An improved MobileNet based on a modified poor and rich optimization algorithm for lithium-ion battery state-of-health estimation.

The integration of ultrahigh thermal stability, minimal thermal conductivity, and robust mechanical flexibility into a single thermal insulation material remains a critical challenge, especially for safeguarding against transient thermal extremes like lithium battery thermal runaway. This study presents an all-inorganic flexible membrane fabricated via a facile electrospinning technique, which strategically embeds hollow silica (SiO2) microspheres (HSMs) within a scaffold of SiO2 nanofibers (SNF). This design yields a 3D self-lubricating architecture that confers extraordinary mechanical durability, withstanding over 100000 bending cycles under 99% strain and 72 h of vibration without significant weight loss-a performance that surpasses conventional ceramics by orders of magnitude. Simultaneously, the composite membrane exhibits an ultralow and stable thermal conductivity of 31.39mW m-1 K-1, together with a high specific airflow resistance of 122.11 (kPa S m-1) mm-1, synergistically inhibiting heat conduction and convection. The SNF/HSMs composite membrane demonstrates exceptional thermal resilience, enduring long-term exposure at 1100°C and surviving drastic thermal shocks from 1300°C to -196°C. When evaluated in a practical flame test at 700°C, a mere 5-mm-thick membrane effectively maintains a low backside temperature of ≈160°C. This work establishes a groundbreaking design principle for high-performance, flexible thermal protection systems.

Disordered cathode materials are attractive candidates for next-generation lithium-ion batteries (LIBs), but the intrinsic instability of anionic redox hinders their commercialization. Unlike conventional Li-excess disordered systems limited by compositional constraints of Li1+xM1-xO2, Immm-Li2NiO2 offers a platform to access highly lithiated chemistries that enable in situ disorder formation during electrochemical cycling. This allows lattice O to contribute to charge compensation; however, O2 release at high voltages compromises reversibility and cycling stability. To address this, fluorination generates a quadrupolar Li-O-M-F configuration that lowers the Li─O─Li band energy level and delays the onset of anionic redox. This electronic structure modification suppresses O2 evolution, enhances structural stability, and improves cycling performance. By coupling electrochemically induced disorder with stabilization through Li-O-M-F units, this work establishes a new framework for engineering durable, high-capacity cathodes, offering a blueprint for material design strategies that transcend stoichiometric restrictions and unlock stable anion redox functionality.

Abstract Graphene is a prominent 2D nanomaterial which consists of a monolayer of sp²-hybridized carbon atoms arranged in a honeycomb lattice. It is widely used due to its extraordinary electrical conductivity, mechanical strength, thermal stability, and large specific surface area. These exceptional properties make it highly suitable for a wide range of applications, particularly in the fields of energy, biomedicine, and optoelectronics. This review provides a comprehensive overview of advanced graphene-based nanostructures, focusing on their synthesis methods, including top-down (mechanical exfoliation, chemical exfoliation, chemical synthesis) and bottom-up (CVD, pyrolysis, epitaxial growth), as well as key characterization techniques, including X-ray Diffraction, X-ray Photoelectron Spectroscopy, Raman Spectroscopy, Scanning Electron, Transmission Electron Microscopy, and Atomic Force Microscopy. Particular attention is placed on recent breakthroughs in energy applications, where graphene and its derivatives are utilized as a high-performance electrode material in supercapacitors, lithium-ion and magnesium-ion batteries, and photovoltaic cells due to their outstanding charge transport and storage capabilities. In biomedical applications, graphenebased materials are integrated into drug delivery systems, biosensors, photothermal platforms, and wearable devices due to their biocompatibility, functional surface chemistry, and structural versatility. Furthermore, graphene's optical transparency, tunable electronic properties, and flexibility position it as a leading material in the development of next-generation optoelectronic devices, including organic light-emitting diodes, transparent conductive electrodes, and highsensitivity photodetectors. Through the critical analysis of recent studies, this review underscores graphene's role as a platform material for multifunctional and scalable nanotechnologies and discusses future perspectives in tailoring its properties for applicationspecific performance in smart and sustainable systems.

