Search Results (395)

The enormously increasing demand for rare earth elements (REE), due to their wide high tech-application areas and their limited and unproportioned reserves across the globe, induced the utilization of secondary resources to provide more robust REE supply chains. In several studies, hydrometallurgical/pyrometallurgical routes have been employed to recover REE’s from secondary resources such as industrial residues, end-of-life magnets, batteries, and catalysts. In this pioneer study, we investigate the feasibility to use end-of-life RE-silicate environmental barrier coatings (EBCs) of turbine engine components as a secondary Yb resource. For this purpose, state-of-the-art EBC materials, ytterbium monosilicate (Yb₂SiO₅), and ytterbium disilicate (Yb₂Si₂O₇), were exposed to a fictional aeroengine scenario involving in service contamination by airborne mineral dusts, commonly referred to as CMAS corrosion. CMAS-corroded Yb-silicate pellets were exposed to sulfuric acid leaching. Phase and microstructural analyses were conducted on starting materials and leaching residues, in a comparative manner, to explain the leaching mechanism. Leaching solutions were analyzed by ICP-OES indicating a very promising preliminary leaching efficiency and selectivity for Yb₂SiO₅, whereas Yb₂Si₂O₇ displayed a very low leachability. Further prospects were suggested to enhance process efficiency and implications on repair/overhaul end-of-life Yb-silicate EBCs are discussed. Full article

While battery electric vehicles (BEVs) are pivotal for transport decarbonization, existing life cycle assessments (LCAs) often confound vehicle design effects with inter-brand manufacturing variations. In this study, a comparative cradle-to-grave LCA was conducted for three distinct BEV segments—a sedan, an SUV, and an MPV, produced by a single manufacturer on a shared platform. Leveraging detailed bills of materials, plant-level energy data, and region-specific emission factors for a functional unit of 150,000 km, we quantify greenhouse gas emissions across the full life cycle. Results show the total emissions scale with vehicle size from 25 to 31 t CO₂-eq. However, the MPV exhibits the highest functional carbon efficiency, with the lowest emissions per unit of interior volume. Material production and operational electricity use dominate the emission profile, with end-of-life metal recycling providing a 15–20% mitigation credit. Scenario modeling reveals that grid decarbonization can slash life cycle emissions by around 30%, while advanced battery recycling offers a further 15–18% reduction. These findings highlight that the climate benefits of BEVs are closely linked to progress in power system decarbonization, and provide references for future optimization of low-carbon vehicle production and reuse. Full article

With rising efforts to enable a circularity of valuable resources of lithium-ion batteries, a growing number of recycling companies in Europe are expanding their capacities and developing new recycling technologies. The European Union (EU) has set a benchmark for battery recycling by publishing recycling targets. These targets require precise mass determination of the individual battery components, making disassembly of the battery mandatory for characterization. The paper puts forth a semi-automated disassembly procedure for determining the composition of the components at the module and cell levels across a range of designs. Our analysis incorporates the introduction of TGA as a novel, direct method for determining the cathode active material with an accuracy above 99%. This approach is intended to define the recycling input for all extant recycling routes by providing quantitative experimental results with statistical significance. The results indicate a black mass proportion of 61.6% at the module level and 53–74% at the cell level. Additionally, there are significant differences in value creation, ranging from 0.80 to 1.81 USD kg⁻¹ black mass, depending on the cell chemistry. The procedure can be used for EoL and scrap material, and enables greater transparency and comparability in battery recycling, opening up new perspectives for the resource-efficient and targeted use of various recycling technologies. Full article

Background: Research to identify changes in driving behavior that occur with the onset of Pre-MCI and MCI is an emerging area with many gaps still to be addressed. These gaps include limited use of objective, continuous measurement of driver behavior in real-life traffic conditions and comprehensive, biomarker-validated, cognitive evaluation based upon both testing and clinical ratings. Using these strategies, the questions addressed in this exploratory study are whether or not differences in driving behavior are indicative of Pre-MCI/MCI and which behaviors are most predictive of Pre-MCI/MCI. Methods: As part of a naturalistic longitudinal study, older drivers with a Montreal Cognitive Assessment score ≥ 19 had telematic sensors installed in their vehicles and underwent comprehensive cognitive assessment quarterly for three years. Thirty-six participants were classified as Unimpaired (n = 23) or Pre-MCI/MCI (n = 10/3) based upon a neuropsychological battery and diagnostic algorithm. A penalized generalized linear mixed-effects model (GLMM) with a logistic link and LASSO regularization was used to model Pre-MCI/MCI group membership vs. unimpaired as a function of ten trip-level telematic features (trip distance, hard acceleration, hard braking, hard turns, speed average, maximum speed, RPM average, fuel level, throttle average, and throttle variability) at the end of their first 12 months in the study. Results: Higher RPM, shorter average trips, and greater throttle variability predicted higher odds of Pre-MCI/MCI, while more frequent hard braking, hard turns, higher mean speed, and lower average throttle (steadier pedal control) predicted lower odds of Pre-MCI/MCI. Conclusions: The model clearly distinguished unimpaired older drivers from those with MCI or Pre-MCI, suggesting that distinct patterns of driver behavior may be related to levels of cognitive function. Full article

