Abstract Lithium-ion batteries are essential for enabling a carbon-neutral transport sector, with electric vehicles representing the most viable alternative to combustion engine vehicles. However, charging batteries rapidly without compromising durability remains a major challenge. This work investigates pulse charging as a strategy to mitigate durability losses while maintaining high charge rates. Battery cycling experiments were performed using various charge and discharge waveforms, with frequency and duty cycle systematically varied across specimen groups. A constant current protocol served as reference. The results show that capacity loss during constant current charging can be substantially reduced by employing pulsed waveforms. Specifically, a reduction of about 50% in capacity loss is observed when square-wave pulse charging at frequencies above 100 mHz is applied. Electrochemical analysis suggests that constant current charging accelerates ageing processes primarily on the negative electrode, likely linked to lithium plating and associated solid-electrolyte interphase (SEI) formation. By contrast, pulse charging seems to alleviate these effects. Overall, the findings demonstrate that pulse charging can improve battery lifetime while sustaining charging rates, offering a promising pathway to enhance durability in lithium-ion batteries for transport applications.

Wind power generation exhibits pronounced volatility and intermittency, and direct grid connection may cause instability in grid frequency. To address this issue, this paper proposes an optimisation strategy for hybrid energy storage systems to mitigate wind power fluctuations, integrating lithium-ion batteries with supercapacitors within wind power systems. Firstly, the grid-connected power of wind turbines and the reference power of the energy storage system are determined through dynamic weight adjustment using a weighted filtering algorithm combining adaptive exponential smoothing and recursive averaging algorithms. Secondly, the fish-eagle optimisation algorithm is employed to refine variational modal decomposition parameters. The modal components derived from decomposing the energy storage system’s reference power are converted into Hilbert marginal spectra. Following determination of the cut-off frequency, high-frequency signal components are managed by supercapacitors, while low-frequency components are handled by lithium-ion batteries. Finally, an optimised configuration model for the hybrid energy storage system is constructed to minimise the annual lifecycle target cost. Case study analysis demonstrates that this approach effectively smooths fluctuations in wind power output while fully leveraging the complementary characteristics of both energy storage types, achieving a balance between system economics and overall performance.

A stable solid electrolyte interphase (SEI), formed by the reductive decomposition of electrolytes at the anode surface, is crucial for ensuring the safety and long-term performance of lithium metal and post-lithium metal batteries. Conventionally, the lowest unoccupied molecular orbital (LUMO) energy of an ion-solvent complex has been used as the primary descriptor for predicting reductive decomposition kinetics and electrolyte stability. In this work, we critically evaluate this assumption and show that LUMO energies do not exhibit a direct linear correlation with reductive decomposition kinetics. Instead, the relationship between LUMO levels and reductive stability is inherently nonlinear across diverse electrolyte chemistries, limiting the general applicability of LUMO-based screening. To address this limitation, we developed machine learning (ML) models that use multiple structural and electronic structure parameters as input features and free energy barriers as the target quantity. The models are trained on free energy barriers obtained from density functional theory (DFT) calculations for 200 ion-solvent complexes spanning a wide range of lithium metal battery (LMB) and post-LMB electrolytes. These nonlinear models were found to significantly outperform traditional linear approaches based solely on the LUMO energy, yielding more accurate predictions of the reductive stability of electrolytes. Our findings highlight the need for multifeature, nonlinear models to capture the complexity of electrolyte reactivity and offer a computational framework to accelerate the rational design of stable electrolytes for next-generation battery technologies.

In response to the inherent limitations of single-battery low-voltage supply and the escalating demand for electrical equipment power in vehicles, the feasibility of replacing single-battery with battery/supercapacitor (SC) hybrid power supply (HPS) has been proposed and verified. This HPS directly connects a lithium-ion battery in parallel with SC and combines a bidirectional DC-DC power converter to boost the voltage to the required level. It can drive the vehicle to operate at low speeds and provide power for electrical equipment. A model based on MATLAB/Simulink is constructed and simulated using the Federal Test Procedure-75 driving cycles. In addition, experiments are carried out to verify the boost characteristics of the HPS. Furthermore, a case study is conducted to compare the proposed HPS with 14 V and 48 V single-battery systems under the China Light-duty Vehicle Test Cycle-Passenger Car operating condition. The results indicate that during frequent load fluctuations, the SC quickly responds to dynamic power and instantaneous high-power demands, which significantly reduces battery stress. The proposed HPS demonstrates key advantages by enhancing dynamic response capability, reducing battery stress to extend lifespan, and improving energy efficiency. This study provides a new solution for the practical application of low-voltage power supply in vehicles.

