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Yolk–shell structured Sn@C materials are promising candidates of anode materials for lithium-ion batteries due to their high structural stability along cycling. However, their synthesis usually suffers from complicated procedures, low efficiency and uncontrolled morphology and size. In this study, a novel yolk–shell structured composite of phenolic-resin-based Sn@hollow carbon (C) composite was effectively synthesized using tributyl(ethenyl)stannane as the tin source and hexadecyltrimethylammonium bromide as the structure-directing agent. Small Sn particles, with diameters of 10–20nm, were discovered to be encapsulated within hollow carbon spheres of about 100nm and exhibited high dispersion. Benefiting from this excellent structural design, the specific lithium-storage capacity of this composite can still retain a value of 640mAh g${}^{-1}$ after 150 cycles at 0.1A g${}^{-1}$. An excellent rate performance of 280mAh g${}^{-1}$ was achieved at a high current density of 1A g${}^{-1}$, without decay after 600 cycles. The present study highlights the superiority of yolk–shell structure and provides a viable option for synthesizing advanced Sn-based anode materials of lithium-ion batteries.

The existing conventional vehicle transportation landscape in India is grappling with challenges stemming from extensive air pollution, health risks, surging oil prices, limited fossil fuel resources, substantial oil import expenses and energy volatility. To counter these issues, Electric Vehicles (EVs) are progressively replacing internal combustion engines, offering a promising route toward decarbonization and mitigating climate concerns. EVs rely on electric motors powered by batteries, predominantly Lithium-ion batteries (LIBs), known for their superior attributes such as low self-discharge, high energy density and extended life cycle. Nevertheless, LIB performance is significantly influenced by operating temperatures, with suboptimal conditions leading to decreased efficiency, power loss and faster aging. Addressing this, an effective Battery Thermal Management System (BTMS) becomes crucial to maintain batteries at optimal temperatures, enhancing their efficiency and safety. This study focuses on a computational analysis of passive heating systems employing Fins and Phase Change Materials (PCM) for 18650 Li-ion battery thermal management at low temperatures, with specific attention to battery module analysis. Numerical analysis using ANSYS FLUENT investigates the influence of varying PCM thickness on heat transfer, predicting temperature distribution and discussing its impact on battery output performance.

The electrification of transportation has led to an increased emphasis on the thermal management of battery packs to ensure their safety, efficiency, and longevity. This study presents a comprehensive estimation of the C-rate through analytical and the thermal behavior of an electric two-wheeler battery pack (3.2kWh) under varying operational conditions through a combination of simulation and experimental testing. Numerical analysis was conducted using Celsius EC Solver (version 2023.2) software, with a specific focus on parameters such as C-rate and temperature distribution. Experimental validation was performed under controlled conditions, and key metrics such as ambient temperature, maximum temperature reached at various locations, heat generation rates, voltage, and current were measured. The findings of this study demonstrate that there is less than 4% error which is well agreed between simulation and experimental results. This indicates a significant congruence between the simulated and experimental data, underscoring the reliability of the proposed simulation model. This analysis provides valuable insights for optimizing thermal management strategies in battery packs.

Fibrous membranes are promising separators for high-performance lithium ion battery because of their high porosity and superior electrolyte uptake. In this paper, the fabrication of hybrid gel polymer electrolyte (HGPE) by introducing SnO₂ nanoparticles in poly(vinylidine fluoride) by electrospinning technique and soaking the electrospun nanofibrous membranes in 1 M LiPF₆ in ethylene carbonate (EC)/diethyl carbonate (DEC) (1:1, v/v). The as-prepared electrospun HGPE with SnO₂ nanofiller was characterized by scanning electron microscopy. The influence of SnO₂ on the structure of polymer membrane, physical, and electrochemical properties is systematically investigated. HGPE shows significant high ionic conductivity 4.6 × 10${}^{-3}$ S/cm at room-temperature and better cell performance such as discharge C-rate capability and cycle performance. The hybrid gel polymer nanofibrous membrane favors high uptake of lithium electrolyte so that electrolyte leakage is reduced. The gel polymer electrolyte with SnO₂ filler was used for the fabrication of Li/PVdF-SnO₂/LiFePO₄ coin cell. The fabricated cell was evaluated at a current density of 0.2 C-rate and delivered stable and excellent cycle performance. This study revealed that the prepared HGPE can be employed as potential electrolyte for lithium ion batteries.