This study investigates whether Battery Electric Vehicles (BEVs) or Fuel Cell Electric Vehicles (FCEVs) represent the superior alternative to conventional vehicles for last-mile delivery, with a particular focus on large enterprises that prioritize both economic feasibility and environmental performance. Life Cycle Assessment and Life Cycle Cost methodologies are applied to evaluate both technologies across the full cradle-to-grave life cycle within a unified framework. The functional unit is defined as one kilometer traveled by a BEV or FCEV in last-mile transportation, and the system boundary includes vehicle manufacturing, operation, maintenance, and end-of-life treatment. The environmental impacts are assessed using the ReCiPe 2016 Midpoint (H) method implemented in OpenLCA 2.0.4, and normalization follows the standards provided by the official ReCiPe 2016 framework. The East China Power Grid serves as the baseline electricity mix for the operational stage. Regarding GHG emissions, FCEVs demonstrate a 12.36% reduction in carbon dioxide (CO₂) emissions compared to BEVs. This reduction is particularly significant during the operational phase, where FCEVs can lower CO₂ emissions by 53.51% per vehicle relative to BEVs, largely due to hydrogen energy’s higher efficiency and durability. In terms of economic costs, BEVs hold a slight advantage over FCEVs, costing approximately 0.8 RMB/km/car less. However, during the manufacturing phase, FCEVs present greater environmental challenges. It is recommended that companies fully consider which environmental issues they wish to make a greater contribution to when selecting vehicle types. This study provides insight and implications for large companies with financial viability concerns about environmental impact regarding selecting the two types of vehicles for last-mile transportation. The conclusions offer guidance for companies assessing which vehicle technology better aligns with their long-term operational and sustainability priorities. It can also help relevant practitioners and researchers to develop solutions to last-mile transportation from the perspective of different enterprise sizes. Full article

Lithium-ion batteries are widely used in energy storage systems, and temperature is an important factor that affects the battery aging performance. Battery aging tests have been conducted in environmental chambers in numerous studies. The ambient temperature is usually regarded as an indicator that affects the battery aging performance. However, with the same ambient temperature but different heat exchange conditions, the battery cycle aging life can still vary. In this study, a side face temperature control device and end faces temperature control device for a cylindrical battery were designed and made. Together with the environmental chamber, three types of heat exchange conditions were used to conduct cycle aging tests for the 21700 cylindrical battery. Based on the aging results of batteries under different heat exchange conditions, the battery aging mechanisms were analyzed. At the end of the battery’s life, the maximum loss rate of the active anode material is close to 20%, and the loss rate of the lithium inventory of most test groups is approximately 10%. The internal resistance growth rate of the aged battery can exceed 50%. During the battery aging process, battery temperature data were monitored, and the cumulative time-averaged surface temperature (CTAT) was proposed as a new metric to assess the temperature level for the long-term operating battery. The aging results of the 21700 cylindrical batteries show that within the temperature range of this study, the lower the CTAT, the faster the battery capacity degrades. The correlation between the battery temperature level and aging performance was also analyzed, which can be used to predict the battery cycle life. The analysis of battery aging mechanisms and the proposed temperature metric in this study provide guidance for research on battery life sustainability, as well as the thermal management strategy design of the battery. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

The transition to electric vehicles (EVs) plays a critical role in reducing global carbon emissions. However, the end-of-life management of electric vehicle batteries (EVBs) presents significant sustainability and operational challenges. This study proposes a blockchain-based framework that enables full lifecycle tracking of EVBs, from production to disposal or reuse, while addressing issues of transparency, efficiency, and regulatory compliance. The framework incorporates a multi-criteria decision model to guide data-driven end-of-life routing—whether for second-life reuse or direct recycling—based on technical, environmental, and economic indicators. By integrating smart contracts with a hybrid web/mobile platform, the system ensures tamper-proof documentation, stakeholder accountability, and compliance with the EU battery passport regulation. A detailed cost analysis of deploying the framework on Ethereum is also presented. The proposed solution aims to enhance the sustainability of EVB management, reduce environmental impact, and promote circular economy practices within the EV industry. Full article

