Search Results (5,172)

The study of pore structure regulation methods has always been a central focus in enhancing the capacitance performance of porous carbon electrodes in lithium-ion capacitors (LICs). This study proposes a novel approach for the synergistic regulation of the pore structure in porous carbon using phenol-formaldehyde (PF) resin and boric acid (BA). PF and BA are initially dissolved and adsorbed onto porous carbon, followed by hydrothermal treatment and subsequent heat treatment in a N₂ atmosphere to obtain the porous carbon materials. The results reveal that adding BA alone has almost no influence on the pore structure, whereas adding PF alone significantly increases the micropores. Furthermore, the simultaneous addition of PF and BA demonstrates a clear synergistic effect. The CO₂ and H₂O released during the PF pyrolysis contribute to the development of ultramicropores. At the same time, BA facilitates the N₂ activation reaction of carbon, enlarging the small mesopores and aiding their transformation into bottlenecked structures. The resulting porous carbon demonstrates an impressive capacitance of 144 F·g⁻¹ at 1 A·g⁻¹ and a capacity retention of 19.44% at 20 A·g⁻¹. This mechanism of B-catalyzed N₂-enhanced mesopore formation provides a new avenue for preparing porous carbon materials. This type of porous carbon exhibits promising potential for applications in Li-S battery cathode materials and as catalyst supports. Full article

This study investigates the properties of LiCoO₂ coatings as cathodes for lithium-ion batteries, focusing on the effects of annealing on their structural, morphological, chemical, vibrational, and electrochemical characteristics. The LiCoO₂ coatings were deposited on silicon and glass substrates using RF magnetron sputtering at 100 W and subsequently annealed at 600 °C for 1 h. The films were characterized before and after annealing using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and electrochemical impedance spectroscopy (EIS). Annealing improved the crystallinity of LiCoO₂, which is critical for enhancing lithium-ion diffusion. Furthermore, an XPS analysis revealed a layered structure with a Li-rich outer layer and a Co-rich underlayer, indicating a more uniform distribution of Li and Co, along with increased oxygen content. Additionally, the annealing process refined the microstructure of the LiCoO₂ coating, positively impacting its electrochemical performance. A comparative analysis of cyclic voltammetry (CV) and galvanostatic charge/discharge (GCD) results demonstrated a significant improvement in the charge/discharge capacity post-annealing. This study successfully highlights the beneficial effects of annealing on LiCoO₂ thin-film cathodes, offering valuable insights for developing more efficient and sustainable lithium-ion batteries through sputter-deposition processes. Full article

This review paper explores the emerging field of conversion cathode materials, which hold significant promises for advancing the performance of lithium-ion (LIBs) and lithium–sulfur batteries (LSBs). Traditional cathode materials of LIBs, such as lithium cobalt oxide, have reached their limits in terms of energy density and capacity, driving the search for alternatives that can meet the increasing demands of modern technology, including electric vehicles and renewable energy systems. Conversion cathodes operate through a mechanism involving complete redox reactions, transforming into different phases, which enables the storage of more lithium ions and results in higher theoretical capacities compared to conventional intercalation materials. This study examines various conversion materials, including metal oxides, sulfides, and fluorides, highlighting their potential to significantly enhance energy density. Despite their advantages, conversion cathodes face numerous challenges, such as poor conductivity, significant volume changes during cycling, and issues with reversibility and stability. This review discusses current nanoengineering strategies employed to address these challenges, including nano structuring, composite formulation, and electrolyte optimization. By assessing recent research and developments in conversion cathode technology, this paper aims to provide a comprehensive overview of their potential to revolutionize lithium-ion batteries and contribute to the future of energy storage solutions. Full article

