Search Results (79)

This study uses electrochemical impedance spectroscopy (EIS) to investigate coupled effects of mechanical deformation depth and size on impedance responses of large-format prismatic lithium-ion batteries (LIBs). Stepwise out-of-plane deformations were applied using hemispherical impactors of two different diameters (30 mm and 180 mm), representing localized and global mechanical loading while maintaining consistent contact conditions. Cells were deformed to 25%, 50%, 75%, and 95% of the internal short-circuit deformation depth, with EIS measurements conducted at each level. Relative changes of measured impedance parameters and fitted equivalent circuit model (ECM) parameters were analyzed. Results show that localized deformation decreases charge transfer resistance ΔR₁ up to 8.0% and total impedance ΔZ up to 1.6%, indicating enhanced charge mobility due to internal structural damage. In contrast, global compression increases ohmic resistance ΔR₀ up to 2.1% and ΔZ up to 2.0%, likely due to reduced separator porosity. Phase angle ΔPhase showed opposite trends under localized and global loading, reflecting different capacitive responses. These results reveal that deformation depth and size significantly influence EIS measurements, with non-linear interactions and transition points indicative of irreversible damage. These results support the use of EIS as a non-destructive diagnostic tool for identifying mechanical damage in LIBs. Full article

This study aims to fabricate magnesium-doped SiOx materials through the integrated application of physical vapor deposition and chemical vapor deposition techniques, with the objective of developing high-performance anode materials for lithium-ion batteries. With the macroscopic particle size held constant, this study endeavors to explore the impact of variations in the size of microscopic silicon crystals on the properties of the material. Under the effect of magnesium doping, the influence mechanism of different microscopic grain sizes on the reaction kinetics behavior and structural stability of the material was systematically studied. Based on the research findings, a reasonable control range for the size of silicon crystals will be proposed. The research findings indicate that both relatively small and large silicon crystals are disadvantageous for cycling performance. When the silicon crystal grain size is 5.79 nm, the composite material demonstrates a relatively high overall capacity of 1442 mAh/g and excellent cycling stability. After 100 cycles, the capacity retention rate reaches 83.82%. EIS analysis reveals that larger silicon crystals exhibit a higher lithium ion diffusion coefficient. As a result, the silicon electrodes show more remarkable rate performance. Even under a high current density of 1C, the capacity of the material can still be maintained at 1044 mAh/g. Full article

Ni-rich layered LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622) cathodes are the most promising candidates for high-energy lithium-ion batteries, but their performance is often limited by structural instability and capacity fading due to large primary particle sizes and surface degradation. Precise control of the primary particle size significantly impacts the performance of NCM622 cathodes and can mitigate fatigue mechanisms, but the underlying processes remain unclear. In this study, NCM622 cathodes with various primary particle sizes were synthesized by applying a controlled co-precipitation strategy by systematically controlling the ammonia feed rate and solution pH during precursor formation. Interestingly, higher ammonia feed rates promoted the formation of smaller, more ordered primary particles, whereas lower feed rates and reduced pH produced larger primary particles in spherical secondary structures. Electrochemical evaluation revealed that cathodes composed of smaller primary particles exhibited enhanced Li⁺ diffusion kinetics and superior electrochemical performance compared to those synthesized under lower ammonia feeding or reduced pH conditions. Moreover, the optimized NCM622 electrode demonstrated excellent rate capability and maintained a stable layered microstructure during cycling, retaining ~86% of its initial capacity. These results demonstrate that fine-tuning the ammonia feeding conditions during co-precipitation provides a simple and effective approach to control primary particle growth, thereby improving the structural integrity and electrochemical durability of NCM622 cathode materials. Full article

