Search Results (52)

The salt flotation of graphite in the presence of lithium nickel manganese cobalt oxide (NMC) was assessed by performing colloidal probe atomic force microscopy (CP-AFM) on sessile gas bubbles and conducting batch flotation tests with model lithium-ion-battery black mass. The modeling of film drainage and detachment during particle–bubble interactions provides insight into the fundamental microprocesses during salt flotation, a special variant of froth flotation. The interfacial properties of particles and gas bubbles were tailored with salt solutions containing sodium chloride and sodium acetate buffer. Graphite particles can attach to gas bubbles under all tested conditions in the range pH 3 to pH 10. The attractive forces for spherical graphite are strongest at high salt concentrations and pH 3. The conditions for the attachment of NMC to gas bubbles were evaluated with simulations using the Stokes–Reynolds–Young–Laplace model for film drainage, under consideration of DLVO forces and a hydrodynamic slip to account for irregularities of the particle surface. CP-AFM measurements in the capillary force regime provide additional parameters for the modeling of salt flotation, such as the force and work of detachment. The contact angles of graphite and NMC particles during retraction and detachment from gas bubbles were obtained from a quasi-equilibrium model using CP-AFM data as input. All CP-AFM experiments and theoretical results suggest that pristine NMC particles do not attach to gas bubbles during flotation, which is confirmed by the low rate of NMC recovery in batch flotation tests. Full article

There is an increasing demand for the development of efficient and sustainable battery recycling processes. Currently, many recycling processes rely on toxic inorganic acids to recover materials from high-value battery chemistries such as lithium nickel manganese cobalt oxides (NMCs) and lithium cobalt oxide (LCOs). However, as cell manufacturers seek more cost-effective battery chemistries, the value of the spent battery value chain is increasingly diluted by chemistries such as lithium iron phosphate (LFPs). These cheaper alternatives present a difficulty when recycling, as current recycling processes are geared towards dealing with high-value chemistries; thus, the current processes become less economical. To date, much research is focused on treating a single battery chemistry; however, often, the feed material entering a battery recycling facility is contaminated with other battery chemistries, e.g., LFP feed contaminated with NMC, LCO, or LMOs. This research aims to selectively leach various battery chemistries out of a mixed feed material with the aid of a green organic acid, namely oxalic acid. When operating at the optimal conditions (2% solids, 0.25 M oxalic acid, natural pH around 1.15, 25 °C, 60 min), this research has proven that oxalic acid can be used to selectively dissolve 95.58% and 93.57% of Li and P, respectively, from a mixed LFP-NMC mixed feed, all while only extracting 12.83% of Fe and 8.43% of Mn, with no Co and Ni being detected in solution. Along with the high degree of selectivity, this research has also demonstrated, through varying the pH, that the selectivity of the leaching system can be altered. It was determined that at pH 0.5 the system dissolved both the NMC and LFP chemistries; at a pH of 1.15, the LFP chemistry (Li and P) was selectively targeted. Finally, at a pH of 4, the NMC chemistry (Ni, Co and Mn) was selectively dissolved. Full article

Lithium-ion batteries (LIBs) are widely used in diverse applications, ranging from portable ones to stationary ones. The appropriate handling of the immense amount of spent batteries has, therefore, become significant. Whether recycled or repurposed for second-life applications, knowing their chemistry type can lead to higher efficiency. In this paper, we propose a novel machine learning-based approach for accurate chemistry identification of the electrode materials in LIBs based on their temperature dynamics under constant current cycling using gated recurrent unit (GRU) networks. Three different chemistry types, namely lithium nickel cobalt aluminium oxide cathode with silicon-doped graphite anode (NCA-GS), nickel cobalt aluminium oxide cathode with graphite anode (NCA-G), and lithium nickel manganese cobalt oxide cathode with graphite anode (NMC-G), were examined under four conditions, 0.2 C charge, 0.2 C discharge, 1 C charge, and 1 C discharge. Experimental results showed that the unique characteristics in the surface temperature measurement during the full charge or discharge of the different chemistry types can accurately carry out the classification task in both experimental setups, where the model is trained on data under different cycling conditions separately and jointly. Furthermore, experimental results show that the proposed approach for chemistry type identification based on temperature dynamics appears to be more universal than voltage characteristics. As the proposed approach has proven to be efficient in the chemistry identification of the electrode materials LIBs in most cases, we believe it can greatly benefit the recycling and second-life application of spent LIBs in real-life applications. Full article

