Search Results (5,455)

Battery system engineers face the challenge of balancing competing requirements regarding performance, maintainability, sustainability, safety, and cost—especially in the automotive industry. IBS potentially offer a solution with fewer trade-offs. They feature a battery management system with advanced sensing and data analysis capabilities that facilitate improved battery monitoring and operation. Reconfigurable energy storage units enable sophisticated operating strategies, including complete cell state control, full energy content utilization, and a measured response to faults. This article presents the design, development, and operation of a full-scale intelligent battery system prototype comprising 324 automotive lithium-ion cells with a nominal voltage of 400$\mathrm{V}$. The system exhibits a modular single cell architecture and an advanced centralized battery management system. We detail the system architecture, hardware and software component design, and system integration. Initial tests demonstrate the battery’s operability, extended functionality, and enhanced safety. Our analysis shows that the additional losses introduced by reconfigurability are more than offset by the benefits of full energy utilization—even for new cells, with increasing advantage as aging progresses. The results underscore the potential of intelligent battery systems and motivate further research and development toward economic assessment and industrial adoption. Full article

Silicon–carbon (Si–C) composite anodes are a promising pathway to enhance the energy density of lithium-ion batteries (LIBs), yet the substantial volume changes of silicon during (de)lithiation cause mechanical degradation, capacity fading, and safety risks that hinder practical use. To address these challenges, we develop an electrochemical–thermal–mechanical coupled model tailored for LIBs with Si–C anodes. Built upon the Newman pseudo-two-dimensional framework, the multi-scale model integrates particle-, electrode-, and cell-level submodels. Electrochemical–mechanical coupling is captured through intercalation-induced particle expansion and cell-level thermal expansion, while bidirectional electrochemical–thermal coupling is introduced via a lumped thermal model with temperature-dependent electrochemical kinetics. The model is validated against experimental data, accurately reproducing current–voltage profiles, temperature rise, and displacement under various operating conditions. Simulations further reveal the distinct contributions of silicon and graphite: although silicon accounts for only a small fraction of anode mass, it can contribute 30% to the capacity of the cell owing to the high specific capacity of Si. At the same time, while silicon particles undergo volume changes exceeding 300%, the overall cell expansion remains below 7.5% due to structural dilution effects from other components. These findings establish a quantitative link between silicon content, electrochemical behavior, and cell expansion, providing theoretical guidance for the rational design of high-energy-density LIBs. Full article

This comprehensive review examines the transformative role of metal–organic frameworks (MOFs) in advancing battery separator technology to address critical safety challenges in rechargeable lithium metal batteries. MOF-based separators leverage their highly specific surface area, tunable pore structures, and functionalized organic ligands to enable precise ion-sieving effects, uniform lithium-ion flux regulation, and dendrite suppression—significantly mitigating risks of internal short circuits and thermal runaway. We systematically analyze the mechanisms by which classical MOF families (e.g., ZIF, UiO, MIL series) enhance separator performance through physicochemical properties such as electrolyte wettability, thermal stability (>400 °C), and mechanical robustness. Furthermore, we highlight innovative composite strategies integrating MOFs with polymer matrices (e.g., PVDF, PAN) or traditional separators, which synergistically improve ionic conductivity while inhibiting polysulfide shuttling in lithium–sulfur batteries and side reactions in aqueous zinc-ion systems. Case studies demonstrate that functionalized MOF separators achieve exceptional electrochemical outcomes: Li–S batteries maintain >99% Coulombic efficiency over 500 cycles, while solid-state batteries exhibit 2400 h dendrite-free operation. Despite promising results, scalability challenges related to MOF synthesis costs and long-term stability under operational conditions require further research. This review underscores MOFs’ potential as multifunctional separator materials to enable safer, high-energy-density batteries and provides strategic insights for future material design. Full article

The growing use of lithium-ion batteries (LIBs) in electric vehicles has accelerated the need for efficient strategies to extend their lifespan through second-life applications, where retired batteries are repurposed for stationary storage and other less demanding roles. This paper reviews the most pertinent degradation mechanisms underlying battery aging and the most frequently occurring faults during battery operation. After establishing the correlation between degradation and fault occurrence, reliable state-of-health (SOH) and remaining useful life (RUL) predictions are identified as central to ensuring safety, reliability, and cost-effectiveness in repurposed systems. Next, we present a systematic review of the recently published studies on battery prognosis, with methods categorized into three groups: (i) physics-informed and hybrid models; (ii) purely data-driven approaches; and (iii) transfer learning and features extraction methods. A comparative analysis highlights the strengths and limitations of each group and identifies the most promising approaches for battery repurposing. Modeling heterogeneous second-life packs remains particularly challenging, as cells often enter repurposing with different usage histories and only partial BMS records. In this context, transfer learning and domain adaptation emerge as the most promising directions. In parallel, Generative Adversarial Networks (GANs) can help in addressing the challenge of data scarcity, particularly when integrated into hybrid frameworks for second-life applications. At the same time, systematic exploration of health indicators—including the possibility of stage-specific ones—remains essential. Finally, reinforcement learning offers a complementary yet still underexplored path, enabling real-time adaptation in dynamic scenarios, as batteries enter nonlinear regimes beyond the knee point. Full article