Pretreating spent lithium-ion batteries

An improved variable forgetting factor sliding window recursive least square-chaotic firefly optimization method for key dynamic parameters identification of lithium-ion batteries with hybrid electrochemical empirical and circuit modeling

Effective thermal runaway (TR) detection is critical for the safety of lithium-ion battery packs, particularly in electric vehicles. However, deploying laboratory-validated methods into resource-constrained battery management systems (BMS) presents significant engineering challenges. This review surveys the state of the art in on-board TR monitoring, with an emphasis on the practical constraints of automotive applications. We first examine available precursor signals, including thermal, electrical, gas, and acoustic emissions, and evaluate their trade-offs regarding response speed and integration complexity. Second, diagnostic algorithms, from threshold-based logic to deep learning, are assessed against key performance metrics such as computational latency, false alarm rates, and lead time. Furthermore, the review discusses essential deployment considerations, including model compression techniques, inference hardware architectures, and compliance with functional safety standards. Specifically, the review discusses the implementation challenges of multi-modal data fusion, with a particular focus on the constraints imposed by limited hardware resources and long-term sensor reliability. Future directions regarding data standardization and cloud-edge collaboration are also discussed.

To enhance the electrochemical performance of silicon electrodes, it is essential to comprehensively understand their underlying lithium storage mechanisms. Unfortunately, the vast diversity of silicon anode types and compositions complicates efforts to accurately predict and validate these reaction processes. Accordingly, a structurally well-defined silicon-based model compound is in great need. Thus, we select methacrylate polyhedral oligomeric silsesquioxane (MAPOSS) as the subject for studying the lithium-silicon bonding mechanism due to its clear chemical structure and composition. Through detailed characterization of the morphological and chemical structural changes of MAPOSS before and after cycling, our results reveal an intriguing phenomenon: the synergistic interaction (here termed as "Dynamic Self-Organizing Lithium Bonds") between Si atoms in the core and carbonyl (C = O) groups in the side arms of MAPOSS promotes reversible dynamic Li+ ions storage. Density functional theory simulations further support this deduction. Furthermore, MAPOSS is employed as a binder in graphite anodes after polymerization. At 0.2 C, the resulting half-cell exhibits an impressive specific capacity exceeding 450mAhg-1 over 250 cycles. This study demonstrates that the integration of MAPOSS into the full cell configuration allows for a reduction in the N/P ratio and is expected to improve the overall energy density of the battery.

This work proposes a novel hybrid framework for real-time and embedded-compatible estimation of lithium-ion battery State of Health (SoH), integrating electrochemical insights with machine learning and physics-informed filtering. The novelty of this work lies in formulating a complete IC-driven virtual Electrochemical Impedance Spectroscopy (EIS) mechanism that estimates impedance parameters directly from time-domain charge data, eliminating any need for traditional frequency-domain excitation or EIS hardware. The approach introduces and utilises a virtual EIS module that emulates impedance diagnostics using Incremental Capacity (IC) curve features. Key impedance parameters—electrolyte resistance (Re) and charge transfer resistance (Rct)—are inferred via a Support Vector Regression (SVR) model optimized using the Cuckoo Search Algorithm (CSA), thereby removing the dependence on expensive, bulky, and non-deployable impedance instruments. These virtual impedance estimates, combined with terminal voltage and temperature readings, are utilized by an XGBoost regressor to predict SoH in a data-driven yet electrochemically grounded manner. To strengthen reliability, the proposed pipeline additionally incorporates quantitative performance validation, achieving Re MAE = 0.012 Ω, Rct MAE = 0.015 Ω, and SoH estimation with R 2 consistently above 0.95 across multiple NASA PCoE cells. Further to ensure temporal consistency and robustness under dynamic degradation conditions, an Enhanced Extended Kalman Filter (EKF) is applied, treating the ML output as a pseudo-measurement and refining it through recursive state estimation. This refinement reduces RMSE by up to 50% and improves R 2 score upto 0.965, ensuring degradation patterns that align with physical aging behavior. The pipeline is lightweight, modular, and suitable for real-time deployment in embedded Battery Management Systems (BMS). Since the proposed method require trivial measurements (voltage, temperature) and IC-derived features, it is well suited for microcontroller-based BMS hardware with limited computational resources, broadening its deployability across first-life EV packs, second-life stationary applications, and scalable onboard systems.

