Search Results (334)

The increasing demand for lithium-ion batteries (LIBs) highlights the need for efficient recycling strategies to recover critical materials and reduce environmental impact. This study presents an optimized hydrometallurgical process for the recovery of lithium (Li) and iron (Fe) from spent lithium iron phosphate (LiFePO₄ or LFP) battery cathodes through selective leaching and precipitation. A factorial experimental design was employed to model the leaching process and to optimize the leaching parameters and the best efficiency was achieved at Acid Conc. (1.5 N), Agitation (350 rpm), and solid-to-liquid ratio (15%) and achieved Li (80%), Al (0.28%), and Fe (76%) recovery. The intensified conditions (H₂SO₄ 3 N, 350 rpm, 20% solid-to-liquid ratio) outside the factorial domain for potential interest in scale-up were also applied to achieve 90.8% Li and 98.0% Fe co-extraction at room temperature within only 30 min, in contrast to the elevated temperatures (60–95 °C) typically required in the literature. To enhance Fe–Li separation, various precipitation agents were tested; among these, oxalic acid at the stoichiometric dosage achieved 98.1% Fe removal while limiting Li loss to 2%, making it the most effective purification method. By combining mild, room-temperature leaching with highly selective Fe removal, this approach reduces the energy and reagent requirements associated with conventional thermally assisted LFP recycling routes. Overall, this work provides an effective approach for recovering valuable components from spent LIBs, reducing material losses and contributing to the development of circular resource recovery strategies. Full article

This paper presents a model predictive control (MPC)-based energy management strategy for hybrid power systems combining a proton-exchange membrane fuel cell (PEMFC) with a lithium iron phosphate (LFP) battery storage unit for renewable energy applications. The proposed framework optimizes power allocation between the two sources while respecting operational constraints, including current limits, power balance requirements, and state-of-charge (SOC) bounds with soft constraints to prevent overcharging and deep discharging. Unlike conventional rule-based approaches, the MPC formulation employs a quadratic cost function with tunable weighting factors that enable flexible prioritization of either fuel cell conservation or battery lifetime extension. Accurate yet computationally efficient models are developed for both components: an equivalent circuit model for the LFP battery and a theoretical electrochemical model for the PEMFC. The performance of the proposed strategy is validated through comprehensive simulations under realistic renewable generation and load profiles. Five case studies are examined, each representing different operational scenarios characterized by varying initial SOC conditions and component prioritization weights. The results demonstrate that the MPC-based approach effectively manages power distribution, maintains SOC within safe operating ranges, and adapts to changing system conditions. Quantitative analysis shows that the tunable weighting strategy successfully limits high-current events, reducing high-current operation and potentially mitigating current-related degradations. The proposed framework offers a scalable and flexible solution for improving the reliability of hybrid energy storage in modern renewable grids. Full article

To improve the inherently low electronic and ionic conductivity of lithium iron phosphate (LFP) cathode materials, the synergistic modification of Al doping and carbon coating has been proven to be an effective strategy. However, the doping effects of different aluminum (Al) sources have not been systematically compared, and the mechanism of the synergistic effect between the characteristics of the Al source and the synthesis process remains poorly understood. To address this, the present study systematically investigated the effects of three Al sources on the structure and electrochemical performance of LFP/C composites under two sintering processes: static and dynamic. Phase and microstructure characterizations confirmed the successful doping of Al³⁺ and the formation of an effective carbon coating. Electrochemical tests indicated that the choice of Al source and sintering process was strongly coupled: in the dynamic fluidized-bed process, which is highly characterized by efficient mass and heat transfer, Al(OH)₃, due to its lower thermal decomposition temperature and the release of active H₂O, promoted uniform Al³⁺ doping and optimized the quality of the carbon coating, thereby achieving the best overall performance. By contrast, under the sluggish reaction kinetics of static sintering, the chemically stable Al₂O₃ achieved ordered doping through slow solid-state diffusion, demonstrating the best cycling stability. In both processes, the overly stable AlPO₄ failed to release Al³⁺ effectively, resulting in limited performance improvement. This work reveals the key principle that the intrinsic reactivity of the Al source must be matched with the kinetics of the sintering process, deepens mechanistic understanding of the doping modification, and provides clear experimental evidence for the selection of the optimal Al source under different synthesis processes. Full article

Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LiFePO₄) batteries is challenging because the voltage feedback used for correction does not provide constant SOC-related information under different operating conditions. Conventional extended Kalman filters (EKFs) usually apply measurement correction based on predefined statistical assumptions, while overlooking variations in voltage-domain observability and innovation reliability. This paper proposes a hybrid estimation framework, termed observability- and innovation–reliability-regulated EKF with reliability-scaled residual learning (OIR-EKF-RSRL). The proposed method retains a first-order RC model and EKF as the physical estimation backbone, while regulating voltage correction according to local OCV-SOC sensitivity and normalized innovation reliability. A reliability-scaled residual learning module is further introduced after physical filtering to compensate for remaining SOC deviations rather than directly predicting SOC. The learned residual correction is modulated by a reliability-dependent scaling coefficient before fusion with the OIR-EKF estimate, after which the final SOC estimate is constrained to the physical interval [0, 1]. The framework is evaluated using the CALCE A123 LiFePO₄ dataset under a frozen temperature-disjoint train–validation–holdout protocol. On the independent holdout set, OIR-EKF-RSRL reduces the RMSE from 3.331 percentage points for the conventional EKF to 2.942 percentage points. The results demonstrate that reliability-aware measurement utilization and reliability-scaled residual compensation provide an interpretable solution for LiFePO₄ SOC estimation under varying voltage-information quality. Full article

An integrated model is presented to estimate the energy consumption of a Formula SAE–type electric vehicle by combining MATLAB/Simulink simulations with real operational data from the Cotopaxi kart circuit. The implemented subsystem incorporates vehicle dynamics, speed, and grade profiles, and it enables the calculation of tractive energy across different competition scenarios. The L3 cycle (maximum speed 90.91 km/h, distance 16.67 km) proved the most demanding, with a tractive energy consumption of 1389.10 Wh, mechanical losses of 630.89 Wh, and a useful net energy of 758.21 Wh. Rolling resistance and inertia accounted for 21.33% and 56.29% of the consumption, respectively, highlighting the influence of acceleration/deceleration dynamics. All cycles were dominated by active phases, with 0% stops and cruising time < 0.21%, validating the model for high-demand conditions. The technical feasibility of second-life Lithium Iron Phosphate (LFP) cells for the battery pack was also confirmed: a 30s2p configuration using 100 Ah cells meets the peak energy demand of 18.30 kWh while maintaining adequate operational margin and a safe discharge C-rate. Structural simulation in ANSYS enabled optimization of mass and stiffness, reducing overall energy demand. This multidisciplinary approach provides a quantitative basis for battery sizing and energy management strategies in competitive electric vehicle applications. Full article

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic impacts and supply chain vulnerabilities. This study presents a comprehensive cradle-to-gate environmental life cycle assessment (LCA), life cycle costing (LCC), and semi-quantitative social assessment of alternative battery housing materials and battery cell architectures. To achieve a functionally accurate comparison, alternative materials, including a novel recyclable thermoplastic acrylic sheet molding compound (SMC), commercial thermoset SMCs, aluminum (AlMg₃), and stainless steel, are evaluated using an analytical stiffness- and strength-equivalent methodology across three real-world geometric demonstrators. Simultaneously, lithium iron phosphate (LFP) liquid electrolyte prismatic cells and solid-state polymer pouch cells are assessed. Material-level results indicate that, while aluminum minimizes the structural mass, primary aluminum manufacturing exhibits the highest global warming potential and processing costs. Conversely, Polytec SMC and Elium SMC achieve the lowest environmental impacts alongside competitive total production costs. At the cell level, prismatic LFP architectures display superior environmental performance compared to solid-state pouch cells, which suffer from energy-intensive processing and lower volumetric capacity normalization. Demonstrator-level aggregation reveals that the electrochemical cells heavily dominate the environmental and economic footprint of the complete assembly, with the housing accounting for less than 5% of the total global warming potential (GWP) and 1% of the total costs. The social assessment reveals moderate and comparable performance across all systems, with slight advantages for thermoplastic composite-based configurations in terms of circularity potential and innovation perception. Overall, the study highlights the critical importance of the cell architecture and manufacturing processes in determining battery system sustainability, while demonstrating the relevance of lightweight composite housings in reducing the structural mass with a minimal environmental penalty. Full article