Lithium-ion batteries risk failing when subjected to different abuse tests, resulting in gas and flames. In this study, 5 Ah nickel manganese cobalt oxide (NMC) pouch cells were subjected to external heating; overcharge at rates of 2.5, 5 and 10 A; and nail penetration. Tests were conducted in air and N₂ atmospheres. Additional external heat tests were performed on cells at 5, 25, 50, and 75% SoC and on two, three, and four cell blocks. Gas volumes were calculated, and the gas composition was given for H₂, CO, CO₂, C₂H₄, C₂H₆, CH₄, C₃H₆, and C₃H₈. For tests under an air atmosphere at 100% SoC, the volume of gas varied between abuse methods: 3.9 L (external heat), 6.4 L (overcharge), and 8.9 L (nail penetration). The gas composition was found to predominantly contain H₂, CO₂, and CO for all abuse methods; however, higher concentrations of H₂ and CO were present in tests performed under N₂. External heat tests at different SoCs showed that the gas volume decreased with SoC. Overall, the type of abuse method can have a large effect on the gas volume and composition produced by cell failure. Full article

This paper presents entropy measurements for a large set of commercial Li-ion cells. We present entropy data on full cells with a variety of common Li-ion cell electrode chemistries; graphite, hard carbon, lithium-titanium-oxide (LTO), lithium cobalt-oxide (LCO), nickel manganese cobalt oxides (NMC), nickel cobalt aluminium oxide (NCA), lithium iron-phosphate (LFP), as well as electrodes with mixes of these. All data were collected using an accelerated potentiometric method in steps of approximately 5% State-of-Charge (SoC) across the full SoC window. We observe that the entropy profiles depend on the chemistry of the Li-ion cells, but that they also vary between different commercial cells with the same chemistry. Entropy contributions are quantified with respect to both, their means, positive and negative contributions as well as their SoC variation. In addition, we present how different cyclic ageing temperatures change the entropy profiles for a selected commercial Li-ion cell through ageing. A clear difference in entropy profiles is observed after a capacity loss of 20%. This difference can be attributed to different ageing mechanisms within the Li-ion cells, leading to changes in the balancing of electrodes, as well as changes in the electrode materials. Full article

Achieving accurate battery state of health (SOH) estimation is crucial, but existing methods still face many challenges in terms of data quality, computational efficiency, and cross-scenario generalization capabilities. This study proposes a hybrid deep learning framework incorporating transfer learning to address these challenges. The framework integrates inception depthwise convolution (IDC), channel reduction attention (CRA) mechanism, and staged training strategy to improve the accuracy and generalization ability of SOH estimation. The IDC module of the proposed model is capable of extracting battery degradation time series features from multiple scales while reducing the computational overhead. The CRA module effectively reduces the computational complexity and memory usage of global feature capture by compressing the channel dimensions. A well-designed pre-training/fine-tuning two-stage training strategy achieves accurate cross-scene SOH estimation by utilizing large-scale source-domain data to learn generalized aging features and then uses a small amount of new data to quickly fine-tune the base model. The proposed method is validated using two publicly available datasets, including 54 nickel cobalt manganese oxide (NCM) cells and 16 nickel manganese cobalt oxide (NMC) cells. The experimental results show that the root mean square error (RMSE) of the model on the NCM and NMC datasets is 0.522% and 0.283%, respectively, with a coefficient of determination (R²) not less than 0.98 and mean absolute percentage error (MAPE) of 0.431% and 0.22%, respectively. The proposed method not only achieves high-precision SOH estimation among the same type of batteries but also demonstrates strong generalization ability under different battery chemistries and scenarios. Full article

Lithium nickel manganese cobalt oxides (NMCs) are widely used as cathode materials in commercial batteries. Efforts have been made to enhance battery energy density and stability by adjusting the element ratio. Nickel-rich NMC shows promise due to its high capacity; however, its commercial viability is hindered by severe capacity fade, primarily caused by poor mechanical stability. To address this, understanding the chemo-mechanical behavior of Ni-rich NMC is crucial. The mechanical failure of Ni-rich NMC materials during battery operation has been widely studied through theoretical approaches to identify possible solutions. The elastic properties are key parameters for structural analysis. However, experimental data on NMC materials are scarce due to the inherent difficulty of measuring the properties of electrode active particles at such a small scale. In this study, we employ molecular dynamics (MDs) simulations to investigate the elastic properties of NMC materials with varying compositions (NMC111, NMC532, NMC622, NMC721, and NMC811). Our results reveal that elasticity increases with nickel content, ranging from 200 GPa for NMC111 to 290 GPa for NMC811. We further analyze the contributing factors to this trend by examining the individual components of the elastic properties. The simulation results provide valuable input parameters for theoretical models and continuum simulations, offering insights into strategies for reducing the mechanical instability of Ni-rich NMC materials. Full article

