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Its crucial importance to the long-term operation of lithium-ion batteries has made the solid electrolyte interphase (SEI) the subject of intensive research efforts. These investigations are challenging, however, due to the very complex and fragile nature of this layer. With its typical thickness being in the range of some 10 nm and its chemical make-up being highly sensitive to even the smallest amounts of impurities, it becomes clear that artifacts are easily introduced in investigations of the SEI, especially if the measurements are performed ex situ. To help ameliorate these issues, we herein report a combination of non-destructive operando techniques that can be employed simultaneously in the same electrochemical cell to provide a plethora of physical, morphological, and electrochemical data on the macroscopic and microscopic scale. These techniques encompass atomic force microscopy (AFM), electrochemical quartz crystal microbalance with dissipation monitoring (EQCM-D), and impedance spectroscopy (EIS). This work focuses on how to combine these techniques in a single electrochemical cell, which is suitable to study SEI formation while avoiding noise, crosstalk, inhomogeneous SEI formation, and other pitfalls.

A highly redox-active ternary nickel sulfide and cobalt-anchored carbon nanocomposite (NiS-Co@C) electrochemical electrode is synthesized by a two-step pyrolysis-hydrothermal method using biomass-derived carbon. The high-crystalline hierarchical porous nanostructure provides abundant voids and cavities, along with a large specific surface area, to improve the interfacial properties. The as-synthesized electrode achieved a specific capacity of 640 C g−1 at 1 A g−1, with a capacity retention of 93% over 5000 cycles, revealing outstanding electrochemical properties. Nickel sulfide nanoparticles embedded in the cobalt-anchored carbon framework improved redox activity, ion transport, and conductivity, resulting in a dominant diffusion-controlled battery-type behavior. Moreover, a hybrid supercapattery, based on battery-type NiS-Co@C as the positrode and capacitive-type activated carbon as the negatrode, achieved a maximum specific energy/power of 33 Wh kg−1/7.1 kW kg−1 with a 91% capacity retention after 5000 cycles. The synergistic effect of the combinatorial battery–capacitor behavior of the hybrid supercapattery has improved the specific energy–power considerably, leading the development of next-generation energy storage technologies.

Silicon-based anode materials are used to improve the performance of next-generation high-energy-density lithium-ion batteries (LIBs). However, the inherent limitations and cost of these materials are hindering their mass production. Commercial graphite can overcome the shortcomings of silicon-based materials and partially reduce their cost. In this study, a high-performance, low-cost, and environmentally friendly composite electrode material suitable for mass production was developed through optimizing the silicon content of commercial silicon–graphite composites and introducing a small amount of graphene and carbon nanofibers. This partially overcomes the inherent limitations of silicon, enhances the interface stability of silicon-based materials and the cycle stability of batteries, and reduces the irreversible capacity loss of the initial cycle. At a silicon content of 15 wt%, the initial Coulombic efficiency (ICE) of the battery was 65%. Reducing the silicon content in the composite electrode from 15% to 10% increased the ICE to 70% and improved the first lithiation and delithiation capacities. The battery exhibited excellent cycle stability at a current density of 0.1 A g−1, retaining approximately 65% of its capacity after 100 cycles, good performance at various current densities (0.1–1 A g−1), and an excellent reversible performance.

Despite their high specific capacity, magnetron-sputtered Si/Al thin films face rapid capacity decay due to stress-induced cracking, delamination, and detrimental electrolyte reactions. This study introduces a carbon-coated composite anode that overcomes these limitations, delivering superior reversible capacity, exceptional rate capability, and stable cycling performance. An electrochemical evaluation reveals that the CF-Si/Al@C-500-1h composite exhibits marked enhancements in capacity retention (43.5% after 100 cycles at 0.6 A·g−1) and rate capability, maintaining 579.1 mAh·g−1 at 3 A·g−1 (1 C). The carbon layer enhances electrical conductivity, buffers volume expansion during lithiation/delithiation, and suppresses silicon aggregation and electrolyte side reactions. Coupled with an aluminum framework, this architecture ensures robust structural integrity and efficient lithium-ion transport. These advancements position CF-Si/Al@C-500-1h as a promising anode material for next-generation lithium-ion batteries, while insights into scalable fabrication and carbon integration strategies pave the way for practical applications.