This study analyzes the lifetime characteristics and degradation behavior of lithium-ion batteries under increasing charge–discharge cycles. The experiment focused on RE (Real Part), IM (Imaginary Part), and DCIR Degradation% (Direct Current Internal Resistance Degradation). The RE increased from 0.0023 Ω at the initial state to 0.00293 Ω after 1200 cycles, representing a 28% rise, with a sharp acceleration after 400 cycles due to interfacial resistance buildup and electrolyte decomposition. The IM shifted from negative to positive values, indicating delayed electrochemical reactions and enhanced inductive behavior. A pronounced transition occurred between 400 and 800 cycles, confirming this range as a critical phase of performance degradation. Correlation analysis between SoH (State of Health) and DCIR Degradation% showed that while SoH decreased slightly from 100% to 87.3%, DCIR Degradation% increased significantly to 137.8%, indicating that internal resistance growth is the dominant cause of aging. When SoH falls below 70%, the battery reaches its effective end-of-life, accompanied by severe heat generation and power loss. In conclusion, the combined analysis of RE, IM, and DCIR Degradation% demonstrates that accumulated internal resistance is the key factor determining battery lifetime. Stabilizing the SEI layer, reinforcing electrode structures, and improving electrolyte stability are essential strategies for extending battery durability. Full article

The recovery of critical metals from spent lithium-ion batteries (LIBs) is essential to reduce environmental impacts and promote circular economy strategies. This study developed a sustainable and scalable process for the recovery and complete valorization of lithium, cobalt, and other valuable components from end-of-life LIBs. Hydrometallurgical treatment using biodegradable citric and oxalic acids was employed as a green alternative to conventional inorganic acids, achieving high selectivity and reduced environmental impact. Experimental work was conducted on 3 kg of LIBs from discarded laptop batteries (Dell and HP). After safe discharge and dismantling, the cathode materials were thermally treated at 300 °C to detach active components, followed by acid leaching in 1 M citric acid at 30 °C, pH 2.5, and 6 h of reaction. Lithium and cobalt were recovered as oxalates with efficiencies of 90% and 85%, respectively, while copper, aluminum, and graphite were separated through mechanical and thermal processes. Beyond metal recovery, the process demonstrates a circular upcycling approach, transforming recovered materials into functional products such as aluminum keychains, copper jewelry, and graphite-based pencils. This integrated strategy connects hydrometallurgical extraction with material reuse, advancing toward a zero-waste, closed-loop system for sustainable LIB recycling and local resource valorization. Full article

The accelerating integration of electromobility into renewable-based power systems necessitates a comprehensive understanding of vehicle life cycles and their interactions with emerging smart grid infrastructures. This study employs a Life Cycle Assessment (LCA) approach to evaluate the environmental performance of materials and components used in A- and B-segment passenger vehicles, within the framework of sustainable energy system development. Four propulsion technologies—petrol, diesel, compressed natural gas (CNG), and battery electric vehicles (BEVs)—were analyzed across two technological horizons (2020 and 2050), considering both landfilling and recycling end-of-life scenarios. The results demonstrate that while BEVs offer the lowest operational emissions and the greatest potential for supporting grid flexibility and renewable energy integration, they also exhibit the highest environmental burdens during production, primarily due to battery manufacturing. Nevertheless, the adoption of advanced recycling technologies significantly mitigates these impacts by reducing resource depletion, global warming potential, and cumulative energy demand. The findings highlight that circular material management and high-efficiency recycling are critical enablers of sustainable electromobility. By linking vehicle charging, energy storage, and recycling strategies, the integration of transport and energy systems can enhance grid stability, improve resource efficiency, and accelerate progress toward a decarbonized, resilient, and smart energy future. Full article

