Search Results (174)

The article presents the possibilities of integrating artificial intelligence (through specific machine learning techniques) in the design and construction process of a battery in order to optimize its thermal management. The workflow starts from CFD thermal simulations (1C-rate) of a battery (16 Li-ion cells, type 18650, 4 × 4 arrangement), and based on the results, a complex thermal landscape is created through radial basis function (Rbf) interpolation. Furthermore, a robust neural network (NN) model is proposed and validated through the obtained performances, which is used further for the optimization of the design space (DSO) and multi-objective optimization (MOO) processes. The obtained results show that for DSO, a cell spacing of 1.37 mm is proposed for a maximum cell temperature of 25.53 °C, and in the case of MOO, a cell spacing of 2.64 mm (for minimum fan energy consumption). The main conclusion of the obtained results shows that the use of the NN model as a surrogate (the Digital Twin of a physical model) presents two great advantages in the process of designing a battery: running a CFD simulation for each point on the 2D grid would take hours, while the NN model can generate the entire map and find the optimum in less than 2 s, and moreover, thousands of additional points can be evaluated to find the thin limit of optimal models, effectively filtering out thousands of energy-consuming “suboptimal” configurations. Full article

This paper presents a modeling framework for large-capacity lithium-ion battery energy storage systems (BESSs), developed within the Modelica LIBSystems library and focused on system-level integration. The framework builds on a combined analysis of the electrical, thermal and degradation behavior at the cell level to model the BESS interconnection to the electrical grid. A semi-empirical aging model was incorporated following its validation at the cell level against capacity loss experimental measurements. Two case studies were conducted for a 10.5 MW/15 MWh BESS installed in the isolated power system of Terceira Island. The first analyzed the short-term response to a 5% load step decrease under 60% and 80% renewable penetration scenarios, yielding a frequency nadir improvement of 3 mHz and 21 mHz, respectively. The second projected long-term degradation under two dispatch strategies: one derived from historical time series, and another synthetically constructed to induce more frequent and deeper cycling. After 1000 days of operation, the state of health declined to 95.2% in the historical-based case and to 93.5% under the aggressive profile. The proposed framework establishes a unified, cross-domain modeling workbench for Li-ion BESS applications, enabling evaluation of the system design, control strategies, operation conditions, and system-level performance across both dynamic and long-term horizons. Full article

This paper presents a case study of research-based learning (RBL) implemented in an undergraduate engineering course through a module titled “State-of-Charge Monitoring of Li-Ion Batteries Using Thermographic Surface-Temperature Measurements”. The experiment involved 10 third-year engineering students and employed a single-group pre- and post-test design and a lecturer interview. The module provided students with an authentic research experience using advanced laboratory equipment. The study examines students’ attitudes, satisfaction, and development of research skills, as well as the lecturer’s perspective on the advantages and challenges of RBL. While the study had a limited timeframe and specific design characteristics, the findings could benefit researchers interested in integrating RBL. Results indicated that students showed initial interest, primarily seeking practical knowledge and skills. By the end of the experiment, they reported that RBL fostered high motivation and strengthened their sense of commitment, responsibility, and initiative. Despite the students’ enthusiasm and the lecturer’s motivation, the results show that preparing and implementing RBL required significant time and effort on the lecturer’s part. The students’ lack of prior knowledge in research activities and the limited time frame posed considerable challenges. Recommendations include implementing RBL over a longer period and involving additional educators to enhance student support. Full article

