Search Results (407)

Recycling of lithium-ion batteries (LIBs) remains challenging due to high energy requirements, losses of key elements like lithium, and the heterogeneity of waste streams arising from different cathode chemistries. This study evaluates multiple recycling methods for LIBs black mass (BM), to recover critical raw materials and other valuable components. Three types of BM including nickel–manganese–cobalt (NMC), lithium iron phosphate (LFP) and a heterogeneous mixture of cell phones and laptops (HL; German: Handy/Laptops), were treated using froth flotation, pyrometallurgy, and biohydrometallurgy and their respective recovery efficiencies were assessed. The flotation results revealed that the HL sample had the lowest mis-recovery of non-ferrous metals into the froth product (around 10%), leading to further flotation only for HL. During screening, 94–99% of iron, phosphorus, and carbon in LFP-type BM were recovered in the fine fraction (<45 µm), while 92–99% of lithium, cobalt, manganese, nickel, and carbon in NMC-type BM were recovered in the same fraction. During precipitation, 99% of iron and 100% of phosphorus were recovered from LFP bioleachates at pH 3, while ~97–100% of dissolved cobalt, manganese, and nickel were recovered from NMC bioleachates. These findings confirm that no single recycling method is optimal for all battery chemistries. Full article

The increasing number of new battery electric vehicle (BEV) registrations worldwide and the development of advanced batteries with higher energy density and pack capacity have contributed to large volumes of batteries approaching the end of their first service life. The retired batteries may be repurposed in second-life applications or recycled. The degradation profile of BEVs is a critical determinant in second-life potential of the retired batteries. This study presents a comprehensive analysis of calendar and cycle mechanisms in lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC) batteries across 37 commercially available BEV models in the European Union (EU) market. Three scenarios are considered, based on the operational temperature—Scenario I with a 273.15 K, Scenario II with a 298.15 K, and Scenario III with a 318.15 K operational temperature—and two degradation metrics are determined for each analyzed BEV: state-of-health (SoH) and remaining capacity at end-of-life (EoL). The results show that the SoH of both chemistries is highly dependent on the state-of-charge (SoC), temperature, depth of discharge (DoD), and real usable capacity. Moreover, the Tesla Model 3–Premium RWD, Volkswagen ID.3, ID.4, and ID5–GTX, and the Tesla Model Y–Premium AWD all exhibit a remaining capacity between 40 and 77 kWh at EoL, depending on degradation profile, making them a viable option for second-life applications. Full article

Battery Energy Storage Systems (BESS), utilising chemistries based on Nickel Manganese Cobalt (NMC) containing lithium-ion devices, often present fire safety hazards that existing qualitative risk frameworks, including NFPA 855’s 5 × 5 consequence-likelihood matrix, are insufficiently granular to quantify. This paper presents an original probabilistic risk assessment (PRA) of fire hazards associated with BESS for a 485.52 kWh NMC installation at the Equinix SG4-4A data centre in Singapore, using Monte Carlo simulation (N = 10,000 iterations) to characterise uncertainty in hydrogen fluoride (HF) gas dose, time to Immediately Dangerous to Life or Health (IDLH) concentration, cabinet-to-cabinet propagation probability, and suppression effectiveness. The HF yield is modelled as a triangular distribution (0.3–0.8 g/kWh, mode 0.5 g/kWh), ventilation activation delay as log-normal (median 90 s), and suppression effectiveness as a piecewise function of water application delay. The results demonstrated that HF dose exceeded the National Institute for Occupational Safety and Health (NIOSH) IDLH of 25 mg/m³ in 100% of simulated scenarios for both single- and two-compartment designs, thus confirming that threshold HF toxicity was essentially unavoidable for any occupant present during a full thermal runaway event, and that ventilation alone cannot achieve adequate risk reduction. The single-stage suppression effectiveness was found to be only 37.9% (mean), providing quantitative confirmation that two-stage (clean agent + water) suppression is warranted for NMC chemistry. The two-compartment design was found to reduce the peak HF dose by 50%, and also reduced the mean IDLH clearance time from 599 to 301 min, thus shifting residual risk from As Low As Reasonably Practicable (ALARP)-tolerable to broadly acceptable under UK Health and Safety Executive (HSE) criteria. The paper proposes a quantitative PRA framework as a complement to NFPA 855 Chapter 5’s qualitative Hazard Mitigation Analysis, enabling more informed engineering decisions for BESS fire safety. To the best of our knowledge, this is the first study to apply Monte Carlo simulation to HF dose modelling in a tropical data-centre BESS context and thereby address a documented gap in the literature. Full article