ABSTRACT The booming electric vehicle market is driving demand for lithium-ion batteries, particularly nickel-manganese-cobalt (NMC) batteries, yet also brings social risks. This study employed social life cycle assessment (S-LCA) to evaluate social risks in NMC battery production, use, and recycling. Following UNEP/SETAC guidelines, 10 indicators covered three stakeholder categories: workers, local communities, and society. The results show that production stage has prominent risks in drinking water coverage (DWC) and illiteracy, reaching 4.44E + 03 medium-risk hours (MRH) and 3.99E + 03 MRH, respectively. The use phase also presents significant social risks, particularly in terms of indicators, such as child labor, total (CL), fair salary, and unemployment (U), which account for over 85%. Although the recycling phase has a relatively low overall impact, the risks in DWC and illiteracy, total (I) indicators reach 30%. Substitution of secondary materials can reduce CL and unemployment by approximately 39% and 40%, respectively, but exacerbates issues related to DWC and health expenditures. It is projected that by 2050, the adoption of clean energy could reduce risks in the use phase by about 50%. Recommendations include optimizing cathode materials, advancing recycling technologies, and transitioning to clean energy to promote sustainable battery industry development.

Distribution of relaxation times (DRT) curves for lithium batteries facilitates the analysis of internal dynamics and polarization characteristics, offering substantial model interpretability. Furthermore, they serve as valuable tools for estimating battery states. However, current DRT curve calculation methods based on pulse data are constrained by device sampling rates and shelving times, limiting their ability to produce DRT curves corresponding to impedances in the middle- and low-frequency ranges. This paper employs a data-driven methodology to derive battery DRT curves, thereby circumventing the constraints of sampling frequency and shelving time on the scope of the DRT prediction. The accuracy of DRT curve predictions is enhanced through the development of an encoder-decoder network that integrates the benefits of convolutional neural networks (CNNs), long-short-term memory (LSTM) networks, and multihead self-attention (MSA) mechanisms, thereby improving the network's capacity to capture both temporal and spatial features, as well as to identify the weights and long-term dependencies of sequential data. The proposed method is validated using experimental test data sets and publicly available data sets. The findings indicate that a broader prediction range of DRT curves can be achieved, and the developed neural network enhances the forecast accuracy of the DRT curves. Moreover, the states of charge (SOC) estimation is corroborated by the predicted DRT curves, revealing that the SOC estimation aligns closely with results derived from actual DRT curves, demonstrating a high predictive accuracy. This method is beneficial for analyzing the correlation between polarization reactions and the SOC across various cells.

Sodium-ion batteries (SIBs) are attracting attention as cost-effective alternatives to lithium-ion batteries (LIBs) for large-scale energy storage. Among SIB cathodes, P2-Na0.67Ni0.33Mn0.67O2 delivers high capacity and rate capability but suffers from rapid capacity fading under high-voltage charging due to a detrimental P2-O2 phase transition and interfacial side reactions. Here, we demonstrate a dual-modification strategy combining Cu2+ doping and MgO surface coating to address these challenges. The dual-modified cathode (Na0.67Ni0.28Cu0.05Mn0.67O2@MgO) delivers markedly improved performance: a high-capacity retention of 90.88% after 200 cycles at 1 C and significantly enhanced rate capability (95.23 mAh g-1 at 10 C). Ex situ XRD analyses reveal that the P2-O2 phase transition is effectively suppressed, leading to minimal structural change during cycling. DFT calculations reveal that the Cu-MgO dual modification synergistically enhances the electronic conductivity of the electrode and suppresses transition-metal layer gliding. The results indicate that Cu2+ doping enhances structural stability by regulating Na+/vacancy ordering and suppressing the high-voltage phase transition, whereas the MgO coating alleviates electrolyte-induced surface degradation and enhances Na+ diffusion kinetics. This work offers a valuable reference for designing high-performance cathode materials in sodium-ion battery systems.

Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large design space composed of different solvents, additives, and salts, which is difficult to explore experimentally. High-fidelity molecular simulation can accurately predict the bulk properties of electrolytes by employing accurate potential energy surfaces, thus guiding the molecule and formula engineering. At present, the overly simplified classic force fields rely heavily on experimental data for fine-tuning, thus its predictive power on microscopic level is under question. In contrast, the newly emerged machine learning interatomic potential (MLIP) can accurately reproduce the ab initio data, demonstrating excellent fitting ability. However, it is still haunted by problems such as low transferability, insufficient stability in the prediction of bulk properties, and poor training cost scaling. Therefore, it cannot yet be used as a robust and universal tool for the exploration of electrolyte design space. In this work, we introduce a highly scalable and fully bottom-up force field construction strategy called PhyNEO-Electrolyte. It adopts a hybrid physics-driven and data-driven method that relies only on monomer and dimer EDA (energy decomposition analysis) data. With a careful separation of long/short-range and nonbonding/bonding interactions, we rigorously restore the long-range asymptotic behavior, which is critical in the description of electrolyte systems. Through this approach, we significantly improve the data efficiency of MLIP training, allowing us to achieve much larger chemical space coverage using much less data while retaining reliable quantitative prediction power in bulk phase calculations. PhyNEO-Electrolyte thus serves as an important tool for future electrolyte optimization.

