voltage variation is directly proportional to the diffusivity, leading to an analytical expression for estimating diffusivity. The validation for this model is performed for various different conditions, with data generated using the Pseudo-2D electrochemical-thermal model. It is demonstrated that the diffusivity estimation using DELVRT is significantly more accurate compared to the widely used galvanostatic intermittent titration technique (GITT), especially where GITT performed poorly owing to high non-linearity in the open circuit potential-state of charge relationship of the active material.

Novel Insights into Enhanced Stability of Li-Rich Layered and High-Voltage Olivine Phosphate Cathodes for Advanced Batteries through Surface Modification and Electron Structure Design.

The design of cathode/electrolyte interfaces in high-energy density Li-ion batteries is critical to protect the surface against undesirable oxygen release from the cathodes when batteries are charged to high voltage. However, the involvement of the engineered interface in the cationic and anionic redox reactions associated with (de-)lithiation is often ignored, mostly due to the difficulty to separate these processes from chemical/catalytic reactions at the cathode/electrolyte interface. Here, a new electron energy band diagrams concept is developed that includes the examination of the electrochemical- and ionization- potentials evolution upon batteries cycling. The approach enables to forecast the intrinsic stability of the cathodes and discriminate the reaction pathways associated with interfacial electronic charge-transfer mechanisms. Specifically, light is shed on the evolution of cationic and anionic redox in high-energy density lithium-rich 0.33Li2MnO3·0.67LiNi0.4Co0.2Mn0.4O2 (HE-NCM) cathodes, particularly those that undergo surface modification through SO2 and NH3 double-gas treatment to suppress the structural degradation. The chemical composition and energy distribution of the occupied and unoccupied electronic states at the different charging/discharging states are quantitatively estimated by using advanced spectroscopy techniques, including operando Raman spectroscopy. The concept is successfully demonstrated in designing artificial interfaces for high-voltage olivine structure cathodes enabling stable battery operation up to 5.1V versus Li+/Li.

Generalizable Solar Irradiance Prediction for Battery Operation Optimization in IoT-Based Microgrid Environments

The reliance on fossil fuels as a primary global energy source has significantly impacted the environment, contributing to pollution and climate change. A shift towards renewable energy sources, particularly solar power, is underway, though these sources face challenges due to their inherent intermittency. Battery energy storage systems (BESS) play a crucial role in mitigating this intermittency, ensuring a reliable power supply when solar generation is insufficient. The objective of this paper is to accurately predict the solar irradiance for battery operation optimization in microgrids. Using satellite data from weather sensors, we trained machine learning models to enhance solar irradiance predictions. We evaluated five popular machine learning algorithms and applied ensemble methods, achieving a substantial improvement in predictive accuracy. Our model outperforms previous works using the same dataset and has been validated to generalize across diverse geographical locations in Florida. This work demonstrates the potential of AI-assisted data-driven approaches to support sustainable energy management in solar-powered IoT-based microgrids.

Modulating ion–dipole interactions in nonflammable phosphonate-based electrolyte for safe and stable sodium-ion pouch cells

Abstract Phosphonate-based electrolyte with the merits of low cost and intrinsic nonflammability are promising candidates to realize the safe operation of sodium-ion batteries. However, they generally suffer from poor interfacial chemistry because of the solvent-dominated solvation structure induced by the strong ion–diploe interactions between cations and phosphonate molecules. Herein, we report an electrolyte design strategy that selectively improves the competitive coordination of low-solvating-power molecules, achieving stable interfacial chemistry with non-flammable, low-cost and fluorine-free electrolyte. By improving ion–ion interaction between cation and anion, weakly coordinated molecules can enter the Na+ solvation shell, thereby promoting more adjustable and advantageous interfacial chemistry. As a result, the fluorine-free Prussian blue||hard carbon pouch cell, with a high cathode mass loading of ca. 20 mg cm−2, reaches a high-capacity retention with an energy density of over 221.7 Wh kg−1 based on electrode mass and 115.1 Wh kg−1 based on battery mass.