In the context of an increasing demand for electricity, batteries increasingly appear as one of the main sources of power supply. In particular, research on batteries is mainly focused on new and high-performance materials, and innovative and more sustainable production processes. This work addresses both aspects by developing a gravure-printed cathode based on lithium nickel manganese cobalt oxide (NMC 111, LiNi_0.33Mn_0.33Co_0.33O₂). To this end, the formulation of a gravure-printable ink was investigated in order to meet both the printing and functional requirements. The formulation of a multicomponent dispersion able to produce a cathodic layer was particularly challenging, since such a system was found to be highly sensitive to the composition and specific interactions among the active material and the other components involved in ink preparation. Although a methodology based on Ca, aimed at obtaining layers with high macroscopic printing quality, was adopted, only a few printable inks having similar characteristics were obtained. From a microscopic point of view, printing allowed the identification of the best ink formulation able to produce the greatest layer homogeneity, thus yielding the best possible performance (150 mAh g⁻¹ at C/20). Full article

The rapid increase in electric vehicles has increased the generation of spent lithium-ion batteries (LIBs) and the need for efficient lithium recovery technologies. This study compared two distinct sulfation roasting routes, using sodium sulfate (Na₂SO₄) at 750 °C and sulfuric acid (H₂SO₄) at 550 °C, applied to black mass derived from lithium iron phosphate (LFP) and lithium nickel manganese cobalt oxide (NCM) batteries. Metal extraction efficiencies were determined by inductively coupled plasma optical emission spectrometry, while reaction products were identified by X-ray diffraction analysis. Sulfation roasting using Na₂SO₄ resulted in low lithium recovery for both materials, with maximum extractions of 5.7% for LFP and 24.5% for NCM. In contrast, H₂SO₄-assisted roasting achieved high lithium recovery from NCM black mass, reaching 90.8%, 91.5%, and 88.5% at 45, 90, and 180 min at 550 °C, respectively, with lithium predominantly converted into water-soluble lithium sulfate. Lithium extraction from LFP black mass remained below 13% under all conditions. Statistical analysis confirmed that lithium recovery at 45 min was equivalent to longer residence times, while prolonged roasting increased manganese coextraction and altered cobalt and nickel behavior. Overall, sulfuric acid-assisted sulfation roasting is an efficient and energy-favorable route for lithium recovery from NCM black mass, whereas sulfation roasting is unsuitable for LFP materials, under the tested conditions. The results highlight the importance of cathode chemistry segregation and demonstrate the feasibility of reducing processing time without compromising lithium recovery. Full article

State of charge (SoC) estimation for an electric vehicle (EV) is critical for range estimation, preventing overcharging or undercharging, energy management, and battery lifespan. The current studies are typically based on single-instance learning, focus on a specific battery chemistry or a single vehicle type, rely on controlled Lab conditions, and often lack explainability mechanisms. To overcome all these limitations, this paper proposes a more practically applicable and explainable SoC estimation framework that jointly addresses vehicle diversity (bus and passenger EVs), battery chemistry variability (Nickel Cobalt Manganese and Lithium Iron Phosphate), collective battery dynamics, and real-world on-road operational uncertainties within a unified learning architecture. This study introduces MIL-LGBM, a specialized method that integrates Multiple Instance Learning with Light Gradient Boosting Machine to effectively capture the complex nonlinear behavior of battery SoC while maintaining a low computational footprint. An experimental study conducted on a real-world dataset demonstrated that the proposed framework achieved a 0.799 MAE for passenger vehicles across a wide range of on-road driving conditions. Full article