In this study, a battery case was developed using a 3D (three dimensional)-printed composite of hexagonal boron nitride (hBN) and polylactic acid (PLA) to enhance the thermal performance of lithium-ion battery (LiB) modules. A 10 wt.% amount of hBN was incorporated into the PLA matrix to improve the composite’s thermal conductivity while maintaining electrical insulation. A 3S2P (3 series and 2 parallel) battery configuration was initially evaluated based on the results of a baseline study for comparison and subsequently subjected to a newly developed test procedure to assess the thermal behavior of the designed case under identical environmental conditions. Initially, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses were utilized for material characterization, and their results verified the successful integration of hBN by confirming its presence in the hBN-PLA composite. In thermal tests, experimental results revealed that the fabricated hBN-PLA composite battery case significantly enhanced heat conduction and reduced surface temperature gradients compared to the previous baseline study with no case. Specifically, the maximum cell temperature (T_max) decreased from 48.54 °C to 45.84 °C, and the temperature difference (ΔT) between the hottest and coldest cells was reduced from 4.65 °C to 3.75 °C, corresponding to an improvement of approximately 20%. A 3S2P LiB module was also tested under identical environmental conditions using a multi-cycle charge–discharge procedure designed to replicate real electric vehicle (EV) operation. Each cycle consisted of sequential low and high discharge zones with gradually increased current values from 2 A to 14 A followed by controlled charging and rest intervals. During the experimental procedure, the average ΔT between the cells was recorded as 2.38 °C, with a maximum value of 3.50 °C. These results collectively demonstrate that the 3D-printed hBN-PLA composite provides an effective and lightweight passive cooling solution for improving the thermal stability and safety of LiB modules in EV applications. Full article

Vibrational spectroscopy is one of the most powerful techniques available for the characterization of materials for Li-ion batteries (LIBs) and one of the most useful tools when X-ray diffraction is ineffective for amorphous substances. Raman spectroscopy is essentially a probe to examine the surface of compounds that strongly absorb visible light, which is the case for all electrode materials, while infrared spectroscopy is a tool that examines the entire volume of particles. The purpose of this review is to study the lattice dynamics of cathode, anode, and electrolyte materials of advanced LIBs, especially nanomaterials for high-power-density application. Ex situ and in situ analyses are presented, which satisfy several key issues, such as structural stability over long-term cycling. Full article

The adoption of autonomous-driving rovers represents a feasible solution to improve the sustainability of the agricultural sector, as they are smaller and more efficient compared to traditional machinery. However, endurance and productivity can be critical factors for the adoption of such vehicles. In addition, the autonomous-driving algorithm should guarantee that the rover is able to accomplish tasks without supervision. In this paper, a numerical analysis of an autonomous-driving rover with a hybrid fuel-cell powertrain, specifically designed for orchards and vineyards, is presented. The proposed powertrain presents a first innovative integration of a metal-hydride hydrogen-storage system into an orchard mobile machine. A Li-ion battery pack is the main energy source, while the fuel-cell system operates in a range-extender configuration. A co-simulation model was developed comprising the autonomous-driving algorithm, a multibody model, and a powertrain model. Experimental tests were carried out to characterize the fuel-cell system and the metal-hydride tank, and the obtained data were used to develop and tune their numerical models. A virtual test scenario consisting of a typical rover maneuver, namely a 180-degree turn, performed in different soil and payload conditions, was defined, and simulations were carried out evaluating the rover’s performance. The simulation results showed that the rover completed the mission in loam and hard soil conditions, and with up to 200 kg of payload. Moreover, the fuel-cell range extender significantly enhanced the rover’s endurance, with up to +60% of increase when employing a tank swap technique to replace the metal-hydride tank upon hydrogen depletion. On the contrary, in the case of critical terrain conditions, such as muddy and sandy soils, the rover was not capable of completing the task due to tire slipping. Full article

As a part of the power subsystem in spacecraft, batteries face a less severe environment, but they are sensitive to thermal changes under strict thermal requirements. In order to understand the thermal behavior, the best and cost-effective method is to use a structural thermal representative of the design, apply flight-like conditions, and create a simulation model. In order to fulfill this task, a thermal balance test was applied to a newly developed Li-ion battery’s structural thermal model in this study. The thermal control hardware was designed identical to the flight model, and the tests were conducted on eight scenarios simulating the thermal environment in space. A reduced thermal mathematical model was derived using the experimental data inversion. First, the battery parts that indicate similar temperatures were assumed as a unique node, and each part of the structural thermal model was represented by a single node; the heat exchange between each node was modeled by linear conductors. Due to high temperature differences in higher temperature simulations, the model was updated with radiation conductors, which was called hybrid modeling. It was seen that the final mathematical correlation was satisfied with test cases 4 to 8, which cover the operational and non-operational temperature limits of the Li-ion battery. Full article