It was precisely because LiNi${}_{0.5}$Mn${}_{1.5}$O₄ cathode material had many advantages, including high voltage, high power, no pollution, low cost, etc., that it became a research hotspot for lithium-ion cathode materials. Although LiNi${}_{0.5}$Mn${}_{1.5}$O₄ has a three-dimensional Li${}^{+}$ diffusion channel, there are still some problems that lead to capacity decay and reduced cycling stability, limiting its commercial application. In this paper, the LiNi${}_{0.5}$Mn${}_{1.5}$O₄ cathode material was prepared by the freezing precipitation method. We studied the effects of different drying methods, such as blast drying, water bath drying and freeze-drying on the structure, morphology and electrochemical properties of LiNi${}_{0.5}$Mn${}_{1.5}$O₄ cathode materials. The study found that the LiNi${}_{0.5}$Mn${}_{1.5}$O₄ cathode material prepared by the precipitation-freeze-drying method had a complete crystal form, a stable structure and no Li_xNi${}_{1-x}$O impurity peak. The SEM image showed that the particles were smaller and had a smooth surface. The initial discharge-specific capacity at 0.1C was 105.2mAh⋅g${}^{-1}$. After 50 cycles, its specific discharge capacity was 99.4mAh⋅g${}^{-1}$ and the capacity retention rate was 94.5%. Compared with LiNi${}_{0.5}$Mn${}_{1.5}$O₄ materials prepared by other methods, it had better electrochemical performance.

The degradation degree prediction of lithium-ion battery has been studied through experimental data. Characterization parameters on the degradation degree of lithium-ion battery were deduced under consideration of the internal and external factors. The analysis of discrete degree was proposed to depict the degradation degree for lithium-ion battery. Furthermore, based on fuzzy inference system (FIS), the predicted model of the degradation degree for lithium-ion battery was built and its output was defined as the degenerate coefficient $\beta$, $\beta \in [0,1]$. Finally, by learning, training and simulating, the FIS model has been validated to be reliable and applicable in prediction on the degradation degree of lithium-ion battery. The simulation results show that the degradation degree of lithium-ion battery is more serious when $\beta$ is closer to 1, and the degradation degree is lighter when $\beta$ is closer to 0.

Some characteristic parameters were epurated in this paper by analyzing internal and external factors of the degradation degree of lithium-ion battery. These characteristic parameters include open circuit voltage (OCV), state of charge (SOC) and ambient temperature. The degradation degree was evaluated by discrete degree of the array, which is composed of the above parameters. The epurated parameters were verified through adaptive neuro-fuzzy inference system (ANFIS) model building. The expression of degradation coefficient was finally determined. The simulation results show that the expression is reasonable and precise to describe the degradation degree.

The lithium ion battery is considered as the experimental object, and its discharge characteristics are studied. A model of the battery in different charge-states is established by a tool of neural network while battery’s rebound voltage, temperature and load are set as input parameters. The validity of the model is tested based on the experimental data. The accuracy, adaptability and stability of the SOC in this model is validated in a variety of the working conditions, and the accuracy of the model is demonstrated to be higher than 5%.

We synthesized rock-salt-type Li–Ti oxynitride (LiTi${}_n$O${}_x$N${}_y$, $n=1$, 1.25, 1.5, 1.75, and 2) using solid-phase reaction. The best charge/discharge characteristics were obtained with a Ti/Li ratio of 1.25, and the maximum discharge capacity was 140 mAh/g.

The accurate health status evaluation of lithium-ion batteries is crucial for preemptive identification of potential battery failures and averting hazardous incidents, given its essential role in indicating the extent of battery degradation. The challenge in determining the State of Health (SOH) arises from the absence of a precise and standardized definition, as well as the difficulty in measuring essential input variables. Therefore, this paper utilizes current and voltage data during the charge and discharge process as direct inputs for SOH estimation and proposes a deep learning-based lithium-ion battery SOH estimation approach. Specifically, it leverages Bayesian optimized Convolutional Neural Network (CNN) within a data-driven framework. Experimental results demonstrate that the proposed deep learning method achieves a Mean Absolute Error (MAE) of 1% and a Maximum Error (MAX) below 4% in estimation accuracy, highlighting its enhanced precision and robustness.