In this study, a multi-objective optimization framework based on an artificial neural network (ANN) was developed for an inlet perforated distributor plate in a 24S24P 10 kWh cylindrical lithium-ion battery module using immersion cooling. A combined Newman, Tiedeman, Gu and Kim with Computational Fluid Dynamics (NTGK-CFD) model was used to generate a symmetrically designed space by varying the input variables, including hole size A (mm), hole spacing ΔH (mm), and coolant mass flow rate V_in (kg/s). A three-level full factorial design was used to generate 27 cases, then CFD simulations were performed to provide a training data for the ANN model to predict the output variables, including maximum temperature T_max, maximum temperature difference ΔT_max, and pressure drop ΔP. The results show that the ANN model provides a reliable predictive model, capable of reproducing the thermal-hydraulic behavior of the immersion-cooled battery module with high fidelity via correlation coefficients R of 0.997 for all three output variables. In addition, Pareto-based optimization shows designs that balance cooling efficiency and pumping power. The selected optimal solution maintains T_max within the optimal range at 37.97 °C while reducing ΔP by up to 44%, providing a practical solution for large-scale battery module thermal management in EVs. Full article

This study investigates the application of the Positive Energy District paradigm to two existing and morphologically diverse urban districts in Rome: Testaccio and Valco San Paolo. The research aims to evaluate the feasibility and effectiveness of district-scale energy retrofitting strategies, integrating dynamic simulation tools to model current energy behavior and assess future scenarios. The methodology combines a range of interventions including envelope insulation, high-performance glazing, HVAC system upgrades, efficient lighting solutions, and large-scale photovoltaic deployment. Additionally, the study explores the potential benefits of energy storage systems, with particular focus on the optimal sizing of lithium-ion battery solutions to enhance local self-consumption and reduce grid dependency. Key performance indicators are used to analyze the alignment between renewable energy generation and district demand, as well as the interaction with the electrical grid. By calibrating simulation models with real thermophysical and consumption data, the research ensures methodological robustness and enables the replicability of the proposed approach in other urban contexts. The study offers a comprehensive framework for planners and policymakers seeking to support the decarbonization and resilience of urban districts through the implementation of PEDs. Future developments will focus on optimizing storage management, assessing the environmental impact of battery life cycles, and integrating PEDs within broader urban energy ecosystems. Full article

Hydrometallurgy is currently the mainstream industrial process for recovering valuable components (nickel, cobalt, manganese, lithium, etc.) from spent ternary lithium-ion battery cathode materials. During the crushing of lithium batteries, cathode materials, anode materials (graphite), and electrolytes become mixed. Consequently, fluoride ions inevitably enter the leaching solution during the hydrometallurgical recycling process, with concentrations as high as 100–300 mg/L. These fluoride ions not only adversely affect the quality of the recovered precursor products but also pose environmental risks. To address this issue, this study employs a synthesized lanthanum–zirconium (La@Zr) composite material, with a specific surface area of 67.41 m²/g and a pore size of 2–50 nm, which can reduce the fluoride ion concentration in the leaching solution to below 5 mg/L, significantly lower than the 20 mg/L or higher that is typically achieved with traditional calcium salt defluorination processes, without introducing new impurities. Under optimal adsorption conditions, the lanthanum–zirconium adsorbent exhibits a fluoride ion adsorption capacity of 193.4 mg/g in the leaching solution, surpassing that of many existing metal-based adsorbents. At the same time as the valuable metals, Li, Ni, and Co, are basically not adsorbed, the selective adsorption of fluoride ions can be achieved. Adsorption isotherm studies indicate that the adsorption process follows the Langmuir model, suggesting monolayer adsorption. The secondary adsorption process is primarily governed by chemical adsorption, and elevated temperatures facilitate the removal of fluoride ions. Kinetic studies demonstrate that the adsorption process is well described by the pseudo-second-order model. After desorption and regeneration with NaOH solution, the adsorbent still has a favorable fluoride removal performance, and the adsorption rate of fluoride ions can still reach 95% after four cycles of use. With its high capacity, rapid kinetics, and excellent selectivity, the adsorbent is highly promising for large-scale implementation. Full article

