Search Results (6,538)

This study presents an integrated process for the recovery and purification of lithium hydroxide (LiOH) from lithium sulfate (Li₂SO₄) solution obtained by sulfuric acid leaching of spent crucibles used for producing the cathodes of LIBs. The recovered leachate contains considerable concentrations of metallic impurities, including Na, K, Mg, Ca, Al, and Ni, which hinder the direct production of high-purity LiOH. To overcome this limitation, a pretreatment step combining cation- and anion-exchange resins was introduced to control impurity levels and condition the solution prior to conversion. Under the optimized ion-exchange condition of 10 g cation-exchange resin and 50 g anion-exchange resin, the solution pH was adjusted to 6–7, resulting in effective impurity removal through combined ion-exchange and solution-conditioning effects. More than 90% of Al was removed, while Mg, Ca, Na, K, and Ni were removed by approximately 70–75%. After purification, LiOH was produced through a double-displacement conversion reaction using Ba(OH)₂. The results showed that the reaction temperature and the [OH]:[Li] molar ratio were the key parameters governing the sulfate-removal-based apparent conversion efficiency and filtrate-based LiOH purity. Excess OH⁻ promoted the formation of dissolved and complexed species, thereby lowering the purity of the LiOH-containing filtrate. In contrast, the optimum condition was identified at 70 °C and an [OH]:[Li] molar ratio of 1:1, under which SO₄²⁻ was effectively removed as solid BaSO₄. Under these conditions, the sulfate-removal-based apparent conversion efficiency reached 91.91%, and the filtrate-based LiOH purity was 98.84%. X-ray diffraction analysis confirmed the coexistence of LiOH·H₂O and LiOH phases in the final recovered product, whereas the precipitate was identified as single-phase BaSO₄, indicating effective sulfate removal. Overall, this study demonstrates the feasibility of producing high-purity LiOH from sulfation-derived Li₂SO₄ leachate through a sequential process consisting of impurity removal, conversion, and drying. The findings provide fundamental process data for the design of lithium recovery and purification routes using spent cathode crucibles as secondary lithium resources. Full article

With the widespread application of lithium-ion battery energy storage stations, thermal runaway (TR) of energy storage batteries has evolved into a safety issue that cannot be overlooked. To prevent the propagation of thermal runaway, this study proposes a thermal runaway protection strategy for prismatic battery modules based on the sodium acetate trihydrate-expanded graphite (SAT-EG), aerogel felt (AEGF) and liquid cooling. The study also investigates the impact of factors such as the thickness of the SAT-EG, the thickness of the AEGF, and the area of the AEGF on the protection performance. The results show that compared with the conventional paraffin-expanded graphite (PA-EG), SAT-EG can block the propagation of thermal runaway, but the maximum temperature of adjacent batteries still approaches T2 (T2 denotes the battery thermal runaway triggering temperature). After introducing AEGF to form a sandwich structure, the maximum temperature of adjacent batteries can be effectively controlled below T1 (T1 denotes the temperature at which heat generation from battery side reactions intensifies). However, the utilization rate of SAT-EG is relatively low, and the thermal runaway trigger time of the thermal runaway battery is advanced. By reducing the AEGF area, the overall utilization rate of SAT-EG can be effectively improved, and the thermal runaway trigger time of the thermal runaway battery can be significantly delayed, gaining time for the detection and handling of thermal runaway and ensuring the safety of energy storage power stations. Full article