Large-capacity lithium iron phosphate (LFP) batteries dominate energy storage systems, but their degradation characteristics differ from small-capacity cells. Most existing life prediction methods require complete voltage–current time-series data, which is hard to obtain in practical operation. This paper proposes two life prediction methods: a data-driven method and an empirical-data hybrid method. The data-driven method adopts Summed Voltage Falloff (SVF) extracted from partial voltage segments as the health indicator, which removes the dependence on full charge–discharge waveform data and enhances engineering practicability. It uses a unified numerical fitting framework with Gaussian process regression (GPR) residual correction, with tailored fitting strategies for 320 Ah and 298 Ah battery datasets. The hybrid method integrates the Arrhenius model with Kalman filtering for closed-loop online prediction correction. Both methods are validated using 281 cycles of 320 Ah battery data, and the data-driven method is further verified with 150 cycles of 298 Ah battery data. Results show that the data-driven method performs better with limited data, suitable for offline one-time inspection scenarios; the hybrid model achieves higher accuracy with sufficient data, applicable to long-term online remaining useful life monitoring. The data-driven method yields a worst-case cycle life of 3304 cycles at 2σ confidence level, and the hybrid model maintains error below 5% when forecasting 1000 cycles with 200 cycles of training data. Full article

Tracking capacity fade and predicting the lifespan of Lithium Iron Phosphate (LiFePO₄) batteries under calendar aging are crucial for the reliability of Battery Energy Storage Systems (BESSs) in tropical regions. Conventional empirical models often rely on static environmental averages and neglect coupled thermal–hygroscopic dynamics. To address these limitations, this paper introduces a multi-physics coupled state-space-based continuous integral model for battery degradation under dynamic tropical boundary conditions. The primary novelty of this research lies in the development of a continuous-time multi-physics state-space degradation model that explicitly captures the interconnected interactions between temperature, humidity, and State of Charge (SoC) under dynamically varying tropical microclimates. Calendar aging tests were conducted for 180 days inside an environmental test chamber under tropical microclimate conditions (average of 29.91 °C, RH of 77.26%), with reference performance tests executed at a low C-rate of C/20 to extract static electrochemical capacity. Parameter identification using an Ordinary Least Squares (OLS) solver demonstrates high model fitting, with R-squared values ranging from 0.8229 to 0.9429. Extrapolation results provide realistic end-of-life projections between 8.9 and 59.8 years and successfully identify the critical physical transition points P1 and P2 at the Solid Electrolyte Interphase (SEI) layer. Overall, this research provides a prognostic instrument for optimizing the operational management of utility-scale BESS in tropical climates. Full article

The rapid expansion of electric vehicles is generating large volumes of spent lithium iron phosphate (LFP) batteries, yet the environmental performance of closed-loop regeneration under industrial conditions remains insufficiently quantified. Here we develop a life cycle assessment of a closed-loop recycling–regeneration pathway using factory-level inventory data from an integrated plant, benchmarking 1 kg of regenerated LFP cathode-active material (CAM) at the plant gate against virgin LFP CAM (ecoinvent v3.10; ReCiPe 2016 Midpoint). Relative to virgin production, the closed-loop route reduces global warming potential (GWP100) by 7.73% (from 6.59 to 6.08 kg CO₂-eq kg⁻¹ CAM), fossil fuel potential (FFP) by 3.80%, surplus ore potential (SOP) by 97.57%, and carcinogenic human toxicity (HTPc) by 36.72%—a clear but heterogeneous advantage, large for mineral resources and modest for climate. Iron phosphate and lithium carbonate recovery dominate the burdens, with H₂O₂ being the largest single GWP100 contributor (23.7%) and the most sensitive inventory parameter, while the SOP advantage is highly robust. Grid-decarbonization scenarios widen the GWP100 reduction to 22.4% under near-zero-carbon electricity. The carbon competitiveness of closed-loop LFP regeneration is therefore governed by the balance between avoided virgin-material burdens and reagent- and energy-intensive recovery operations. Full article