Experimental, mathematical modelling and theoretical investigation of Ti and Sn Co-doped LiMn₂O₄; enhancing structural and electrochemical properties for cathode Lithium-Ion batteries

Lithium-ion battery formation is a pivotal step that dictates performance, longevity, and manufacturing safety. At the core of this process is the formation and evolution of the solid electrolyte interphase (SEI), whose nanoscale thickness, high environmental sensitivity, and dynamic behavior have long hindered direct characterization. Here, we exploit the SEI-induced refractive index matching effect and utilize operando optical microscopy to directly visualize SEI growth in real time. Our observations reveal pronounced lateral heterogeneity and asynchronicity during the first lithiation of graphite anodes. Surprisingly, high formation current promotes synchronized SEI growth, leading to a more uniform SEI coverage. Building on this mechanistic insight, we designed a pulsed high-current formation protocol for 2 Ah LiFePO4 (LFP)/graphite pouch cells, achieving nearly an order-of-magnitude reduction in formation time while simultaneously enhancing the cycling performance. These findings challenge the prevailing belief that low currents are essential for developing a uniform SEI and pave a path toward safer and more efficient large-scale battery production.

Silicon-based materials offer exceptional theoretical capacity for lithium-ion batteries (LIBs), but their practical application remains severely hindered by large volume expansion, low electrical conductivity, and unstable solid electrolyte interphase (SEI) formation during cycling. Herein, a binder-free silicon-containing carbon composite anode (denoted as CP-Si@C-4, where CP represents the conductive carbon paper substrate) is designed: carbon constitutes the structural and conductive framework, while silicon nanoparticles serve as a functional alloying component contributing characteristic lithiation/delithiation behavior. This framework comprises a conductive carbon paper (CP) scaffold, a resin-derived carbon matrix for homogeneous silicon dispersion, an interconnected carbon nanotube (CNT) network enabling long-range electron transport, and a conformal chemical vapor deposition (CVD) carbon layer for interfacial stabilization. Rather than simply increasing the overall carbon content, a series of control electrodes with distinct carbon configurations are deliberately designed to decouple the respective roles of bulk stress buffering and particle-level interfacial stabilization during cycling. The results indicate that functionally differentiating and coordinately regulating these two functions is critical for achieving durable binder-free silicon-containing carbon composite anodes. Benefiting from this cooperative multidimensional carbon architecture, the optimized CP-Si@C-4 anode delivers an initial Coulombic efficiency (ICE) of 86.3% and maintains a reversible capacity of ~990 mA h g−1 at 2 A g−1 after 1000 cycles. This work provides a structural design concept for improving long-term stability in binder-free silicon-containing carbon composite anodes.

Li-rich layered oxide materials (LLOs) represent a promising pathway to ultrahigh-energy lithium-ion batteries due to their exceptional theoretical capacity exceeding 250 mAh g-1. However, conventional O3-type LLOs suffer from severe voltage fade, rapid capacity decay, and poor rate capability, primarily caused by irreversible oxygen release, detrimental transition metal migration, and consequent structural degradation into spinel phases. Herein, we demonstrate that these challenges can be effectively mitigated through structural engineering by adopting an O2-type configuration in Li[Li0.125Ni0.125Co0.125Mn0.625]O2 (O2-LNCMO). The unique face-sharing coordination in the O2 structure inherently suppresses the transition metal migration into lithium layers, stabilizes the anionic redox activity, and inhibits oxygen release. This inherent structural stability, synergized with expanded interlayer spacing that facilitates rapid Li+ diffusion, enables exceptional electrochemical performance in O2-LNCMO. The material achieves minimal voltage decay of only 0.1 V over 100 cycles, alongside outstanding rate capability, delivering over double the capacity of its O3-type counterpart at high current densities. This work develops promising O2-type LLOs as high-energy cathodes and provides valuable insights into structural design strategies for next-generation lithium-ion batteries.