Metal-organic frameworks (MOFs), owing to their highly tunable structures, large specific surface areas, rich pore architectures, and diverse functionalities, have emerged as promising candidates for addressing environmental and energy challenges. With continuous advances in green synthesis techniques, eco-friendly applications of MOFs are progressively transitioning from laboratory research to real-world engineering. This review systematically summarizes recent progress in MOF applications across multiple green technology domains, including environmental remediation, sustainable energy conversion and storage, agricultural and food sciences, and healthcare. Emphasis is placed on the mechanisms and performance of MOFs in air pollution control, water treatment, photo/electrocatalytic water splitting and hydrogen storage, lithium-ion batteries and supercapacitors, pesticide delivery systems, food packaging materials, drug delivery, and bioimaging. Furthermore, key challenges facing practical MOF applications, such as material stability, regenerability, scalability in synthesis, and environmental safety, are critically analyzed. Prospects for future research directions are also outlined. This review aims to provide theoretical support and research guidance for the advanced application of MOFs in green chemistry, low-carbon energy, smart agriculture, and precision medicine, thereby promoting their further engineering implementation and industrialization within the framework of sustainable development.

The spinel compound LiNi0.5Mn1.5O4 (LNMO) has attracted increasing attention as a potential cathode material for high-energy lithium-ion batteries (LIBs) of the next generation. Despite its attractive properties, LNMO suffers from transition-metal ion dissolution and pronounced capacity fading, which significantly limit its practical implementation. To address these limitations, Zn is introduced into the LNMO structure to prepare Zn-doped LNMO, designed to stabilize the 16c and 8a sites and thus improve its electrochemical performance. This doping approach improves the structural robustness of LNMO and significantly suppresses Mn dissolution during electrochemical cycling. Even after 1000 cycles at a current rate of 1 C (1 C = 147mA g-1), the Zn-LNMO sample maintains 81.2% of its original capacity, demonstrating substantially improved capacity retention. Moreover, the Zn-LNMO electrode maintains 98.8% of its initial voltage after 1000 cycles, and the corresponding average decline in voltage is as low as 0.06mV for each cycle. This study establishes an atomically engineered doping concept that can be generalized to various cathode systems and serves as an effective guideline for designing high-performance LIBs.

Lithium-ion cells are in high demand worldwide due to the rise in EVs, green energy storage, and consumer electronic devices. Establishing a decentralized manufacturing ecosystem for LIB cells is essential as local public and private firms strive to become major participants in this sector. This research article focuses on the carbon footprint of producing current lithium-ion batteries (LIBs; LFP ̴152 kgCO 2 eq/kWh, NMC811 ̴ 205 kgCO 2 eq/kWh and NMC622 ̴202 kgCO 2 eq/kWh, respectively), discusses on different stage of sustainable manufacturing ecosystem, and investigates the carbon footprint dependency on decentralized manufacturing in any geographic area. As a result of this the overall emissions generated across all production steps of lithium-ion cells may be expected to reduce about 6%–7% by 2030.

The performance degradation of electronic power components during long-term operation can compromise system reliability and safety. Therefore, accurately predicting their remaining useful life (RUL) is critical for the reliability of safety-critical systems that utilize these components. This paper proposes a hybrid model integrating bidirectional long short-term memory networks (BiLSTM) and Gaussian process regression (GPR) for RUL prediction of electronic power components. The BiLSTM module provides high-precision point predictions, while the GPR module leverages the sequence features and trend information extracted by BiLSTM to deliver reliable interval predictions and high-confidence probabilistic outputs. The model’s predictive accuracy was validated using NASA’s publicly available lithium-ion battery dataset. Experimental results demonstrate that, compared to existing models, the proposed model achieves at least a 9.6% improvement in point prediction performance and a 63% improvement in interval prediction performance, fully validating the reliability and accuracy of the BiLSTM-GPR approach. The model was further applied to predict the RUL of DC-DC power modules. The predicted Continuous Ranked Probability Score (CRPS) reached a maximum of 0.050405, while the Probability Integral Transform (PIT) results exhibited a uniform distribution within the (0,1) range, further demonstrating the model’s high reliability and predictive confidence.

Effect of porous layers and array geometry on thermal management in air-cooled lithium-ion battery modules during high-rate cycling

Abstract This paper proposes a novel robust nonlinear observer (RNO) with learning capacity (LC) for state of charge estimation in lithium-ion batteries. The observer is designed via a convex optimization formulation that guarantees the input-to-state stability of the estimation error dynamics under exogenous disturbances. An auxiliary correction term, generated by a machine learning scheme, is incorporated to enhance the estimation performance. The learning mechanism employs a feedforward neural network that processes delayed measurements; however, the proposed structure can accommodate more complex learning mechanisms provided that the correction signal remains magnitude-bounded. The proposed scheme is validated through comprehensive simulations and practical experiments, with its performance benchmarked against well-established observers. Both numerical and experimental results demonstrate that the proposed observer outperforms the RNO without LC as well as other well-known observers.