The paper presents a linear optimisation model aimed at improving the design and operational efficiency of home energy systems (HESs). It focuses on integrating photovoltaic (PV) installations, hybrid heating systems, and emerging energy storage systems (ESSs). Driven by the EU climate policy and the evolution of the Polish electricity market, which have caused price volatility, the model examines the economic and technical feasibility of shifting detached and semi-detached houses towards low-emission or zero-emission energy self-sufficiency. The model simultaneously optimises the sizing and hourly operation of electricity and heat storage systems, using real-world data from PV output, electricity and gas consumption, and weather conditions. The key contributions include optimisation based on large data samples, evaluation of the synergy between a hybrid heating system with a gas boiler (GB) and a heat pump (HP), analysis of the impact of demand-side management (DSM), storage capacity decline, and comparison of commercial and emerging storage technologies such as lithium-ion batteries, redox flow batteries, and high-temperature thermal storage (HTS). Analysis of multiple scenarios based on three consecutive heating seasons and projected future conditions demonstrates that integrated PV and storage systems, when properly designed and optimally controlled, significantly lower energy costs for prosumers, enhance energy autonomy, and decrease CO₂ emissions. The results indicate that under current market conditions, Li-ion batteries and HTS provide the most economically viable storage options. Full article

Particles ejected during thermal runaway (TR) of lithium-ion batteries carry a significant fraction of the total TR energy and can cause danger to other components in the battery system. The associated safety hazards should be addressed in the battery pack development process, which requires a deep understanding of TR particle characteristics. In this study, these characteristics are determined by applying several measurement techniques. Among them, dynamic image analysis and large particle image processing are applied to battery abuse particles for the first time, allowing their size and shape to be quantified in detail. Particles are collected from three overheating tests on a prismatic 51 Ah NMC622 cell under vacuum conditions in an autoclave environment. Battery abuse particles cover a wide size range, from micrometers to millimeters, with the largest particle reaching 51.4 mm. They are non-spherical, whereby sphericity, symmetry, and aspect ratio decrease for larger particles. Re-solidified copper droplets and intact separator pieces indicate particle temperatures of ~200–1100 °C at the time of cell ejection. Particles are partially combustible, with an exothermic onset at ~500 °C associated with graphite oxidation. Reactivity is non-linearly size dependent. Implications of these findings for battery system development are discussed. Full article

The rising demand for lithium-ion batteries (LIBs), driven by the growing consumption of electronic devices and the expansion of electric vehicles, is leading to a concerning depletion of primary metal resources and a significant accumulation of electronic waste. This urgent challenge highlights the need for sustainable recovery methods to extract valuable metals from spent LIBs, aligning with circular economy principles. In this study, the preparation of spent batteries for the bioleaching process was achieved with minimal manipulation. This included a preliminary discharge to ensure safety in subsequent processes and a brief crushing to facilitate the access of leaching agents to valuable metals. Unlike most studies that grind batteries to obtain powders between 70 and 200 microns, our approach works with particles sized around 5 mm. Additionally, our preparation process avoids any thermal or chemical treatments. This straightforward pre-treatment process marks a significant advancement by reducing the complexity and cost of processing. A systematic study was conducted on various fractions of the large particle sizes, using Fe (III) produced through bio-oxidation by A. ferrooxidans and biogenically obtained H2SO4 from A. thiooxidans. The highest metal extraction rates were achieved using the unsorted fraction, directly obtained from the black mass after the grinding process, without additional particle separation. When treated with bio-oxidized Fe (III), this fraction achieved a 95% recovery of Cu, Ni, and Al within 20 min, and over 90% recovery of Co, Mn, and Li within approximately 30 min. These recovery rates are attributed to the combined reducing power of Al and Cu already present in the black mass and the Fe (II) generated during the oxidation reactions of metallic Cu and Al. These elements actively facilitate the reduction of transition metal oxides into their more soluble, lower-valence states, enhancing the overall metal solubilization process. The extraction was carried out at room temperature in an acidic medium with a pH no lower than 1.5. These results demonstrate significant potential for efficient metal recovery from spent batteries with minimal pre-treatment, minimizing environmental impact. Additionally, the simplified residue preparation process can be easily integrated into existing waste management facilities without the need for additional equipment. Full article