Accurate state-of-health (SoH) prediction is vital for safe and efficient battery management, enabling extended lifespan and improving technologies such as electric vehicles and stationary energy storage systems. In this work, a graph neural network-based deep learning framework is proposed to predict the SoH of a commercial nickel–manganese–cobalt oxide (NMC) lithium-ion technology. Health indicators obtained from the in-house-generated experimental aging dataset are used to train and validate the model across batteries subjected to diverse operating conditions. The hybrid architecture combines graph neural networks (GraphSAGE) with convolutional neural networks (CNN) and long short-term memory (LSTM) blocks, capturing both local structural relationships and temporal patterns in the battery data. Evaluation results show strong predictive performance, achieving an R² of 0.989 and a mean squared error of 3.23 × 10⁻⁶. These findings suggest that the proposed methodology could be deployed as a useful diagnostic tool. Full article

Battery use in trolleybuses improves energy efficiency and enables driving outside routes with overhead contact lines. This paper analyses the ageing process of lithium-ion batteries by determining the State of Heath (SOH) curve based on real-world data collected during six and a half years of trolleybus operation. The battery, manufactured using NMC technology (lithium-nickel-manganese-cobalt LiNiMnCoO₂), was used as an onboard energy storage unit. The battery pack powers the trolleybus on the non-electrified route segments and improves its energy efficiency. In this paper, the ageing process of the lithium-ion battery in such a vehicle was investigated, using recorded data for each day of operation between 2016 and 2023. The SOH of the battery was estimated on the basis of three criteria: specific SOC range, specific battery voltage range and specific battery pack temperature range. Under these circumstances, the values of electric charge and energy flow into the battery were analysed, allowing the obtainment of the battery SOH value. Empirical distributions of random variables related to the minimum and maximum battery temperature and battery discharge current were presented. Based on these empirical distributions, statistical descriptors representing health indicators were calculated. The environmental factors (temperature, SOC, discharge and charge currents) that had a significant impact on the ageing process of the battery under test were analysed as well. Full article

The operational safety and longevity of Lithium-ion Nickel Manganese Cobalt Oxide (NMC) battery packs depend on the early detection of gradual cell degradation rather than reactive fault protection. Conventional Battery Management Systems (BMS) predominantly rely on fixed threshold-based mechanisms, which are insufficient for identifying long-term abnormal trends at the individual cell level preceding failure. This studyproposes an intelligent IoT-based battery monitoring and visualization framework for trend-oriented abnormal behavior analysis in a 72 V, 20 cell NMC battery pack. A JK-BMS performs cell voltage acquisition, while an ESP32-S3 microcontroller operates as an IoT gateway, wirelessly collecting high-resolution cell level data via Bluetooth Low Energy (BLE). The data are transmitted to a Home Assistant platform, which provides centralized time-series visualization and comparative cell analytics. The primary contribution is a heuristic anomaly detection algorithm that evaluates temporal voltage trends of individual cells, with emphasis on instability within the critical operating range of 3.0–3.5 V. Unlike conventional threshold-based approaches, the proposed method detects repeated abnormal patterns over time. A frequency-based alert mechanism categorizes battery health into normal, warning, and critical states based on cumulative anomaly occurrences, enabling progressive degradation assessment. Experimental results demonstrate that the proposed framework effectively identifies early-stage degradation patterns that remain undetected by conventional BMS logic. The system supports predictive maintenance, enhances operational safety, and provides a scalable, cost-effective solution for advanced battery health monitoring in electric mobility and distributed energy storage applications. Full article