The need for the rapid advancement of lithium-based energy storage technologies continues to outpace progress in materials development and manufacturing, creating a widening gap between laboratory-scale innovation and industrial deployment. There is a need to examine the key materials and processing challenges that limit the performance, cost-effectiveness, and sustainability of next-generation lithium batteries. For material considerations, many commonly used electrodes face issues of volumetric expansion and performance degradation over charging cycles. To address these issues, binders are a crucial component to consider as they adhere active materials to the electrodes, and their structure can be altered to mitigate undesirable effects from these components. Hence, the selection and exploration of alternative binders are becoming increasingly important in the pursuit of longer-lasting and safer Li-batteries. From a manufacturing perspective, current production lines rely on multistep, energy-intensive processes, e.g., from slurry-mixing to cell assembly, that elevate costs and complicate scale-up. Emerging chemistries incorporating nanomaterials or solid-state components face additional barriers related to yield, process control, and defect management, all of which can exacerbate safety risks related to processing during production and thermal runaway in produced batteries. End-of-life considerations, including disassembly, recycling, and the safe handling of toxic materials, further contribute to the technological and logistical complexity of large-scale deployment. The field is moving toward sustainable material alternatives, more efficient and adaptive manufacturing routes, and advanced technologies such as solid-state electrolytes and nanostructured electrodes. Together, these developments provide a roadmap for overcoming current bottlenecks and enabling the next generation of high-performance, safe, and sustainable lithium battery technologies. This review examines the progress made in finding alternative materials and synthesis methods for the optimization of lithium battery cells, with a focus on the development of novel binders, slurry synthesis and manufacturing framework. In addition, the advantages and limitations of the alternative binder materials and processes are also explored, with a focus on scalability for manufacturing, safety concerns, sustainability and end-of-life challenges. Full article

The reduction in driving range during winter remains a major barrier to the widespread adoption of battery electric buses (BEBs), as battery performance degradation and increased Heating, Ventilation and Air Conditioning (HVAC) energy demand significantly raise total energy consumption. This study investigates the use of proton exchange membrane fuel cells (PEMFCs) as auxiliary power units for range-extended electric buses (FC-REEBs) under low-temperature conditions to address this challenge. A comprehensive dynamic model was developed in MATLAB/Simulink 2025a version, integrating a fuel cell system, lithium-ion battery, power conversion unit, vehicle dynamics, and cabin thermal model. The model was evaluated under the World Harmonized Vehicle Cycle (WHVC) to compare three fuel cell operation strategies defined by fuel cell capacity and operating modes for cabin heating and battery charging. Performance was compared in terms of SOC variation, fuel cell loading patterns, hydrogen consumption, and equivalent fuel economy. Results indicate that the high-capacity strategy improves SOC stability but increases hydrogen consumption and reduces overall efficiency. In contrast, the strategy prioritizing cabin heating with minimal battery charging effectively utilizes waste heat and achieves the highest equivalent fuel economy. These findings highlight key trade-offs among different operating strategies and demonstrate that fuel cells can significantly enhance BEB efficiency and driving performance in cold environments while reducing battery load. 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

Integrated energy systems (IES) are widely recognized as a key pathway toward carbon neutrality, enabling the coupling and coordinated optimization of electricity, heat, gas, and cooling. This review provides a structured, technology-oriented overview of IES based on a unified five-subsystem framework (production, conversion, transmission, storage, and consumption). It systematically covers: (1) renewable energy utilization—solar, wind, and geothermal—supported by a global spatial distribution map and representative top-performing commercial products; (2) energy cascade utilization, where combined heat and power/combined cooling, heating and power (CHP/CCHP) raises overall efficiency from approximately 35–40% to 70–90%; (3) multi-form energy storage—electrical, electrochemical, chemical, thermal, and mechanical—distinguishing short-term balancing (e.g., lithium-ion (Li-ion), flywheels, supercapacitors, with 85–95% round-trip efficiency) from long-duration and seasonal applications (e.g., pumped hydro, hydrogen/power-to-gas (P2G), redox flow batteries); and (4) forecasting, collaborative optimization, and the bidirectional integration of IES with smart grids and grid modernization. A strategic strengths, weaknesses, opportunities, and threats–Political, Economic, Sociological, Technological, Legal, and Environmental (SWOT–PESTLE) analysis is further presented to position IES within the global energy transition. The review highlights that IES and grid innovation are mutually enabling, and that realizing the full carbon-neutrality potential of IES requires coordinated progress in standardization, digitalization, long-duration storage, and cross-sector policy alignment. Full article