The lithium-ion battery equalization system is a critical component in Low-Earth Orbit (LEO) satellite power supply systems, ensuring the consistency of battery cells, maximizing the utilization of battery pack capacity, and enhancing battery reliability and cycle life. In DC bus satellite power systems, passive equalization technology is widely adopted due to its simple structure and ease of control. However, passive equalization suffers from drawbacks such as complex thermal design and limited operation primarily during battery charging. These limitations can lead to inconsistent control over the depth of discharge of individual battery cells, ultimately affecting the overall lifespan of the battery pack. In contrast, active equalization technology offers higher efficiency, faster equalization speeds, and the ability to utilize digital control methods, making it the mainstream direction for the development of lithium-ion battery equalization technology. Nevertheless, active equalization often requires a large number of switches and energy storage components, involves complex control algorithms, and faces challenges such as large size and reduced reliability. Most existing active equalization techniques are not directly applicable to DC bus satellite power systems. In this study, based on the operational characteristics of LEO satellite power storage batteries, an active–passive hybrid equalization topology utilizing a switching matrix is proposed. This topology combines the advantages of a simple structure, ease of control, and high reliability. Its feasibility has been validated through experimental results. Full article

Exploring novel two-dimensional layered transitional metal dichalcogenides and elucidating their reaction mechanism are critical to designing promising anode materials for lithium-ion batteries (LIBs). Herein, a novel layered TaS₂ nanosheet was obtained via a typical solid-phase reaction method followed by a simple ball-milling treatment, and first explored experimentally as an anode for LIBs. The TaS₂ nanosheet anode delivered an excellent cycling stability, with 234.6 mAh g⁻¹ after 500 cycles at 1 A g⁻¹. The optimized performance could be attributed to the large interlayer spacing, high conductivity, and reduced size of the TaS₂ nanosheet, which effectively alleviated the volume change during the reaction process and accelerated the Li⁺ or e⁻ transport. Especially, the TaS₂ nanosheet anode presented an unusual intercalation reaction mechanism, accompanied with a reversible phase transition from the 2H to the 1T phase during the first de-lithiation process, which is evidenced by the multiple ex situ characterizations, further revealing the enhanced electrochemical performance results from the 1T phase with the larger interlayer spacing and higher electrical conductivity. This work provides a novel insight into the intercalation reaction mechanism of TaS₂, which shows potential in high-performance LIBs. Full article

Lithium iron phosphate (LiFePO₄) is a promising cathode material for lithium-ion batteries (LIBs), but its low conductivity and poor rate performance limit its application in high-power devices. In this study, we employed a particle size grading strategy to enhance the electrochemical performance of LiFePO₄. By mixing small and large particles in different ratios (3:1, 2:1, 1:1, 1:2, and 1:3), we synthesized graded iron phosphate precursors, which were then used to prepare LiFePO₄ cathode materials. The effects of particle size distribution on the material’s structural properties and electrochemical performance were systematically investigated. SEM images revealed that the morphology of LiFePO₄ changed with varying precursor ratios, with the 3:1 ratio resulting in a more uniform particle distribution. The results showed that the 3:1 ratio exhibited the highest discharge capacity of 159.4 mAh/g, while larger particle ratios (2:1 and 1:1) led to decreased capacity due to the increased proportion of larger particles. Additionally, the LiFePO₄ materials prepared from non-in situ mixed precursors exhibited higher tap densities, with the 2:1 ratio achieving the highest tap density of 2.545 g/cm³. This study demonstrates the effectiveness of the particle size grading approach in improving the electrochemical properties of LiFePO₄ and provides insights into the design of high-performance cathode materials for advanced lithium-ion batteries. Full article

Sodium-ion batteries (SIBs) have garnered significant attention as a cost-effective and sustainable alternative to lithium-ion batteries (LIBs) due to the abundance and eco-friendly extraction of sodium. Despite the larger ionic radius and heavier mass of sodium ions, SIBs are ideal for large-scale applications, such as grid energy storage and electric vehicles, where cost and resource availability outweigh the constraints of size and weight. A critical component in SIBs is the electrolyte, which governs specific capacity, energy density, and battery lifespan by enabling ion transport between electrodes. Among various electrolytes, composite polymer electrolytes (CPEs) stand out for their non-leakage and non-flammable nature and tunable physicochemical properties. The incorporation of NASICON (Na Super Ionic CONductor) fillers into polymer matrices has shown transformative potential in enhancing SIB performance. NASICON fillers improve ionic conductivity by forming continuous ion conduction pathways and reduce polymer matrix crystallinity, thereby facilitating higher sodium-ion mobility. Additionally, these fillers enhance the mechanical properties and electrochemical performance of CPEs. Hence, this review focuses on the pivotal roles of NASICON fillers in optimizing the properties of CPEs, including ionic conductivity, structural integrity, and electrochemical stability. The mechanisms underlying sodium-ion transport facilitated by NASICON fillers in CPE will be explored, with emphasis on the influence of filler morphology and composition on electrochemical properties. By scrutinizing the recent findings, this review underscores the potential of NASICON-based composite polymer electrolytes as appropriate material for the development of advanced sodium-ion batteries. Full article