One of the major concerns for battery electric vehicles (BEVs) is the occurrence of thermal runaway (TR), usually of a single cell, and its propagation to adjacent cells in a battery pack. To guarantee sufficient safety for the vehicle occupants, the TR mechanisms must be known and predictable. In this work, we compare thermal runaway scenarios using different initiation protocols (heat–wait–seek, constant heating, nail penetration) and battery chemistries (nickel manganese cobalt oxide, NMC; lithium iron phosphate, LFP; and sodium-ion batteries, SIB) with the cells in a fully charged state. Our goal is to specifically trigger a variety of different possible TR scenarios (internal failure, external hotspot, mechanical damage) with different types of chemistries to obtain reliable data that are subsequently employed for modeling and prediction of the phenomenon. The safety of the tested cells depending on their chemistry can be summarized as LFP > SIB >> NMC. The data of the TR experiments were used as the basis for high-fidelity modeling and predicting of TR phenomena in 3D. The models simulated reaction rates, represented by the typically employed Arrhenius approach. The effects of the investigated TR triggering methods and cell chemistries were represented with sufficient accuracy, enabling the application of the models for the simulation of thermal propagation in battery packs. Full article

The crashworthiness of electric vehicles depends on the response of lithium-ion cells to significant deformation and high strain rates. This study thoroughly explores the mechanical behavior due to damage of lithium-ion battery (LIB) cells, focusing on Lithium Nickel Manganese Cobalt Oxide (NMC) and Lithium Iron Phosphate (LFP) types during both quasi-static indentation and dynamic high-velocity penetration tests. Employing a novel approach, a hemispherical indenter addresses gaps in stress–strain data for pouch cells, considering crucial factors like strain rate/load rate and battery cell type. In the finite element method (FEM) analysis, the mechanical response is investigated in two stages. First, a viscoplastic model is developed in Abaqus/Standard to predict the indentation test. Subsequently, a thermomechanical model is formulated to predict the high-speed-impact penetration test. Considering the high plastic strain rate of the LIB cell, adiabatic heating effects are incorporated into this model, eliminating heat conduction between elements. Addressing a notable discrepancy from prior research, this work explores the substantial reduction in force observed when transitioning from a single cell to a stack of two cells. The study aims to unveil the underlying reasons and provide insights into the mechanical behavior of stacked cells. Full article

Power loss is a limiting factor for batteries and individual cells. The resulting heat generation due to the power loss leads to reduced battery performance and, thus, lower efficiency. These losses are largely due to the internal resistance of the cells. Therefore, it is important to accurately determine the value of the internal resistance of lithium–ion cells. From the literature, it was found that there are three widely used internal resistance-measurement methods (current step method, direct-energy-loss method, and calorimeter measurement), with negligible research on their comparison demonstrating the most efficient method. Henceforth, to find the most optimal method, this research adopts all three methods on a variety of cell chemistries, including Lithium-ion Manganese Oxide (LMO), Lithium Iron Phosphate (LFP), Nickel Manganese Cobalt (NMC), and Lithium Titanium-Oxide (LTO) for different c-rates (1 C, 2 C, and 3 C), with a wide temperature range (from 0 °C to 40 °C). Full article

Electric vehicles (EVs) are becoming increasingly popular, due to their beneficial environmental effects and low operating costs. However, one of the main challenges with EVs is their short battery life. This study presents a comprehensive approach for predicting the Remaining Useful Life (RUL) of Nickel Manganese Cobalt-Lithium Cobalt Oxide (NMC-LCO) batteries. This research utilizes a dataset derived from the Hawaii Natural Energy Institute, encompassing 14 individual batteries subjected to over 1000 cycles under controlled conditions. A multi-step methodology is adopted, starting with data collection and preprocessing, followed by feature selection and outlier elimination. Machine learning models, including XGBoost, BaggingRegressor, LightGBM, CatBoost, and ExtraTreesRegressor, are employed to develop the RUL prediction model. Feature importance analysis aids in identifying critical parameters influencing battery health and lifespan. Statistical evaluations reveal no missing or duplicate data, and outlier removal enhances model accuracy. Notably, XGBoost emerged as the most effective algorithm, providing near-perfect predictions. This research underscores the significance of RUL prediction for enhancing battery lifecycle management, particularly in applications like electric vehicles, ensuring optimal resource utilization, cost efficiency, and environmental sustainability. Full article