With the growing demand for durable and corrosion-resistant materials in advanced Li-ion battery cases, super duplex stainless steels (SDSSs) have emerged as promising candidates due to their excellent mechanical and electrochemical properties. This study aims to investigate how the ferrite and austenite phase balance in SDSS EN 1.4501 affects the microstructural and electrochemical behavior of Ag coatings, tailored for next-generation battery enclosure applications. Ag coatings were deposited to PVD (to 1 μm) on SDSS EN 1.4501 substrates with varying ferrite (from 32 vol.% to 70 vol.%) and austenite ratios (from 56 vol.% to 30 vol.%) to evaluate the influence of phase balance on coating performance. Microstructural analysis was performed using field emission scanning electron microscopy (FE-SEM, mag x 1000), electron backscatter diffraction (EBSD), and X-ray diffraction (XRD, from 20° to 80°), which provided insights into surface morphology, crystallographic texture, and phase distribution. Electrochemical characteristics were assessed through open circuit potential (OCP), and potentiodynamic polarization in a simulated corrosive environment. The results showed that a balanced duplex microstructure promoted superior Ag coating adhesion, grain refinement, and uniform phase distribution. Furthermore, the electrochemical analyses indicated enhanced corrosion resistance and passivation layer stability in volume fraction balanced substrates, as evidenced by more noble OCP values (form −0.06 V to −0.11 V), and potentiodynamic polarization value (higher corrosion potential (from 0.08 V to 0.10 V), and lower corrosion current densities (from 3 × 10⁻⁷ A/cm² to 4 × 10⁻⁷ A/cm²)). These findings demonstrate that optimizing the phase balance in SDSS is critical for achieving high-performance Ag coated surfaces, offering significant potential for durable and corrosion-resistant Li ion battery casing applications. Full article

The anisotropic jelly roll structure of cylindrical Li-ion batteries leads to highly directional heat generation, causing severe radial heat accumulation and creating a critical demand for precise thermal management. Conventional anisotropic phase change materials (PCMs), often reliant on single-dimensional conductive skeletons, exhibit limited enhancement in thermal conductivity anisotropy. This study proposes a novel strategy utilizing a hybrid carbon aerogel composed of one-dimensional carbon nanotubes (CNTs) and three-dimensional expanded graphite (EG) to construct highly aligned thermal conduction pathways within a flexible PCM. A three-step experimental method was employed to successfully fabricate a composite PCM with highly anisotropic thermal conductivity. A case study confirmed that, compared to a sole 3D skeleton, the hybrid 1D/3D aerogel significantly improves the alignment of the microstructure. At an optimal hybrid aerogel content of 8 wt.%, the composite achieved a 5.0% increase in radial thermal conductivity and a remarkable 16.7% increase in axial thermal conductivity, indicating a significantly optimized anisotropy ratio. When applied to a cylindrical battery thermal-management case, this material enables directed heat dissipation, effectively lowering the maximum battery-surface temperature by 13.1 °C. This work provides a scalable approach for designing high-performance anisotropic flexible PCMs tailored for advanced thermal management in high-power-density Li-ion batteries and other compact electronics. 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 recent increase in end-of-life (EoL) lithium-ion batteries (LiBs) has become a significant concern worldwide. Most studies in the literature have primarily focused on recovering cathode active metals from black mass (BM), whereas the separation of anode–cathode foils, plastics, and casing metals which are the essential components of LiBs has received relatively little attention. To reduce costs and maximize the recovery of valuable metals in subsequent hydrometallurgical or pyrometallurgical processes, EoL LiBs require appropriate pre-treatment. This study aims to scrape off the BM adhering to the electrode foils resulting from gradual crushing and subsequently separate the plastics and copper (Cu) from other metals through a two-step selective flotation process. The results demonstrated that plastics, due to their natural hydrophobicity, could be effectively removed using a frother, achieving more than 95% recovery with less than 5% metallic contamination. Following plastic flotation, Cu particles were floated in the presence of 3418A, yielding a Cu concentrate containing 65.13% Cu with a recovery rate of 96.4%. Additionally, the aluminum (Al) content in the non-floating material, remaining in the cell, increased to approximately 77%. Full article