Niobium pentoxide (Nb₂O₅) is a promising anode candidate for lithium-ion batteries due to its high theoretical capacity, excellent rate capability, and safe working potential. However, its inherent low conductivity limits its practical application in fast-charging scenarios. In this work, we develop an ultrathin carbon-coated two-dimensional T-Nb₂O₅ nanosheet composite (T-Nb₂O₅@UTC) through a facile solvothermal reaction and subsequent CVD acetylene decomposition. This unique design integrates a two-dimensional nanosheet structure with an ultrathin carbon layer, significantly enhancing electronic conductivity, reducing ion diffusion pathways, and preserving structural integrity during cycling. The T-Nb₂O₅@UTC electrode demonstrates an impressive specific capacity of 214.7 mAh g⁻¹ at a current density of 0.1 A g⁻¹, maintaining 117.9 mAh g⁻¹ at 5 A g⁻¹, much outperforming the bare T-Nb₂O₅ (179.6 mAh g⁻¹ at 0.1 A g⁻¹ and 62.9 mAh g⁻¹ at 5 A g⁻¹). It exhibits outstanding cyclic stability, retaining a capacity of 87.9% after 200 cycles at 0.1 A g⁻¹ and 83.7% after 1000 cycles at 1 A g⁻¹. In a full-cell configuration, the assembled T-Nb₂O₅@UTC||LiFePO₄ battery exhibits a desirable specific capacity of 186.2 mAh g⁻¹ at 0.1 A g⁻¹ and only a 1.5% capacity decay after 120 cycles. This work underscores a nanostructure engineering strategy for enhancing the electrochemical performance of Nb₂O₅-based anodes toward high-energy-density and fast-charging applications. Full article

In this work, we employed an electrochemical impedance spectroscopy analysis of commercial Li-ion Panasonic NCR18650B cells in order to monitor their cycle life performance and the influence of the C-rate on the charge/discharge processes. By applying a fast charge rate of 1.5 C, we investigated their speed degradation within three distinct discharge rates, namely, 0.5 C, 1 C, and 1.5 C. In our first approach, we assessed the dynamics of the lithium-ion transport processes, as well as their dependence on discharge rates, with the aim of understanding how their performance correlates with usage conditions. We observed that, as the discharge current increases while the number of cycles decreases, the ohmic resistance in the aged state reduces. Moreover, the charge transfer resistance is not affected by the discharge current, as the values are inversely proportional to the current rate, but mostly by the number of cycles. By performing a state of health analysis of Li-ion batteries with different C-rates until they were completely discharged, we offer a clear indication of how much of the battery’s lifetime available energy was consumed and how much was left, anticipating further issues or when the battery needed replacing. Starting at 60% state of health, the battery degradation has a steeper increase at 0.5 C and 1 C, respectively, while for a deep 1.5 C discharge, it only increases when the battery charge rate can no longer be sustained. Finally, the resonance frequency results highlight a fast increase toward the end of life for 0.5 C and 1 C, which is directly correlated with the above results, as a potentiostatic electrochemical impedance spectroscopy sequence was applied every fourth charge/discharge cycle. When applied at 1.5 C, the linear trend is much more pronounced, similar to the state of health results. Full article

Doping lithium cobalt oxide (LiCoO₂) cathode materials is an effective strategy for mitigating the detrimental phase transitions that occur at high voltages. A deep understanding of the relationships between cycle capacity and the design elements of doped LiCoO₂ is critical for overcoming the existing research limitations. The key lies in constructing a robust and interpretable mapping model between data and performance. In this study, we analyze the correlations between the features and cycle capacity of 158 different element-doped LiCoO₂ systems by using five advanced machine learning algorithms. First, we conducted a feature election to reduce model overfitting through a combined approach of mechanistic analysis and Pearson correlation analysis. Second, the experimental results revealed that RF and XGBoost are the two best-performing models for data fitting. Specifically, the RF and XGBoost models have the highest fitting performance for IC and EC prediction, with R² values of 0.8882 and 0.8318, respectively. Experiments focusing on ion electronegativity design verified the effectiveness of the optimal combined model. We demonstrate the benefits of machine learning models in uncovering the core elements of complex doped LiCoO₂ formulation design. Furthermore, these combined models can be employed to search for materials with superior electrochemical performance and processing conditions. In the future, we aim to develop more accurate and efficient machine learning algorithms to explore the microscopic mechanisms affecting doped layered oxide cathode material design, thereby establishing new paradigms for the research of high-performance cathode materials for lithium batteries. Full article