Abstract Lithium-ion batteries play an essential role in various applications, from portable consumer electronics to electric vehicles and energy storage systems but their safety remains a major concern for manufacturers and users. Understanding the thermal runaway (TR) phenomenon and the factors that influence it has therefore received considerable scientific attention. This study focuses on the impact of the experimental setup on the TR parameters. To investigate this, the TR behavior of an NMC 811 cell was tested in two setups with different volumes under two environmental conditions: air and vacuum that represent two extreme conditions for a closed calorimeter. In each case, the same methodology was applied, by heating the cell at 6 °C min −1 , and the measured and calculated parameters were compared between setups. Understanding the effects of the experimental device under different environmental conditions is essential to draw clear conclusions about the potential risks of Li-ion cells. Results show that both configurations give similar trend in the evolution of TR time, cell temperature at venting and TR, and the amount of gas ejected. The mass loss is affected by the setup. The composition of the ejected gases depends on the setup and cell environment. The TR energies measured in the large volume under vacuum were comparable to those in the small volume under air/vacuum, probably due to limited quantity of oxygen in both cases. This study provides a new perspective into TR behavior of Li-ion cell by exploring the impact of the setup and its environment.

This paper presents a comprehensive study on the electrochemical modelling of a Lithium-ion battery with nickel manganese cobalt (NMC) chemistry. The SIFD numerical solution for single particle model is derived to investigate the battery’s performance under different operational conditions. The parameters for 50 Ah NMC batteries have been identified by using particle swarm optimization. Experimental data from a 50 Ah NMC battery at 1/20 C, 1/3 C, and 1 C and cycle data up to 90% SOH are used to estimate parameters. The validity of the model is evaluated by comparing the results of proposed scheme with experimental data and PyBaMM. We get a maximum cell error of 20 mV and 60 mV, which is 0.47% and 1.428% of the nominal voltage. The diffusion coefficient D sk in solid electrodes tends to reduce when the number of cycles increases, which directly affects the battery performance. Furthermore, the volume of active material ( e k ) in both k = n , p become reduce when the number of cycles increases.

Abstract The growing demand for efficient energy storage systems in applications such as electric vehicles, smart grids, and portable electronics has intensified interest in high-performance lithium–metal batteries. Conventional fabrication routes for porous copper current collectors (CCs) face limitations in achieving complex architectures and reliable mechanical stability. In this work, stereolithography-based 3D printing combined with pressureless sintering is employed for the rapid fabrication of copper CCs. For the first time, porous copper CCs are fabricated using this approach, delivering controlled architectures with enhanced structural robustness and electrochemical functionality. Optimization of sintering parameters, including sintering temperature, heating rate, and holding time, was carried out using Response Surface Methodology based on a Box–Behnken design, followed by multi-objective genetic algorithm analysis in MATLAB. The optimized conditions significantly improved relative density, compressive yield strength, and volumetric shrinkage, while minimizing experimental effort. The fabricated porous copper CC exhibited superior mechanical strength under compression, withstanding ~ 35 MPa at 60% strain, ensuring integrity during coin cell assembly and cycling. Electrochemical testing demonstrated a stable and high Coulombic efficiency of approximately 95 percent over 100 cycles, significantly outperforming conventional copper foil. The porous structure effectively facilitated uniform lithium deposition, mitigated dendrite growth, and accommodated volume fluctuations. This research offers a scalable route to fabricate durable, high-performance CCs, advancing next-generation electrochemical systems with stable, high-surface-area electrodes. Graphical Abstract

Efficient identification of cathode chemistry in end-of-life lithium-ion batteries is essential for enabling effective battery recycling. Current approaches often rely on battery disassembly or time-consuming testing, limiting their practical use at scale. Here we report a rapid classification strategy based on X-ray fluorescence spectroscopy combined with statistical analysis. A reference dataset was established from high-quality elemental spectra collected from more than 100 end-of-life lithium-ion batteries. Statistical grouping was used to define cathode categories, which were validated by selective disassembly and complementary chemical analysis. The trained classification model was then applied to newly acquired spectra collected within seconds per battery, enabling fast identification without additional disassembly. The approach achieves high prediction accuracy across the studied dataset and demonstrates the feasibility of rapid cathode identification for battery recycling applications.

Direct recycling of end-of-life lithium-ion batteries cathode active materials by hydrothermal route.

In situ construction of poly(acrylic acid)/conjugated organic framework binder with superior mechanical strength and ionic conductivity for Si-based anodes in Lithium-ion batteries.

A sustainable route for turning metallurgical wastes into modified zinc ferrite used as anode in lithium ion batteries

Enabling electron redistribution via electron-deficient boron quantum dots confined in Ti3C2 MXene for fast lithium-ion storage.