This paper proposes an SOH estimation method that fuses mechanical–electrical–thermal multi-modal features by introducing expansion force monitoring. Aging tests on 16 prismatic 530 Ah LiFePO₄ batteries from two brands are conducted at 25 and 45 °C. Each full cycle is divided into charge, post-charge rest, discharge, and post-discharge rest, with SOH defined by the capacity ratio. From cycle-level data, 37 candidate features are extracted and cleaned using local median and median absolute deviation. Using only training cells, Spearman correlation eliminates highly redundant features, and 12 key features are retained via internal validation. Under 4-fold cross-validation with complete battery grouping, Random Forest, XGBoost, LightGBM, and LSTM are compared. LightGBM achieves the best performance with an average MAE of 0.0032, RMSE of 0.0037, and R² of 91.36%. Ablation shows multi-modal fusion outperforms single-type features; five-category fused features reduce RMSE by ~75.57% versus electrical-only features. Removing expansion force features increases RMSE to 0.0064 and drops R² to 75.66%. These findings confirm that expansion force supplies critical mechanical degradation information, significantly improving SOH estimation for large-capacity energy storage batteries. Full article

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

Early lithium-ion battery lifetime prediction is difficult because only a small fraction of a cell’s lifetime is observed when a prediction is needed. We evaluate a transparent, validation-tuned combination of a supervised early-cycle predictor and a nearest-neighbor retrieval estimate built from similar historical cells. The method is assessed against supervised-only and retrieval-only baselines over 20 repeated cell-wise splits. On the Severson/Toyota Research Institute/Massachusetts Institute of Technology (Severson/TRI/MIT) dataset (124 lithium iron phosphate/graphite cells), fusion reduces the mean absolute error (MAE) of end-of-life (EOL) prediction by 10.08 cycles at 10 observed cycles and 11.90 cycles at 20 observed cycles relative to the validation-selected supervised baseline; the confidence intervals for these gains exclude zero, whereas gains at 50 and 100 cycles are unsupported. Retrieval-only prediction is competitive in the shortest windows. In a targeted 40-split external confirmation on BatteryLife-XJTU (Xi’an Jiaotong University) at 50 observed cycles, fusion reduces mean MAE from 34.11 to 28.02 cycles (paired bootstrap 95% confidence interval for the gain: 0.47–11.41 cycles; one-sided Wilcoxon $p=0.007$). The results support retrieval as an interpretable complement to supervised prediction in compatible weak-signal regimes, not as a universal replacement for supervised models. Full article

The inferior electronic conductivity, sluggish bulk Li⁺ transport, and Jahn–Teller distortion intrinsic to Mn³⁺ collectively impede the practical application of LiMn_0.6Fe_0.4PO₄ (LMFP) as a high-performance cathode for lithium-ion batteries. Herein, a series of W-doped Li(Mn_0.6Fe_0.4)_1−xW_xPO₄/C (x = 0, 0.005, 0.010, 0.015) cathode materials were synthesized via spray-drying combined with carbothermal reduction. Rietveld refinement indicated decreases in the fitted lattice parameters and unit-cell volume with increasing nominal W content, and no crystalline secondary phases were detected within the laboratory XRD detection limit. Although the structural evolution is consistent with W incorporation, direct determination of the occupation site requires further local structural characterization. X-ray photoelectron spectroscopy indicated that the detectable near-surface W species are predominantly present as W⁶⁺, and the semi-quantitative Mn 2p peak-area fitting showed that the fitted relative Mn³⁺ contribution decreased from 70.4% in LMFP-0 to 58.3% in LMFP-2. The optimal composition (LMFP-2, x = 0.010) delivers an initial discharge capacity of 160.2 mAh g⁻¹ at 0.1 C, retains 98.1% capacity after 100 cycles at 1 C, and achieves 126.3 mAh g⁻¹ at 5 C. Electrochemical impedance spectroscopy reveals that LMFP-2 possesses the lowest charge-transfer resistance (195.4 Ω) and the highest Li⁺ diffusion coefficient (5.3 × 10⁻¹⁵ cm² s⁻¹). These improvements may be attributed to the synergistic effects of enhanced bulk electronic conductivity, accelerated Li⁺ diffusion kinetics, and improved structural stability induced by moderate W incorporation. This work establishes W doping as a viable compositional engineering strategy for olivine-based cathode materials. 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