Accurate estimation of internal battery temperature is critical for the safety and state-of-health assessment of lithium-ion batteries, yet it remains challenging due to the trade-off between model accuracy and computational feasibility on resource-constrained edge hardware. This work targets stationary large-scale battery energy storage stations (BESS), where ambient temperatures are actively regulated within a narrow range (typically 15–35 °C), and is developed and validated on large-format prismatic LFP cells. We propose ThermaPhysLite, a lightweight physics-informed neural network (PINN) framework with three innovations: (i) a lightweight PINN architecture tailored for edge devices; (ii) integration of a simplified electro–thermal model—a lumped-parameter thermal circuit coupled with the Bernardi heat generation equation—into a multi-scale temporal convolutional network (MS-TCN) through the PINN paradigm; and (iii) real-time online deployment on the ESP32-S3 embedded platform. Ground-truth internal temperatures were obtained via side-drilled thermocouple embedding in disassembled cells. Offline validation under three operating conditions demonstrates RMSE values of 0.15–0.20 °C. Following INT8 quantization (compressed to 84.29 KB), online deployment yields RMSE values of 0.17–0.24 °C with single-cell inference latency of 120 ms, demonstrating practical viability for BMS in large-scale energy storage systems. Full article

Transition metal sulfides are promising anode materials for lithium-ion batteries, but their practical application is limited by severe volume variation and sluggish reaction kinetics during cycling. Inspired by the natural mineral assemblage of seafloor massive sulfides (SMS), FeS₂/CuFeS₂ composite anodes were prepared by a mechanochemical ball-milling method with mass ratios of 9:1 and 7:3 to reflect the major compositional characteristics of SMS. Among them, the 9:1 composite (F9C1) exhibited the best overall electrochemical performance, delivering a reversible capacity of 763.4 mAh g⁻¹ after 300 cycles at 1 C and retaining 46% of its baseline capacity at 5 C. Structural and electrochemical analyses suggested that the introduction of a small amount of CuFeS₂ likely promoted interfacial interactions between FeS₂ and CuFeS₂ phases, reduced charge-transfer resistance, and enhanced pseudocapacitive contribution, while preserving the capacity advantage of the FeS₂ host phase. These results demonstrate that mineral-inspired compositional design is an effective strategy for improving the lithium-storage performance of sulfide anodes and provides a feasible route for developing electrode materials inspired by naturally coexisting sulfide minerals. Full article

As new energy vehicles and energy storage systems become more common, safety accidents caused by lithium-ion batteries overheating have become more of a concern. Early detection based on distinctive gases (such as H₂ and CO) can give an earlier warning than typical monitoring methods like temperature, voltage, or impedance. Nonetheless, attaining high-precision identification in intricate mixed-gas settings continues to be difficult because of the considerable cross-sensitivity of metal oxide semiconductor (MOS) gas sensors. This research presents an ISA-LSTM-TCN multi-task learning model utilizing an enhanced spatial attention mechanism for the swift identification and concentration forecasting of distinctive gases during lithium-ion battery thermal runaway. The model improves key feature extraction and anti-noise performance by combining the long-term temporal modeling ability of the Long Short-Term Memory (LSTM) network with the multi-scale feature extraction ability of the Temporal Convolutional Network (TCN). It also adds an Improved Spatial Attention (ISA) module with a residual multiplication structure. Moreover, in a multi-task learning framework, joint optimization of gas categorization and concentration regression is facilitated using a hard parameter-sharing method. Tests using a built MOS sensor array dataset show that the model is 99.23% accurate at classifying gases and that the $R^2$ values for predicting H₂ and CO concentrations are 0.9510 and 0.8400, respectively. Tests on public datasets and in different noisy environments show that the model is even better at generalizing and is more robust. The results show that the suggested method allows for quick, accurate detection of thermal runaway gases. This makes it an effective and smart way to monitor battery safety warning systems. Full article

Metallic contamination is a critical manufacturing defect in lithium-ion batteries, but the degradation evolution and electrochemical signatures of Cu-contaminated cells remain insufficiently understood. In this study, Cu particles were intentionally introduced into graphite/NCM811 pouch cells to investigate Cu-induced internal short circuit, cycling degradation, and defect detection. The Cu-contaminated cells exhibit significantly higher initial self-discharge rates, indicating the formation of a cathode-to-anode type internal short circuit. X-ray microscopy and SEM/EDS characterization reveal local separator penetration, electrode deformation, Cu dissolution/migration/deposition, Al current collector dissolution, and deposit accumulation on the anode surface. After cycling, the Cu-contaminated cells showed accelerated capacity fade and increased direct current internal resistance, while their self-discharge rate gradually decreased, suggesting partial mitigation of the internal short circuit path. Incremental capacity analysis was used to evaluate the internal short circuit severity, while differential voltage analysis was further applied to distinguish a Cu-induced internal short circuit from normal aging. This work provides mechanistic insight into Cu-contamination-induced degradation and electrochemical signatures for identifying metallic-contamination defects in lithium-ion cells. Full article