This study investigates a hybrid-battery thermal management system (BTMS) integrating air-cooling, a cold plate, and porous materials to optimize heat dissipation in a 20-cell battery pack during charging and discharging cycles of up to 5C. A computational fluid dynamics (CFD) model based on the equivalent circuit model (ECM) is developed to simulate battery pack behavior under various cooling configurations, including different porous media and vortex generators placed between cells. The impact of battery pack configurations on heat generation is analyzed, and five different porous materials are tested for their cooling performance. The results reveal that, among the examined materials, graphite is the most effective in maintaining the battery temperature within an acceptable range, particularly during high C-rate charging. Graphite integration significantly reduces the thermal stabilization time from over an hour to approximately 600 s. Additionally, our parametric experiment evaluates the influence of ambient temperature, airflow velocity, and cold-plate temperature on the system’s cooling efficiency. The findings demonstrate that maintaining the cold-plate temperature between 300 K and 305 K minimizes the temperature gradient, ensuring uniform thermal distribution. This research highlights the potential of hybrid BTMS designs incorporating porous media and cold plates to enhance battery performance, safety, and lifespan under various operational conditions.

Research on the thermal safety of lithium-ion batteries (LIBs) is crucial for supporting their large-scale application [...]

A priority area for low-cost LIBs is the commercial production of electrodes with a high cycle life and efficiency in an environmentally benign fashion and a cost-effective manner. We demonstrate the use of undoped/untreated, flexible, stand-alone, mesh-like carbon cloth (C-felt) as a potential alternative anode to commonly used graphite composite anodes (GRAs) in LIBs. The performances of commercial GRAs (9 m2/g) and C-felt (102 m2/g) were compared as anodes vs. LiFePO4 (14.5 m2/g) cathodes in the full battery. Half-cell test results determined appropriate mass ratios of 2:1 for GRAs (LiFePO4/GRA) and 1:1 for C-felt (LiFePO4/C-felt). At a 0.3 C discharge rate, the 1:1 ratio yielded a specific discharge capacity of 104 mAh/g, in contrast to 87 mAh/g for the 2:1 ratio for a full cell in the 100th cycle, corresponding to a retention of 82% for the 1:1 LiFePO4/C-felt full cell and 70% for the 2:1 LiFePO4/GRA full cell from their first specific discharge capacities. By varying the ratio of C-felt anode to LiFePO4 cathode in a full cell and expressing the specific capacity in the 100th cycle as a function of the fraction of C-felt present (at a fixed amount of LiFePO4), a maximum specific capacity was achieved at a fraction of C-felt equal to 0.542 or (1:1.18) LiFePO4/C-felt or 106 mAh/g. This corresponds closely to the experimentally determined value and supports (1:1) LiFePO4/C-felt full cell as an optimum ratio that can outperform the (2:1) LiFePO4/GRA full cell in our test conditions. Hence, we present C-felt anode as a potential cost-effective, lightweight anode material for low-cost LIBs.

The massive production and utilization of lithium-ion batteries (LIBs) has intensified concerns about raw material shortage and end-of-life battery management. The development of effective recycling/reusing strategies, especially for the valuable active positive electrode materials, has attracted much interest from both academia and industry. This study presents a comprehensive patent analysis on the recycling technologies of spent LIBs. We screened and examined 672 patent filings associated with 367 application families, covering the period from 1994 to 2024. The analysis reveals an explosive growth in patenting activity since 2020, with China and the United States leading in geographical coverage. Hydrometallurgy continues as the most patented recycling technology, followed by direct regeneration, separation, and pyrometallurgy. Key innovations focus on improving leaching efficiency, developing novel purification methods, and exploring various relithiation strategies. The study also highlights the significant involvement of both companies and academic institutions in driving innovation. Our findings provide insights into the technological landscape, identify emerging trends, and lead to the discussion of potential future developments in LIB positive electrode recycling. This analysis serves as a valuable resource for researchers, industry stakeholders, and policymakers working towards sustainable energy storage solutions and circular economy strategies in the battery sector.

Artificial Neural Networks (ANNs) improve battery management in electric vehicles (EVs) by enhancing the safety, durability, and reliability of electrochemical batteries, particularly through improvements in the State of Charge (SOC) estimation. EV batteries operate under demanding conditions, which can affect performance and, in extreme cases, lead to critical failures such as thermal runaway—an exothermic chain reaction that may result in overheating, fires, and even explosions. Addressing these risks requires advanced diagnostic and management strategies, and machine learning presents a powerful solution due to its ability to adapt across multiple facets of battery management. The versatility of ML enables its application to material discovery, model development, quality control, real-time monitoring, charge optimization, and fault detection, positioning it as an essential technology for modern battery management systems. Specifically, ANN models excel at detecting subtle, complex patterns that reflect battery health and performance, crucial for accurate SOC estimation. The effectiveness of ML applications in this domain, however, is highly dependent on the selection of quality datasets, relevant features, and suitable algorithms. Advanced techniques such as active learning are being explored to enhance ANN model performance by improving the models’ responsiveness to diverse and nuanced battery behavior. This compact survey consolidates recent advances in machine learning for SOC estimation, analyzing the current state of the field and highlighting the challenges and opportunities that remain. By structuring insights from the extensive literature, this paper aims to establish ANNs as a foundational tool in next-generation battery management systems, ultimately supporting safer and more efficient EVs through real-time fault detection, accurate SOC estimation, and robust safety protocols. Future research directions include refining dataset quality, optimizing algorithm selection, and enhancing diagnostic precision, thereby broadening ANNs’ role in ensuring reliable battery management in electric vehicles.