Phase engineering has emerged as a powerful method for manipulating the structural and electrical characteristics of nanomaterials, resulting in significant enhancements in their electrochemical performance. This paper examines the correlation among morphology, crystal phase, and electrochemical performance of nanomaterials engineered for high-performance batteries and supercapacitors. The discourse starts with phase engineering methodologies in metal-based nanomaterials, including the direct synthesis of atypical phases and phase transformation mechanisms that provide metastable or mixed-phase structures. Special emphasis is placed on the impact of these synthetic processes on morphology and surface properties, which subsequently regulate charge transport and ion diffusion during electrochemical reactions. Additionally, the investigation of phase engineering in transition metal dichalcogenide (TMD) nanomaterials highlights how regulated phase transitions and heterophase structures improve active sites and conductivity. The optimized phase-engineered ZnCo₂O₄@Ti₃C₂ composite exhibited a high specific capacitance of 1013.5 F g⁻¹, a reversible capacity of 732.5 mAh g⁻¹, and excellent cycling stability, with over 85% retention after 10,000 cycles. These results confirm that phase and morphological control can substantially enhance charge transport and electrochemical durability, offering promising strategies for next-generation batteries and supercapacitors. The paper concludes by summarizing current advancements in phase-engineered nanomaterials for lithium-ion, sodium-ion, and lithium-sulfur batteries, along with supercapacitors, emphasizing the significant relationship between phase state, morphology, and energy storage efficacy. This study offers a comprehensive understanding of the optimal integration of phase and morphological control in designing enhanced electrode materials for next-generation electrochemical energy storage systems. Full article

The global water crisis, intensified by climate change and population growth, underscores the critical need for sustainable water production. Desalination is a pivotal solution, but its energy-intensive nature demands a transition from fossil fuels to renewable sources. However, the inherent intermittency of solar and wind power poses a fundamental challenge to the stable operation of desalination plants. This review provides a comprehensive analysis of a specifically tailored solution: hybrid energy storage systems (HESS) that synergistically combine batteries and hydrogen fuel cells (FC). Moving beyond a general description of hybridization, this study delves into the strategic complementarity of this pairing, where the high-power density and rapid response of lithium-ion batteries manage short-term fluctuations, while the high-energy density and steady output of fuel cells ensure long-duration, stable baseload power. This operational synergy is crucial for maintaining consistent pressure in processes like reverse osmosis (RO), thereby reducing membrane stress and improving system uptime. A central focus of this review is the critical role of advanced energy management systems (EMS). We synthesize findings on how intelligent control strategies, from fuzzy logic to metaheuristic optimization algorithms, are essential for managing the power split between components. These sophisticated EMS strategies do not merely ensure reliability, they actively optimize the system to minimize hydrogen consumption, reduce operational costs, and extend the lifespan of the hybrid energy storage components. The analysis confirms that a lithium-ion battery-fuel cell HESS, governed by an advanced EMS, effectively mitigates renewable intermittency to significantly enhance freshwater yield and overall system reliability. By integrating component-specific hybridization with smart control, this review establishes a framework for researchers and engineers to achieve significant levels of energy efficiency, economic viability, and sustainability in renewable-powered desalination. Full article

Deep Eutectic Solvents (DESs) have emerged as promising candidates to replace conventional organic solvents in various technological applications due to their low vapor pressure, non-flammability, and ease of preparation at low costs. In particular, Type IV DESs, which are composed of metal salts and hydrogen bond donors, are possible replacements for lithium-ion battery electrolytes. In this study, we investigate the molecular dynamics of solvents of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and pyrazole (PYR) at varying LiTFSI:PYR molar ratios (1:2, 1:3, 1:4, 1:5) using Nuclear Magnetic Resonance Dispersion (NMRD) and Pulsed Field Gradient (PFG) Nuclear Magnetic Resonance (NMR). PFG NMR reveals composition-dependent diffusion trends, while NMRD provides molecular-level insights into the longitudinal relaxation rate (R₁ = 1/T₁). Notably, the LiTFSI:PYR (1:2) sample shows distinct behavior across both techniques, exhibiting enhanced relaxation rates and lower self-diffusion for ¹H compared to the other nuclei (¹⁹F and ⁷Li), suggestive of stronger and more efficient Li⁺–pyrazole interactions, as confirmed by the modeling of the relaxation profiles. Our study advances understanding of ion dynamics in azole-based eutectic solvents, supporting their potential use in safer battery electrolytes. Full article