Accurate estimation of the state of health (SOH) and prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) are critical for ensuring system reliability and safety across diverse energy storage applications. This paper proposes a hybrid deep learning framework that integrates an axial-embedding Transformer (AxEmbTrans) encoder and a bidirectional LSTM (BiLSTM) module for the joint estimation of SOH and RUL. The AxEmbTrans encoder employs axial attention with abstract embeddings to capture global dependencies among multidimensional health features at reduced computational complexity compared to standard self-attention, while the BiLSTM models local temporal dynamics and short-term degradation fluctuations across consecutive cycles, with its bidirectional structure enhancing robustness against transient noise. Informative health features are extracted from charge–discharge curves, grouped into temporal, energy, and thermal categories, and fused using local linear embedding (LLE) for nonlinear dimensionality reduction. An improved Newton–Raphson-based optimizer (INRBO) is introduced to automatically tune the framework’s key hyperparameters, including the hidden dimension, number of attention heads, number of BiLSTM units, and learning rate, incorporating directional similarity modulation and multi-elite guidance to overcome the convergence instability of the standard NRBO. Extensive experiments on NASA and Maryland datasets demonstrate that the proposed method consistently outperforms baselines in both SOH and RUL prediction, achieving higher accuracy, improved robustness, and better cross-condition generalization. Full article

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

Lithium-ion battery (LIB) cells are available in various sizes, formats, and chemistries. Should a LIB be exposed to conditions outside its operating parameters, each variation affects the cell failure mechanisms and any resultant fire dynamic. Battery fires can be dynamic events that differ significantly from those solid-, liquid- or gas-based fire curves often used in standard building material fire resistance tests. This preliminary research aimed to investigate how standard building materials, sometimes used as a compartment fire envelope, such as gypsum plasterboard, react when exposed to a dynamic battery fire. The research explored batteries that produced jet fires, could act as projectiles, or produced overpressures when they failed. The results showed that cylindrical cells can travel at significant speeds and distances due to expulsing the cell’s contents through the cell’s vent or ejected end cap. These cells were shown to be capable of piercing plasterboard and remain hot enough to present a fire risk where they fall on the far side of the plasterboard. It was also found that the overpressures produced by failing prismatic cells affected the structural integrity of some building materials. The results show a need for further research into the effectiveness of standard building fire controls when exposed to LIB fires. Full article

Preceramic polymers, especially silicon oxycarbide (SiOC) and silicon carbonitride (SiCN) ceramics, have gained significant attention due to their wide range of applications in many fields, particularly in energy storage devices beyond conventional lithium-ion batteries (LIBs). This review focuses on the synthesis, structural characteristics, and properties of SiOC and SiCN ceramics as electrodes for battery applications. Furthermore, their promising applications as electrode materials for energy storage systems are explored, along with the most recent advances in the development of such materials and their use in lithium-ion batteries (LIBs), lithium-sulfur batteries (LSBs), potassium-ion batteries (PIBs), sodium-ion batteries (SIBs), and supercapacitors. This review addresses the distinct advantages of SiOC and SiCN ceramics, including high thermal stability, mechanical robustness, and adaptable microstructures. It also examines the challenges associated with the commercialization of these ceramics, including issues related to electronic conductivity and ion transport pathways. Full article

Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider real-world battery operating conditions. In practice, batteries operate under highly diverse usage patterns, environmental conditions, and user profiles, which can significantly affect SoC estimation accuracy. In this paper, we address this limitation by leveraging real-world data, which contains measurements from vehicle batteries under heterogeneous user behaviors and operating scenarios. The proposed methodology includes a data cleaning and filtering preprocessing stage, followed by an original DL framework designed to evaluate SoC estimation under different learning conditions. The framework is data driven and built upon a TimerV2-based architecture capable of capturing long-term temporal dependencies and nonlinear relationships in battery signals. Furthermore, transfer learning strategies are explored to enhance adaptability across different battery configurations and datasets for efficient knowledge transfer. Extensive experiments show that the proposed approach achieves high estimation accuracy and strong generalization performance, demonstrating its suitability for reliable real-time SoC estimation in practical battery management systems. Full article