In 2023, the EU set the target to reduce greenhouse gas (GHG) emissions by 55% until 2030 compared to 1990. The European Transport Policy sees battery–electric vehicles as a key technology to decarbonize the transport sector, so governments support the adoption through dedicated funding programs. Battery–electric trucks hold great potential to decarbonize the transport sector, especially for high-impact, heavy-duty trucks. Theoretical life-cycle assessments (LCA) predict a lower CO₂e emission impact from battery–electric trucks compared to conventional diesel trucks. Yet, one concern repeatedly mentioned by potential users is the doubt about the ecological advantage of battery–electric vehicles. This is rooted in the problem of a much higher CO₂e impact of the lithium-ion batteries production process. As heavy-duty trucks have a much larger battery, the hypothec in the construction phase of the vehicle is significantly higher, which must be regained during the use phase. Although theoretical assessments exist, CO₂e evaluations using real-world application data are almost nonexistent, as the technology is at the very start of the adoption curve. Exemplary is the fact that there were only 72 registered battery–electric heavy-duty tractor trucks throughout the whole of Germany at the start of 2023. This paper aims to deliver one of the first real-world quantifications using operational data for the actual reduction impact of battery–electric heavy-duty trucks compared to diesel trucks. This study uses the methodology of the life-cycle assessment approach according to ISO 14040/14044 to gain a systematic and holistic technology comparison. For this LCA, the system boundaries are considered from cradle to cradle. This includes the production of raw materials and energy, the manufacturing of the trucks, the use phase, and the recycling afterward. The research objects of this study are battery–electric and diesel Volvo FM trucks, which have been in use by the German freight company Nord-Spedition GmbH since May 2023. The GREET^® database is used to assess the emission impact of the material production and manufacturing process. The Volvo tractor trucks resemble a critical case, as the vehicles have a battery size of 540 kWh—around 11 times larger than a usual passenger car. The operation data is directly provided by the logistics company to observe fuel/electricity consumption. Other factors are assessed through company interviews as well as a wide literature research. Finally, a large question mark concerning total emissions lies in the cradle-to-cradle capabilities of large-scale lithium-ion batteries and the electricity grid mix. Different scenarios are being considered to assess potential disposal or recycling paths as well as different electricity grid developments and their impact on the overall balance. The findings estimate the total emissions reduction potential to range between 34% and 69%, varying with assumptions on the electricity grid transition and recycling opportunities. This study displays one of the first successful early-stage integrations of battery–electric heavy-duty trucks into the daily operation of a freight company and can be used to showcase the ecological advantage of the technology. Full article

The growing size and complexity of industrial datasets have intensified the need for efficient fault diagnostics tools. This study addresses the challenge of handling large-scale data by developing a data-driven architecture for fault classification in industrial systems. To extract meaningful insights, 15 time-domain features were combined with 5 Feature Selection Methods to optimize model performance by eliminating redundant features. The sensor data and selected features were analyzed using the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms to enable accurate fault detection and prediction. The proposed framework was validated using publicly available datasets, namely the Case Western Reserve University (CWRU) bearing dataset and the National Aeronautics and Space Administration Ames Prognostics Center of Excellence (NASA PCoE) lithium-ion battery dataset. The results demonstrate the framework’s adaptability and high efficacy across diverse scenarios, achieving an average F1-score exceeding 98.40% using only 10 selected features. This finding highlights the effectiveness of embedded Feature Selection Methods in improving classification performance while reducing computational complexity. The study underscores the potential of the proposed framework as a foundational tool in intelligent manufacturing, offering a versatile solution to enhance fault diagnostics in diverse industrial applications. Full article