Silicon-based, high-energy-density electrodes show severe microstructural degradation due to continuous expansion and contraction upon charging and discharging. This mechanical degradation behaviour affects the cell’s lifetime by changing the microstructure morphology, altering transport parameters, and active volume losses. Since direct experimental observations of mechanical degradation are challenging, we develop a computer simulation approach that is based on real three-dimensional electrode microstructures. By assuming quasi-static cycling and taking into account the mechanical properties of the electrode’s constituents we calculate the heterogeneous deformation and resulting morphological changes. Additionally, we implement an ageing model that allows us to compute a heterogeneously evolving damage field over multiple cycles. From the damage field, we infer the remaining electrode capacity. Using this technique, an anode blend of graphite particles and silicon carbon composite particles (SiC-C) as well as a cathode consisting of Lithium-Nickel-Manganese-Cobalt Oxide with molar ratio of 8:1:1 (NMC811) are studied. In a two-level homogenization approach, we compute, firstly, the effective mechanical properties of silicon composite particles and, secondly, the whole electrode microstructure. By introducing the damage strain ratio, the degradation evolution of the graphite SiC-C anode blend is studied for up to 95 charge-discharge cycles. With this work, we demonstrate an approach to how mechanical damage of battery electrodes can be treated efficiently. This is the basis for a full coupling to electrochemical simulations. Full article

Implementing successful aggregated charging strategies for electric vehicles to participate in the wholesale market requires an accurate battery model that can operate at scale while capturing critical battery dynamics. Existing models either lack precision or pose computational challenges for fleet-level coordination. To our knowledge, most of the literature widely adopts battery models that neglect critical battery polarization dynamics favoring scalability over accuracy, donated as constant power models (CPMs). Thus, this paper proposes a novel linear battery model (LBM) intended specifically for use in aggregated charging strategies. The LBM considers battery dynamics through a linear representation, addressing the limitations of existing models while maintaining scalability. The model dynamic behavior is evaluated for the four commonly used lithium-ion chemistries in EVs: lithium iron phosphate (LFP), nickel manganese cobalt (NMC), lithium manganese oxide (LMO), and nickel cobalt aluminum (NCA). The results showed that the LBM closely matches the high-fidelity Thevenin equivalent circuit model (Th-ECM) with substantially improved accuracy over the CPM, especially at higher charging rates. Finally, a case study was carried out for bidding in the wholesale energy market, which proves the ability of the model to scale. Full article

Research on battery thermal management systems (BTMSs) is particularly significant since the electric vehicle sector is growing in importance and because the batteries that power them have high operating temperature requirements. Among them, heat pipe (HP)-based battery thermal management systems have very high heat transfer performance but fall short in maintaining uniform temperature distribution. This study presented forced air cooling by an axial fan as a method of improving the cooling performance of flat heat pipes coupled with aluminum fins (FHPAFs) and investigated the impact of air velocity on the battery pack’s maximum temperature differential (ΔT_max). All experiments were conducted on lithium nickel manganese cobalt oxide (NMC) pouch battery cells with a 20 Ah capacity in seven series connections at room temperature, under forced and natural convection, at various air velocity values (12.7 m/s, 9.5 m/s, and 6.3 m/s), and with 1C, 2C, 3C, and 4C discharge rates. The results indicated that at the same air velocity, increasing the discharge rate increases the ΔT_max significantly. Forced convection has a higher ΔT_max than natural convection. The ΔT_max was reduced when the air velocity was increased during forced convection. Full article

In our initial study on the high-voltage 5 V cobalt-free spinel LiNi_0.5Mn_1.5O₄ (LNMO) cathode, we discovered a severe delamination issue in the laminates when cycled at a high upper cut-off voltage (UCV) of 4.95 V, especially when a large cell format was used. This delamination problem prompted us to investigate further by studying the transition metal (TM) dissolution mechanism of cobalt-free LNMO cathodes, and as a comparison, some cobalt-containing lithium nickel manganese cobalt oxides (NMC) cathodes, as the leachates from the soaking experiment might be the culprit for the delamination. Unlike other previous reports, we are interested in the intrinsic stability of the cathode in the presence of a baseline Gen2 electrolyte consisting of 1.2 M of LiPF₆ in ethylene carbonate/ethyl methyl carbonate (EC/EMC), similar to a storage condition. The electrode laminates (transition metal oxides, transition metal oxides, TMOs, coated on an Al current collector with a loading level of around 2.5 mAh/cm²) or the TMO powders (pure commercial quality spinel LNMO, NMC, etc.) were stored in the baseline solution, and the transition metal dissolution was studied through nuclear magnetic resonance, such as ¹H NMR, ¹⁹F NMR, scanning electron microscope (SEM), X-ray photoelectron spectroscopy (XPS) and inductively coupled plasma mass spectrometry (ICP-MS). Significant electrolyte decomposition was observed and could be the cause that leads to the TM dissolution of LNMO. To address this TM dissolution, additives were introduced into the baseline electrolyte, effectively alleviating the issue of TM dissolution. The results suggest that the observed delamination is caused by electrolyte decompositions that lead to etching, and additives such as lithium difluorooxalato borate and p-toluenesulfonyl isocyanate can alleviate this issue by forming a firm cathode electrolyte interface. This study provides a new perspective on cell degradation induced by electrode/electrolyte interactions under storage conditions. Full article