Battery cell voltage is an important evaluation index for electric vehicle condition estimation and one of the main monitoring parameters of the battery management system, and accurate voltage prediction is crucial for electric vehicle battery failure warning. Therefore, this paper proposes a novel hybrid gated recurrent unit and long short-term memory (GRU-LSTM) neural network to predict electric vehicle lithium-ion battery cell voltage. Firstly, Pearson coefficient correlation analysis is carried out to determine the input parameters of the neural network by analyzing the influence factors of the voltage parameters, and the hyperparameters of the neural network are determined through cross-validation to construct the lithium-ion battery single-unit voltage prediction model based on GRU-LSTM. Secondly, the voltage prediction accuracy and robustness of the GRU-LSTM model are verified by training the historical data of real vehicles in spring, summer, fall, and winter, combined with four different error indicators. Finally, the feasibility of the proposed method is verified by designing hierarchical warning rules based on the prediction data to realize the accurate warning of multiple voltage anomalies. Full article

While the effect of ageing has been thoroughly analysed, to improve the cycle life of lithium-ion batteries, its impact on safety in case of a mechanical loading is still a new field of research. It has to be found out how mechanical properties, such as the tolerable failure force or deformation, change over the operational lifetime of a battery. To answer this question, mechanical abuse tests were carried out with pouch cells used in recent electric vehicles in a fresh state and after usage over 160.000 km. These tests were complemented with a detailed component level analysis, in order to identify mechanisms that lead to changed cell behaviour. For the analysed aged cells, a significantly different mechanical response was observed in comparison with the respective fresh samples. The tolerable force was severely reduced (up to −27%), accompanied by a notable reduction in the allowable deformation level (up to −15%) prior to failure, making the aged cells clearly more safety critical. Based on the subsequent component tests, the predominant mechanism for this different behaviour was concluded to be particle cracking in the cathode active material. The found results are partly in contrast with the (few) other already published works. It is, however, unclear if this difference is rooted in different cell chemistries or types, or another battery state resulting from varying ageing procedures. This underlies the importance of further investigations in this research field to close the apparent gap of knowledge. Full article

As a multi-electron system material, the excellent capacity and environmentally benign properties of Li₂FeTiO₄ cathodes make them attractive for lithium-ion batteries. Nevertheless, their electrochemical performance has been hampered by poor conductivity and limited ion transport. In this work, the synthesis of Mg-doped Li₂Mg_xFe_1−xTiO₄ (LiFT-Mgx, x = 0, 0.01, 0.03, 0.05) cathode materials was successfully achieved. We observed significant gains in interlayer spacing, ionic conductivity, and kinetics. Hence, the sample of the LiFT-Mg0.03 cathode demonstrated charming initial capacity (112.1 mAh g⁻¹, 0.05 C), stability (85.0%, 30 cycles), and rate capability (96.5 mAh g⁻¹, 85.9%). This research provided precious insights into lithium storage with exceptional long-term stability and has the potential to drive the development of next-generation energy storage technologies. Full article

In Japan, local governments implore residents to remove the batteries from small-sized electronics before recycling them, but some products still contain lithium-ion batteries. These residual batteries may cause fires, resulting in serious injuries or property damage. Explosive materials such as mobile batteries (such as power banks) have been identified in fire investigations. Therefore, these fire-causing items should be detected and separated regardless of whether small-sized electronics recycling or other recycling processes are in use. This study focuses on the automatic detection of fire-causing items using deep learning in recycling small-sized electronic products. Mobile batteries were chosen as the first target of this approach. In this study, MATLAB R2024b was applied to construct the You Only Look Once version 4 deep learning algorithm. The model was trained to enable the detection of mobile batteries. The results show that the model’s average precision value reached 0.996. Then, the target was expanded to three categories of fire-causing items, including mobile batteries, heated tobacco (electronic cigarettes), and smartphones. Furthermore, real-time object detection on videos using the trained detector was carried out. The trained detector was able to detect all the target products accurately. In conclusion, deep learning technologies show significant promise as a method for safe and high-quality recycling. Full article