A hybrid technique is proposed for electric vehicles (EVs) or hybrid electric vehicles (HEVs) with a battery thermal management system (BTMS). The proposed hybrid method is the combined execution of the Pelican Optimization Algorithm (POA) and Firebug Swarm Optimization (FSO). The hunting behavior of pelicans is improved with the help of the FSO technique; hence, it is named the POA-FSO strategy. Here, a battery of 36 lithium-ion (Li-ion) cells is considered. The proposed method of analyzing battery thermal performance analyzed factors like heat flux from the battery cell to the passage spacing size, cooling air and the cooling air’s mass flow rate (MFR). The BTMS performance is studied under maximum battery unit temperature, pressure drop and temperature uniformity. The proposed method enhances the coolant passage spacing and decreases the temperature difference in the battery cells. The proposed approach is executed on the MATLAB platform, and its performance is compared with existing approaches. From the results, it is concluded that the MFR maximizes, and a nonuniform distribution of the MFR of the passage occurs. By using the proposed approach, the maximal temperature change of cooling air among the passages is 2.0K, the maximal temperature change between the battery cells is 7.5K and a pressure drop of 228.03Pa is obtained.

We propose a weak solution theory for the sharp interface limit of the Cahn–Hilliard reaction model, a variational PDE for lithium-ion batteries. An essential feature of this model is the use of Butler–Volmer kinetics for lithium-ion insertion, which arises as a Robin-type boundary condition relating the flux of the chemical potential to the reaction rate, itself a nonlinear function of the chemical potential and the ion concentration. To pass through the nonlinearity as the interface width vanishes, we introduce solution concepts at the diffuse and sharp interface level describing dynamics principally in terms of an optimal dissipation inequality. Using this functional framework and under an energy convergence hypothesis, we show that solutions of the Cahn–Hilliard reaction model converge to a Mullins–Sekerka type geometric evolution equation as the width of the transition layer vanishes.

A microspherical Li₄Ti₅O₁₂/C composite composed of interconnected nanoparticles with BP-2000 carbon black as carbon source is synthesized for use as an anode material in high-power lithium-ion batteries. The composite is prepared through precursor pretreatment including pre-sintering, ball-milling, and spray-drying. The structure, size and surface morphology of the as-prepared particles are investigated by X-ray diffraction and scanning electron microscopy. Results show that the obtained material has a microspherical morphology consisting of nanosized prime particles with compact structure. The precursor pretreatment effectively reduced the agglomeration of the prime particles caused by high temperature sintering and led to a more uniform distribution of BP-2000 on the surface of prime particles generating highly efficient conductive network. The specific capacity of the electrode at 20 C rate is 131 mAh g^-1 and the loss of capacity is less than 2% after the 60 variation cycles (from 1 C to 20 C and back to 1 C). This excellent performance is attributed to the effective conductive network between the prime particles and the reduction of the lithium-ion diffusion pathway.

Amorphous molybdenum oxide films with almost 20$\mu$m thickness are electrodeposited on the Cu foils from a citrate-ammonia molybdate bath containing Ni${}^{2+}$ ions. The content of Ni in the oxide films is very low, 0.87 at.%. XRD and FTIR data suggest that they are composed of hydrous MoO₂. The multilayer and cracked structures are characterized by scanning electron microscopy (SEM), and are beneficial to transmission of Li${}^{+}$ ions between the electrolyte and anode materials. Galvanostatic battery testing shows that amorphous molybdenum (IV) oxides as anodes for lithium-ion batteries exhibit a high specific capacity of 876mA$\cdot$h$\cdot$g${}^{-1}$ at a current density of 50mA$\cdot$g${}^{-1}$, good capacity retention as high as 97.4% after 20 cycles.

An important technological problem is solved by numerical methods. Doping of silicene with phosphorus allows changing the morphology of the walls of the silicene channel without reducing their strength. The structure of lithium packings in the channels is studied in detail. The distribution of normal stresses in the walls of the channel before lithium intercalation and after complete lithium filling is determined. The calculated densities of electronic states allow us to conclude that both doped and undoped silicene on a graphite substrate become electrically conductive. The studied two-dimensional silicene can be used as an anode for next-generation lithium-ion batteries.

Poor cyclic performance of lithium-ion batteries is calling for efforts to study its capacity attenuation mechanism. The internal stress field produced in the lithium-ion battery during its charging and discharging process is a major factor for its capacity attenuation, research on it appears especially important. We established an electrochemical –mechanical coupling model with the consideration of the influence of elastic stiffening on diffusion for graphite anode materials. The results show that the inner stress field strongly depends on the lithium-ion concentration field, greater concentration gradients lead to greater stresses. The evolution of the stress field is similar to that of the concentration gradient but lags behind it, which shows hysteresis phenomenon. Elastic stiffening can lower the concentration gradient and increase elastic modulus, which are two major factors influencing the inner stress field. We conclude that the latter is more dominant compared to the former, and elastic stiffening acts to increasing the internal stress.