The rapid growth of the electric vehicle (EV) market has highlighted the critical importance of securing a stable supply chain for lithium-ion battery (LIB) resources, thereby increasing the need for efficient recycling technologies. Among these, lithium recovery remains a major challenge due to significant losses during conventional processes. In this study, a chlorination roasting process was introduced to convert Li₂O in spent LIBs into LiCl, which was subsequently evaporated for selective lithium extraction and recovery. Roasting experiments were conducted under air, vacuum, and N₂ conditions at 800–1000 °C for 1–5 h, with Cl/Li molar ratios ranging from 0.5 to 8. The optimal condition for lithium evaporation, achieving 100% recovery, was identified as 1000 °C for 5 h, with a Cl/Li molar ratio of 6 under vacuum. Following lithium removal, residual valuable metals were extracted through H₂SO₄ leaching, and the effects of acid concentration and H₂O₂ addition on leaching efficiency were examined. The air-roasted samples exhibited the highest leaching performance, while the vacuum- and N₂-roasted samples showed relatively lower efficiency; however, the addition of H₂O₂ significantly enhanced leaching yields in these cases. Full article

Accurate modeling and performance analysis of brushless DC (BLDC) motors are essential for high-efficiency control in modern drive systems. In this article, a BLDC motor was modeled using system identification techniques. In addition, experimental data were collected from the BLDC motor, including its speed response to various input signals. Using system identification tools, particularly those provided by MATLAB/Simulink R2024b, an approximation model of the BLDC motor was constructed to represent the motor’s dynamic behavior. The identified model was experimentally validated using various input signals, demonstrating its accuracy and generalizability under different operating conditions. Additionally, a series of mechanical load tests was conducted using the AVL eddy-current dynamometer to evaluate performance under practical operating conditions. Fixed load torques were applied across a range of motor speeds, and multiple torque levels were tested to assess the motor’s dynamic response. Electrical power, mechanical power, and efficiency of the entire system were computed for each case to assess overall system performance. Moreover, the real-time state of charge (SOC) of Lithium-ion (Li-ion) battery was estimated using the Coulomb counting method to analyze the impact of Li-ion battery energy level on the BLDC motor efficiency. The study offers valuable insights into the motor’s dynamic and energetic behavior, forming a foundation for robust control design and real-time application development. Full article

The literature clearly indicates that both academia and industry are strongly committed to developing comprehensive processes for spent Li-ion battery (LIB) recycling. In this regard, the current study presents an original contribution by providing a quantitative assessment of a large-scale recycling plant designed for the treatment of completely spent LIBs. In addition to a concept of the basic process, this assessment also considers a case study of a thermal integration and CO₂ capture subsystem. Process flow modeling software was used to evaluate the contribution of all process steps and equipment to overall energy consumption and to mass balance the data required for the technical assessment of the large-scale recycling plant. To underline the advantages and identify the optimal novel process concept, several key performance indicators were determined, such as recovery efficiency, specific energy/material consumption, and specific CO₂ emissions. In addition, the economic potential of the recycling plants was evaluated for the defined case studies based on capital and O&M costs. The results indicate that, even with CO₂ capture applied, the thermally integrated process with the combustion of hydrogen produced in the recycling plant remains the most promising large-scale configuration for spent LIB recycling. Full article

The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10⁻⁴ and 2.66176 × 10⁻⁴. Full article