Lithium-ion batteries are essential to ensure electric mobility and reduce CO₂ emissions from transportation. One of the most commonly used chemistries is nickel–cobalt–manganese (NMC) batteries, which also have applications beyond the automotive sector. The recycling of these batteries requires the development of technologies to enable the selective separation and recovery of the metals present in the battery. One of these selective technologies involves the use of deep eutectic solvents (DESs). This research study investigates the different parameters that influence the recovery of Co(II) from hydrochloric acid medium using the deep eutectic solvent 3 Aliquat 336:7 L-Menthol. Firstly, using synthetic Co(II) solutions, the parameters influencing the cobalt extraction process are examined, and then these optimal conditions are applied to the recovery of cobalt from solutions obtained by dissolving NMC 622 battery black mass in 10 M HCl. The obtained results show that the DES used is highly selective for Co(II) recovery compared to other metals present in the solution (Ni, Li and Mn), achieving recoveries of up to 90% of the cobalt initially present in solution. Stripping with H₂SO₄ 0.5 M allows the recovery of cobalt as a crystalline monohydrate salt (CoSO₄.H₂O). The optimization of the Co/Cu separation conditions is carried out, achieving the separation of Cu(II) using Aliquat 336 in kerosene. Full article

Sodium-ion batteries (SIBs) are gaining attention in research and industry as a sustainable alternative to lithium-ion batteries (LIBs). However, the advantages of sodium over lithium in terms of accessibility, price, and environmental impact are currently not fully exploited because of inexperience in production, leading to inhomogeneities in their behavior. Using electrical (e.g., open-circuit voltage curve (OCV), electrochemical impedance spectroscopy) and non-electrical measurement methods (e.g., laser scanning microscopy, computed tomography), three widely used LIB technologies and one SIB technology, all with the same rated capacity (1500 mAh) and format (18650), are compared in this article. The study reveals significant differences, such as a 12% lower cell weight at the same rated capacity of the SIB using less windings in the jelly roll, as well as a high energy density cell configuration and a much more severe dependency of the discharge capacity on temperature, exceeding the LIBs by at least a factor of 5. Additionally, the impedance of the SIB differs due to slower ion kinetics on the electrodes, showing relevant differences in both the frequency behavior and the pulse relaxation to the LIBs. An OCV reconstruction indicates the sparsity in the available literature data and the necessity to further investigate the characteristics of the SIB to validate it as a drop-in technology on the market. Full article

Swelling in pouch batteries poses reliability issues and safety hazards, resulting in product damage, fires, and explosions. This study examines swelling based on the impact of C-rate and temperature during charge–discharge tests, and upper voltage limit and temperature during constant voltage/float charging tests. Internal cell dynamics related to swelling are analyzed using equivalent circuit model parameters from electrochemical impedance spectroscopy tests, and correlations with thickness are established. Constant voltage charging experiments show that swelling follows an initial increase, a plateau, and then a rapid escalation. The onset of rapid swelling accelerated with temperature and voltage, thereby reducing the time to the knee point. A double-exponent swelling model is developed to predict the evolution of thickness under various stress conditions. The results demonstrate that monitoring swelling rate and magnitude can serve as an effective diagnostic for identifying abnormal cell behavior. Full article

Computed tomography (CT) imaging has proven to be effective for detecting and visualizing a wide range of inhomogeneities and defects. Applying computer vision (CV)-based image processing enables detailed feature measurements on selected CT image slices, which could be of benefit as cells of the same type often show variations in electrical properties. When combined with electrical testing, CT imaging could provide valuable insights into the battery cell, helping to identify potential sources of electrical deviations. However, it remains unclear to what extent CT-based measurements, especially for larger prismatic cells, e.g., those used in automotive applications, can explain electrical deviations aside from identifying significant or latent defects. Therefore, this study performs a correlative analysis and compares the electrical measurement results with CT-based measurements of the cell’s physical features, specifically the anode and cathode sizes. Electrical and CT measurements from ten lithium iron phosphate/graphite (LFP/C) cells of the same type are analyzed. The results indicate that while CT imaging has the potential to help identify the sources of electrical deviations, it also shows that cell-level CT measurements alone cannot fully explain electrical performance deviations. Measurement uncertainty, the potential overlapping impact of other cell features, and the actual influence of the measured physical properties on the cell’s electrical performance limit the correlation between CT-based measurements and electrical parameters. Full article