Many experiments have well found that the lifespan of lithium-ion (Li-ion) batteries can be effectively improved by nanoscale structured electrodes. In order to investigate the coupling mechanical and chemical mechanism underlying the superior performance of nanoscale structured electrodes, an alternative diffusion-stress coupling model considering the surface effect of nanomaterials is proposed. The diffusion effect is included in the linearly elastic constitutive relationship, while the diffusion process is conversely mediated by the stress field. The surface effect of nanomaterials is characterized by a surface-induced traction depending on the bulk surface energy density and surface relaxation parameter. Both the displacement field and the stress field in a typically nanoparticle structured electrode are analyzed. Theoretical predictions show that when the surface effect is considered, both the diffusion-induced radial expansion and the tensile stress are obviously smaller than the classical counterparts, which are attributed to the surface-induced traction analogous to a hydrostatic pressure on the surface of nanoparticles. Furthermore, both the diffusion-induced radial expansion and the tensile stress depend on the particle size, both of which decrease with a decreasing radius of nanoparticles. The present results may not only provide a reasonable explanation for the superior performance of Li-ion batteries with nanoscale structured electrodes but also be helpful for the optimal design of high performance batteries.

Highly ordered mesoporous antimony-doped tin oxide (ATO) materials, containing different amount of antimony in the range of 0–50mol%, are prepared via a nanoreplication method using a mesoporous silica template. The mesoporous ATO materials thus obtained exhibit high electrical conductivity, high reversible capacity, superior cycle stability and good rate capability as anode materials for lithium-ion batteries, compared to those of pure mesoporous tin oxide. Amongst the ATO materials in this work, the mesoporous ATO material with 10mol% of antimony has highest discharge capacity of 1940mAhg^-1 (charge capacity of 1049) at the 1st cycle, best cycle performance (716mAhg^-1 at 100th cycle) and excellent rate capability, which are probably due to the enhanced electrical conductivity as well as reduced crystalline size.

Despite the fact that silicon material can be synthesized from various sources, deriving them from silica resources is of strategic significance for the industrial processing. Here a low-cost nano/micro structure of Si–CNT was derived from nano-SiO₂ and multiwall carbon nanotubes (MWCNTs) with simple methods. By employing table salt (NaCl) as a heat scavenger for the magnesiothermic reduction, the nano/micro structure of the material was remained effective. Comparing to ball milling, a combination of SiO₂, MWCNTs and NaCl by spray drying achieved the long cycle life for Si–CNT composite. This material presented a stable capacity above 968.1mAh g${}^{-1}$ with excellent capacity retention of 85.4% at the 150th cycle versus the 2nd one. The Si nanoparticles, very small particle size in 10–20nm, homogenously dispersed in electronically conductive network of MWCNTS, which accommodate the volume change of Si and reinforce highly conductivity of the Si–CNT composite during repeated cycles. Combined with its low-cost and up-scaling technologies, Si–CNT composite is a promising anode material in rechargeable lithium batteries with high electrochemical performance.

The SiO_x/Si composite enhanced by dual-carbon (i.e., multiwall carbon nanotubes and carbon) was fabricated from the micro silicon monoxide (SiO) by the combination of high-energy mechanical milling, spray drying and pyrolysis. The obtained SiO_x/Si particles were composed of Si-suboxide and embedded nano-sized Si crystallites. As one of dual-carbons, the multi-walled carbon nanotubes were directly scaffolded of anchoring the SiO_x/Si composite particles through spray drying. Another carbon source was directly deposited on the surface of the SiO_x/Si by means of the carbonization of phenol–formaldehyde resin. Nano-sized silicon embedded in the Si-suboxide matrix and dual-carbon provided a compromise between the reversible capacity and cycle stability related to the volume change. The obtained SiO_x/Si/MWCNT/PC-1 electrode delivered an initial capacity of 936.5mAh g${}^{-1}$ and the reversible capacity was maintained at 825.9mAh g${}^{-1}$ with excellent capacity retention of 87.5% on the 200th cycle versus the 6th one (compared with the same current rate). In contrast, although the SiO_x/Si presented the higher initial capacity of 1271.4mAh g${}^{-1}$, its capacity dropped quickly after several cycles and capacity retention was only 25.6% versus the 6th cycle after 100 cycles.