This paper proposes the integration and operation of lithium-ion battery energy storage systems (ESS) in active distribution networks with high penetration of distributed generation based on renewable energy. The goal is to minimize total system costs, including energy purchasing at the substation node, as well as ESS integration, maintenance, and replacement costs over a 20-year planning horizon. The proposed master–slave methodology uses the Salp Swarm Optimization Algorithm to determine ESS location, technology, and daily operation schemes, combined with a successive approximation power flow to compute the objective function value and enforce constraints. This approach employs a discrete–continuous encoding, reducing processing times and increasing the likelihood of finding the global optimum. Validated on a 33-node test system adapted to Medellín, Colombia, the methodology outperformed five metaheuristic algorithms, achieving the highest annual savings (USD 16,605.77), the lowest average cost (USD 2,964,139.99), and the fastest processing time (345.71 s). The results demonstrate that the proposed methodology enables network operators to reduce distribution network costs effectively, offering high repeatability, solution quality, and computational efficiency. 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

An efficient energy management strategy (EMS) is crucial for the energy-saving and emission-reduction effects of electric vehicles. Research on deep reinforcement learning (DRL)-driven energy management systems (EMSs) has made significant strides in the global automotive industry. However, most scholars study only the impact of a single DRL algorithm on EMS performance, ignoring the potential improvement in optimization objectives that different DRL algorithms can offer under the same benchmark. This paper focuses on the control strategy of hybrid energy storage systems (HESSs) comprising lithium-ion batteries and ultracapacitors. Firstly, an equivalent model of the HESS is established based on dynamic experiments. Secondly, a regulated decision-making framework is constructed by uniformly setting the action space, state space, reward function, and hyperparameters of the agent for different DRL algorithms. To compare the control performances of the HESS under various EMSs, the regulation properties are analyzed with the standard driving cycle condition. Finally, the simulation results indicate that the EMS powered by a deep Q network (DQN) markedly diminishes the detrimental impact of peak current on the battery. Furthermore, the EMS based on a deep deterministic policy gradient (DDPG) reduces energy loss by 28.3%, and the economic efficiency of the EMS based on dynamic programming (DP) is improved to 0.7%. Full article

This article presents an electro-thermal model of a prismatic lithium-ion cell, integrating physics-based models for capacity and resistance estimation. A 100 Ah prismatic cell with LFP-based chemistry was selected for analysis. A comprehensive experimental campaign was conducted to determine electrical parameters and assess their dependencies on temperature and C-rate. Capacity tests were conducted to characterize the cell’s capacity, while an OCV test was used to evaluate its open circuit voltage. Additionally, Hybrid Pulse Power Characterization tests were performed to determine the cell’s internal resistive-capacitive parameters. To describe the temperature dependence of the cell’s capacity, a physics-based Galushkin model is proposed. An Arrhenius model is used to represent the temperature dependence of resistances. The integration of physics-based models significantly reduces the required test matrix for model calibration, as temperature-dependent behavior is effectively predicted. The electrical response is represented using a first-order equivalent circuit model, while thermal behavior is described through a nodal network thermal model. Model validation was conducted under real driving emissions cycles at various temperatures, achieving a root mean square error below 1% in all cases. Furthermore, a comparative study of different cell cooling strategies is presented to identify the most effective approach for temperature control during ultra-fast charging. The results show that side cooling achieves a 36% lower temperature at the end of the charging process compared to base cooling. Full article

Lithium-ion batteries are one of the critical components in electric vehicles (EVs) and play an important role in green energy transportation. In this paper, lithium-ion batteries are reviewed from the perspective of battery materials, the characteristics of lithium-ion batteries with different cathode and anode mediums, and their commercial values in the field of electric vehicles. Representative products, including blade battery and Tesla 4680 cells, are inspected. Moreover, the results of commercial application of lithium-ion batteries in electric vehicles are summarized. Furthermore, cutting-edge technologies of lithium-ion batteries are discussed, including electrolyte technology, high-energy-density in situ polymerization technology, and pouch batteries. Finally, the latest EV battery technology development is looked over, including challenges and future development directions. Full article