The increasing demand for lithium-ion batteries (LIBs) raises concerns about the security of critical raw material supply and the management of hazardous waste. Efficient recycling can alleviate these issues by transforming spent batteries into high-value secondary materials for the circular economy. Industrial recycling has traditionally focused on the recovery of nickel (Ni) and cobalt (Co), whereas lithium (Li) recovery has often been sidelined due to technical complexities and fluctuating economic incentives. To meet the European Union (EU) Batteries Regulation target of 80% lithium recovery by the end of 2031, technically effective and economically viable lithium recovery strategies are required. This study investigates the use of food-grade sucrose as an organic reductant for the targeted recovery of lithium from NMC622 and NCA battery materials. The process combines sucrose-assisted reductive roasting with selective water leaching. The effects of roasting temperature, holding time, sucrose dosage, and heating rate were systematically evaluated and optimised. Under the best conditions of 600 °C, 15 min, 15 wt% sucrose, and a heating rate of 20 °C/min, lithium leaching efficiencies of 93.2% and 87.6% were achieved for separated NMC622 cathode material and NMC622-derived black mass, respectively. The method was also applicable to NCA-based black mass, reaching 83.7% lithium recovery under the same conditions. Mechanistic analysis revealed that lithium release was strongly controlled by the extent of transition metal reduction. Cobalt was fully reduced to its metallic state under all tested conditions. However, maximum lithium recovery required nickel to be reduced to metallic Ni and manganese-containing phases to be converted to MnO. The sucrose-assisted roasting process was rapid and holding times longer than 15 min decreased lithium recovery. This decrease was caused by the formation of poorly soluble lithium-containing phases, such as LiF and Li₃PO₄. F composition analysis showed the black mass (1.06 wt%) and anode fractions (2.26 wt%) to contain significantly more F than the cathode fraction (0.46 wt%), hence leading to the 5% Li leaching efficiency difference between cathode and black mass fractions under most conditions tested. Overall, these results demonstrate that sucrose-assisted reductive roasting, followed by selective water leaching, provides a rapid and effective route for high-efficiency lithium recovery from NMC- and NCA-based battery materials. Full article

The adoption of lithium-ion batteries (LIBs) as secondary rechargeable batteries across many industries, including consumer electronics, electromobility, industrial tools, and electrical energy storage, is on the rise. As lithium-ion batteries approach the end of their life, there is a need to assess them for the possibility of a secondary application or reuse for a less demanding application. The extra connections of individual cells, BMS, temperature sensors, and other components to form a compact battery pack pose a challenge for second-life assessment, which usually prefers to separate individual cells for testing before discarding very bad cells for recycling and grading cells with substantive capacity based on their remaining capacity. This is a high cost for the second-life assessment. This work seeks to investigate an approach that avoids dismantling the battery pack into individual modules, cells, and BMS by including a BMS feature that allows the capacity and power ratings to be rescaled onboard after its first use. A set of cells with different chemistries was used in this work: a nickel–cobalt–aluminium oxide cathode with a silicon-doped graphite anode (NCA-GS), a nickel–cobalt–aluminium oxide cathode and graphite, and a lithium–nickel–manganese–cobalt oxide (NMC) cathode with a graphite anode (NMC-G) with various ageing states and behaviours. Their internal resistance and capacity at the beginning and end of life were compared. The scaling factor was obtained by finding the square root of the ratio of the internal resistance at EOL to that at BOL. With the current obtained by multiplying the cycling current rate by the rescaling factor, the surface temperature profile of the aged cells during cycling became the same as the temperature at the beginning of life. The relaxation voltage after discharge to 0% SOC and charge to 100% SOC was used to set the low and high cut-off voltages, respectively. This contributed significantly to reduced ageing and to a lower temperature rise in the spent cells. This set the stage for rescaling or derating battery systems without separating the individual cells, which is a huge cost for second-life use of lithium-ion batteries. BMS can be designed with configurable voltage and current limits, so that when repurposed for a second life, only a simple configuration or firmware update may be necessary. Full article

This two-year study proposes the application of industrial computed tomography (CT) as a complementary technique to conventional capacity and internal resistance measurements for evaluating not only the state of health (SOH) of different lithium-ion battery types used in electric vehicles, but also to predict its past. While commonly used assessment methods primarily focus on electrical properties of batteries, industrial CT allows non-destructive, three-dimensional visualization and systematic evaluation of internal structural changes within individual battery cells and allows to compare different lithium battery type internal structure changes. The study investigates two lithium-ion battery chemistries: lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC). The effects of different discharge rates (1C, 2C, and 3C) on battery degradation were analyzed by comparing CT scan data obtained for the cells in their initial (new) condition and after reaching 60% SOH following cycling-induced aging. The findings provide improved understanding of the physical processes associated with battery aging under varying discharge conditions, enabling a more complete evaluation of battery health. Full article