The rapid growth of electric vehicles (EVs) and energy storage systems (ESSs) has accelerated the generation of waste lithium-ion batteries, posing both environmental and industrial challenges. This study proposes and experimentally validates an AI-based Waste Battery and Plasma Convergence System (AI-WBPCS) designed to integrate residual energy recovery from retired EV batteries with adaptive plasma control. The system aims to establish a self-optimizing energy reuse framework that enhances real-time energy utilization, improves plasma process stability, and supports sustainable circular energy ecosystems. The AI-WBPCS consists of three key sub-models: D₁ for plasma output prediction, D₂ for battery health evaluation, and D₃ for adaptive energy-matching control. These models operate synergistically under a hybrid STM32–Jetson Nano platform, enabling predictive analysis and closed-loop optimization. Experimental validation using 2P6S retired EV modules demonstrated that the D₂ model achieved a 93.7% SOH prediction accuracy and a 2.3% mean absolute error (MAE) in DCIR estimation. The AI-controlled plasma subsystem maintained output stability within ±2.1%, compared to fluctuations exceeding 6% under conventional rule-based methods. The overall energy-matching efficiency (η) reached 96.5%, representing a 13% improvement in power coordination performance. Interpretability analysis using SHAP (SHapley Additive exPlanations) identified SOH (46%) and DCIR (29%) as the dominant features influencing AI-driven decisions, confirming the physical relevance and transparency of the model. The AI-WBPCS provides a practical pathway toward circular-economy-oriented energy reuse, enabling intelligent, autonomous plasma systems for applications such as smart agriculture, biomedical sterilization, and decentralized wastewater treatment. Overall, this research establishes a new paradigm for AI-empowered electrochemical–plasma systems, where artificial intelligence not only enhances operational efficiency but also redefines end-of-life batteries as adaptive energy resources for next-generation green technologies. Full article

The removal of the electrolyte solvents in an early-stage thermal drying step is crucial for safe and efficient recycling processes for end-of-life lithium-ion batteries. A comprehensive understanding of the governing influences on the solvent volatilization during the drying step enables optimized processes. The initial phase of this process is of particular interest because, due to the high spatial accessibility of the solvent, drying is determined by the mass transport in the surrounding gas phase, which can be precisely controlled through the process boundary conditions. In this study, the evaporation of representative binary and ternary electrolyte–solvent mixtures containing linear and cyclic organic carbonates is investigated under defined boundary conditions. The evaporation kinetics and selectivity are assessed by time-discrete measurement of the amount of solvent and its composition during the evaporation experiments. At the conditions applied, the vapor pressure of the solvents governs the evaporation selectivity, with the evaporation kinetics dictated by the mass transport of the solvent vapor in the gas phase. Hence, the evaporation of highly mobile but low volatile solvents, such as ethylene carbonate (EC), is the constraining aspect within this process. Moreover, molecular interactions within mixtures can further hinder the volatilization of EC. The developed simulation model describes the evaporation behavior with high accuracy and thus allows the prediction of minimum drying times. It establishes a solid foundation for designing and scaling the drying processes of end-of-life batteries, which involve complex material interactions. Full article

The rapid growth of China’s electric vehicle (EV) market has led to a peak in end-of-life (EOL) power batteries, yet the recycling sector remains dominated by informal operations. This paper incorporates the formal and informal recycling participation behaviours of EV owners into the framework of evolutionary games, systematically examines the mechanism by which governmental incentive and disincentive mechanisms influence the evolutionary stability of each party, and constructs a tripartite evolutionary game model involving the government, recycling enterprises, and EV owners. Numerical simulation experiments conducted using PyCharm 2.3 provide an in-depth exploration of the strategic evolutionary trajectories of each participating agent. The findings indicate that (1) the stable strategy for the game-theoretic system of EOL power battery recycling is government non-regulation, recycling enterprises adopting formal recycling practices, and EV owners participating in formal recycling; (2) strengthening penalties against recycling enterprises will accelerate their transition towards formal recycling strategies, while increasing incentive levels can significantly enhance the steady-state probability of firms opting for formal recycling; (3) government subsidies for EV owners encourage both EV owners and recycling enterprises to adopt formal recycling, with recycling enterprises shifting first. This study enriches the application of evolutionary game theory in the field of EOL power battery recycling and further provides guidance for the healthy development of the recycling industry. Full article

The rapid increase in retired lithium-ion batteries (LIBs) from electric vehicles (EVs) highlights the urgent need for accurate and automated end-of-life (EOL) assessment. This study proposes an AI-integrated smart grading system that combines hardware diagnostics and deep learning-based evaluation to classify the residual usability of retired batteries. The system incorporates a bidirectional charger/discharger, a CAN-enabled battery management system (BMS), and a GUI-based human–machine interface (HMI) for synchronized real-time data acquisition and control. Four diagnostic indicators—State of Health (SOH), Direct Current Internal Resistance (DCIR), temperature deviation, and voltage deviation—are processed through a deep neural network (DNN) that outputs categorical grades (A: reusable, B: repurposable, C: recyclable). Experimental validation shows that the proposed AI-assisted model improves grading accuracy by 18% and reduces total testing time by 30% compared to rule-based methods. The integration of adaptive correction models further enhances robustness under varying thermal and aging conditions. Overall, this system provides a scalable framework for automated, explainable, and sustainable battery reuse and recycling, contributing to the circular economy of energy storage. Full article