Copper (Cu) doping is recognized as an effective strategy to enhance the electrochemical properties of LiNi_1−x−yCo_xMn_yO₂ (NCM) cathode materials. However, the influence of Cu²⁺ doping on particle size and grain boundary fusion remains insufficiently explored. A simple microwave-assisted solution combustion synthesis method was used to introduce Cu²⁺ into LiNi_0.5Co_0.2Mn_0.3O₂ (NCM523), aiming to regulate particle size and grain boundary fusion. The results demonstrate that increasing the Cu²⁺ doping content promotes particle growth, while an appropriate doping level reduces the degree of grain boundary fusion and cation mixing. Benefiting from these structural improvements, the optimized LiNi_0.5Co_0.2Mn_0.29Cu_0.01O₂ (Cu–1) cathode exhibits significantly enhanced electrochemical performance, delivering a discharge capacity of 128.6 mAh g⁻¹ after 100 cycles at 0.2 C, which is 32 mAh g⁻¹ higher than value of the undoped sample (96.6 mAh g⁻¹). These findings underscore that tailored Cu²⁺ doping can effectively optimize the microstructure of NCM523, leading to superior cycling stability, and provide new insights into the design of high-performance NCM cathodes. Full article

In recent years, immersion cooling has gained wide interest for thermal management of lithium-ion batteries. Usually, dielectric oils or fluorinated liquid are used as immersion coolants to avert short circuits, but they have low thermal conductivity and high cost. Although water offers superior heat-transfer performance, its poor dielectric property means it cannot be used directly as an immersion coolant. Near full-depth partial immersion (NFDPI) was proposed as a viable alternative, in which water does not contact the tabs of batteries. In this study, an NFDPI experimental system is set up, and the effects of coolant flow rate, discharge rate, and inlet–outlet configuration on thermal management performance are investigated. Since direct observation of the immersion tank’s internal flow is challenging, numerical simulations are conducted to resolve the flow field under various operating conditions. The experimental and simulated results reveal that NFDPI cooling effectively limits the module’s maximum temperature, and the module’s maximum temperature spread is mainly attributed to the cell’s vertical temperature gradient. These findings offer guidance for the practical deployment of water-based NFDPI lithium-ion battery energy storage systems. Full article

Accurate state of charge (SoC) estimation is critical for the safety, performance, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. This study investigates the application of Electrochemical Impedance Spectroscopy (EIS) data in conjunction with tree-based ensemble machine learning algorithms—Random Forest, Extra Trees, Gradient Boosting, XGBoost, and AdaBoost—for precise SoC prediction. A real dataset comprising multi-frequency EIS measurements was used to train and evaluate the models. The models’ performances were assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that Extra Trees achieved the best accuracy (MSE = 1.76, RMSE = 1.33, R² = 0.9977), followed closely by Random Forest, Gradient Boosting, and XGBoost, all maintaining RMSE values below 1.6% SoC. Predictions from these models closely matched the ideal 1:1 relationship, with tightly clustered error distributions indicating minimal bias. AdaBoost returned a higher RMSE (3.06% SoC) and a broader error spread. These findings demonstrate that tree-based ensemble models, particularly Extra Trees and Random Forest, offer robust, high-accuracy solutions for EIS-based SoC estimation, making them promising candidates for integration into advanced battery management systems. Full article

Despite its ultrahigh theoretical capacity, silicon anodes for lithium-ion batteries suffer from severe capacity decay caused by over 300% volume changes during cycling. While Si–Ge alloying and spherical nanostructuring have been demonstrated to improve ionic/electronic transport and mechanical resilience, scalable synthesis of homogeneous, sub-150 nm SiGe nanospheres from low-cost precursors remains challenging. Here, we report a hybrid plasma-spraying physical vapor deposition (PS-PVD) process that directly converts metallurgical-grade Si and Ge powders into phase-pure Si_0.8Ge_0.2 nanospheres (<100 nm) at a continuous rate of 1 g min⁻¹. The co-condensation mechanism during formation was elucidated through molecular dynamics (MD) simulations, which revealed a process initiated by inhomogeneous nucleation and followed by uniform cluster growth and spheroidization. Multiscale characterization confirmed the spherical morphology, compositional uniformity, and crystalline structure of the produced Si_0.8Ge_0.2 nanoparticles. The resulting anodes exhibited a stable capacity of ~1500 mAh g⁻¹ at 0.1C over 100 cycles (>80% retention) and a Coulombic efficiency of ~98%. This approach bridges the gap between high-performance design and industrial manufacturability, offering a practical route to next-generation anodes for electric vehicles. Full article