The blue, white, and black glazed areas of nineteen Nevers earthenware pieces bearing a date or precisely datable between 1589 and 1865 were for the first time analyzed by X-ray fluorescence spectroscopy at the Musée de la faïence et des Beaux-Arts-‘Frédéric Blandin’ in the city of Nevers by pXRF in order to categorize the raw materials and recipes used. The semi-quantitative signal comparison of major elements and impurities such as rubidium, strontium and zirconium shows the use of the same raw materials except for six artifacts. At least three types of cobalt, characterized by association with copper, nickel, and manganese, are observed. Different blacks (with manganese or bismuth) are observed. A comparison is made between the classification obtained with chemometry (z-score, PCA, and dendrograms of similarity) and a reasoned analysis of ternary diagrams based on the signal of the most characteristic elements. This preliminary work demonstrates the potential provided for the categorization of enameled ceramics and their dating through non-invasive on-site semi-quantitative elemental analyses. No important advantages were observed for the chemometric procedure: the same conclusions are obtained by quantitative comparisons of the XRF data, but the chemometric procedures allow a clear visualization of the main conclusions. Full article

Achieving the EU’s climate goals by 2050 requires a rapid transition to a resource-efficient and circular economy. The electrification of transport increases the demand for rechargeable lithium-ion batteries (LiBs), where lithium–nickel–cobalt–manganese (Li-NMC) is the predominant cathode technology in the European automotive sector. Large-scale facilities for LiB production and recycling are emerging worldwide, bringing not only technical challenges but also challenges regarding healthy and safe working environments. Current knowledge on occupational exposure and health risks in the LiB industry is limited and largely based on evidence from other occupational settings. However, the LiB industry involves legacy and new combinations of metals and chemicals in novel contexts. Some of these substances have well-known adverse health effects, and combined exposure may increase their absorption and toxicity. Although processes are often highly specialised and automated, manual handling tasks remain, which put workers at risk of exposure. Important knowledge gaps remain regarding exposure levels, exposure pathways, dermal and systemic uptake, combined exposures, and potential health effects among workers. This perspective paper discusses current exposure scenarios and health risks in LiB production and recycling, identifies key knowledge gaps, and highlights future research needs to support evidence-based occupational risk management. To address several of these challenges, the GreenWorkLiB initiative applies a multidisciplinary approach combining exposure assessment, biomonitoring, and occupational medicine. The initiative investigates exposure pathways via air and skin, internal dose through biomonitoring, and potential health effects among workers in LiB production and recycling. The results can support the assessment of human health and safety within the EU’s Safe and Sustainable by Design (SSbD) framework and contribute to safe and sustainable working environments in the LiB industry. Full article

Fast and scalable lithium-ion cell diagnostics require measurements that are shorter and simpler than full impedance analysis, yet richer and more interpretable than single scalar resistance indicators or raw waveform classification alone. This paper introduces a practical recovery stamp screening method in which short post-load voltage recovery intervals after pulse and pulse–multisine excitation are treated as compact diagnostic events, rather than as single resistance-like indices or parameter identification segments. For this purpose, a constrained two-timescale relaxation model is introduced to retain fast and slower recovery contributions in a low-dimensional form. Using laboratory measurements on two lithium-ion pouch cell families based on nickel manganese cobalt oxide (NMC)/graphite and LiFePO₄/graphite chemistry, each retained load removal event is converted into a signed, current-normalized recovery curve and parameterized by the proposed model. The fitted parameters provide a compact, physics-informed recovery state, while the resampled local waveform preserves transition morphology and short-time relaxation structure that are not fully retained by compact variables alone. These two inputs are evaluated separately and jointly in ordered event sequences under a reference-centered binary screening formulation. The curated dataset comprises 48 original recovery events. Local label-preserving augmentation is applied as training-side regularization, yielding 490 event instances and 230 event sequences. A scalar recovery-amplitude baseline has reached balanced accuracies of 0.833 without and 0.929 with operating context, whereas the best deep learning result is obtained only when fitted variables and waveform are combined. In that setting, TimesNet has reached a median validation balanced accuracy of 0.938. These findings show that post-load polarization recovery contains diagnostically useful information beyond scalar amplitude measures and can support rapid, interpretable reference-deviation screening. Full article