Olivine LiFePO4 (LFP) is regarded as a particularly viable cathode for lithium-ion batteries and is distinguished by its remarkable thermal safety, long-term cycling performance, and favorable environmental profile. Nevertheless, the implementation of this material in high-power applications remains constrained by inherently poor electrical conductivity and sluggish kinetics of lithium-ion transport. Conventional carbon-coating strategies generally rely on external carbon sources, whereas this study proposes a novel dual-functional precursor approach using Prussian blue (PB, Fe4[Fe(CN)6]3) as both iron and carbon sources to synthesize carbon-coated LFP (LFP/C) via a one-step sintering process. Electrochemical evaluations show that the synthesized material displays an excellent initial discharge capacity of 160.3 mAh·g-1 at 0.2C and retains 119.1 mAh·g-1 even under a high-rate condition of 6C. Upon returning to 0.2C, the capacity rebounds to 160.9 mAh·g-1, reflecting exceptional rate performance and structural reversibility. Furthermore, capacity retentions of 96.7% and 84.3% are achieved after 500 cycles at 1C and 5C, demonstrating remarkable long-term cycling stability. These performance enhancements originate from the in situ formed carbon layer and highly ordered structure derived from the PB precursor. The former establishes an efficient electron-conducting network and suppresses particle growth, while the latter promotes the formation of uniformly distributed particles, facilitating electrolyte infiltration and lithium-ion transport. This work confirms that the use of PB as a dual-functional precursor constitutes a straightforward and efficient approach to synthesizing high-performance LFP/C, providing a new pathway for developing high-performance lithium-ion batteries.

Micro-sized silicon (μSi) is a promising anode for next-generation high-energy-density lithium-ion batteries (LIBs) due to its high capacity and excellent tap density. However, its severe volume fluctuations induce mechanical degradation and rapid capacity fading. Here, we develop a strain-adaptive design to construct hierarchical Si/graphene composite microspheres (DSMG@C) via scalable spray-drying and chemical vapor deposition (CVD). The architecture integrates an internal graphene scaffold, dual-scale (micro/nano) silicon, and a conformal ∼10 nm graphitic carbon shell, enabling an internal compliant framework with distributed microvoids coupled with an external conformal carbon confinement layer. The graphene-based framework and distributed microvoids accommodate local deformation, while nano-Si serves as an adaptive interstitial filler to densify contacts and disperse stress. The nano-Si disperses stress and fills voids to enhance densification, while the carbon shell reinforces mechanical stability and interfacial robustness. As a result, the DSMG@C anode delivers a high reversible capacity of 1062.8 mAh g-1 after 500 cycles at 1 A g-1, an initial coulombic efficiency of 90.8%, and a superior volumetric capacity owing to its 1.22 g cm-3 compacted density. Kinetic and mechanical analyses confirm its fast ion/electron transport and durable structural integrity. Full cells paired with LiFePO4 exhibit a discharge capacity of 123.4 mAh g-1 at 1 C after 200 cycles with an initial coulombic efficiency (ICE) of 92.7%, demonstrating strong practical potential. This work offers an effective strategy for designing high-performance Si-based anodes through multiscale structural engineering.

HighlightsAll-lifespan dynamic heat generation principles under fast charging are revealed.Overheating degraded cells induce irreversible aging and lower thermal stability.Degraded module suffers severe nonuniform heat accumulation, and degradation.Immersion cooling addresses All-lifespan ultra-fast charging overheating issue.Dynamic thermal management threshold is thermal-economic efficiently optimized.

Manganese molybdate (MnMoO4) has emerged as a promising electrode material for high-performance energy storage systems, owing to its high theoretical capacity and favourable electrochemical characteristics. Nevertheless, its practical application remains constrained by poor structural stability and a limited number of electroactive sites. To address these challenges, MnMoO4 sub-nano sheets (MnMoO4 SNSs) are synthesized by the co-assembly approach using Mn salts, phosphomolybdic acid (PMo12), and oleylamine (OAm). Due to the high specific surface area, structural flexibility, adaptive properties, and electron delocalization of sub-nano structures, the obtained MnMoO4 SNSs deliver a high reversible specific capacity of 965 mAh g-1 at 0.18 A g-1, and demonstrate remarkable cycling durability. This work presents an effective strategy for developing structurally stable sub-nanometer MnMoO4 electrodes and provides valuable insights for the rational design of next-generation high-energy-density storage materials.

Biomimetic spatially graded electrolytes: facilitating rapid ion conduction and dendrite-mitigated operation in solid-state lithium batteries.