This paper proposes an early warning method for thermal runaway in sodium-ion batteries (SIBs) based on multidimensional signal analysis and redundancy optimization. By analyzing signals such as voltage, temperature, strain, and gas concentrations, Principal Component Analysis (PCA) is employed to evaluate the contribution of each signal and reduce data redundancy, while correlation analysis further refines the signal set by eliminating overlapping information. The optimized signals enable a stage-specific warning framework, which identifies distinct phases of thermal runaway progression with high precision. Experimental results validate the effectiveness of the proposed method, showcasing its potential for real-time monitoring and enhanced safety management of sodium-ion battery systems in critical applications.

The thermal runaway propagation (TRP) model of energy storage batteries can provide solutions for the safety protection of energy storage systems. Traditional TRP models are solved using the finite element method, which can significantly consume computational resources and time due to the large number of elements and nodes involved. To ensure solution accuracy and improve computational efficiency, this paper transforms the heat transfer problem in finite element calculations into a state-space equation form based on the reduced-order theory of linear time-invariant (LTI) systems; a simplified method is proposed to solve the heat flow changes in the battery TRP process, which is simple, stable, and computationally efficient. This study focuses on a four-cell 100 Ah lithium iron phosphate battery module, and module experiments are conducted to analyze the TRP characteristics of the battery. A reduced-order model (ROM) of module TRP is established based on the Arnoldi method for Krylov subspace, and a comparison of simulation efficiency is conducted with the finite element model (FEM). Finally, energy flow calculations are performed based on experimental and simulation data to obtain the energy flow rule during TRP process. The results show that the ROM achieves good accuracy with critical feature errors within 10%. Compared to the FEM, the simulation duration is reduced by 40%. The model can greatly improve the calculation efficiency while predicting the three-dimensional temperature distribution of the battery. This work facilitates the efficient computation of TRP simulations for energy storage batteries and the design of safety protection for energy storage battery systems.

The development and electrochemical characteristics of ionic liquid crystal elastomers (iLCEs) are described for use as electrolyte components in lithium-ion batteries. The unique combination of elastic and liquid crystal properties in iLCEs grants them robust mechanical attributes and structural ordering. Specifically, the macroscopic alignment of phase-segregated, ordered nanostructures in iLCEs serves as an ion pathway, which can be solidified through photopolymerization to create ion-conductive solid-state polymer lithium batteries (SSPLBs) with high ionic conductivity (1.76 × 10−3 S cm−1 at 30 °C), and a high (0.61) transference number. Additionally, the rubbery state ensures good interfacial contact with electrodes that inhibits lithium dendrite formation. Furthermore, in contrast to liquid electrolytes, the iLCE shrinks upon heating, thus preventing any overheating-related explosions. The Li/LiFePO4 (LFP) cells fabricated using iLCE-based solid electrolytes show excellent cycling stability with a discharge capacity of ~124 mAh g−1 and a coulombic efficiency close to 100%. These results are promising for the practical application of iLCE-based SSPLBs.

The performance of lithium-ion batteries deteriorates significantly under extreme-cold conditions due to increased internal resistance and decreased electrochemical activity. This study presents an experimental analysis of a battery thermal management system (BTMS) incorporating electromagnetic induction heating and a fluid-based heat transfer mechanism to alleviate these problems. The experimental setup utilizes a closed-loop circulation system where ethylene glycol-based fluid flows through induction-heated copper tubes, ensuring efficient heat transfer to an 18650-cell battery. This study evaluates heating performance under varying ambient temperatures (−15 °C and −5 °C), fluid flow rates (0.22, 0.3, and 0.5 L/min), and induction power levels (150 W, 225 W, 275 W, and 400 W). The results indicate that lower flow rates (e.g., 0.22 L/min) provide faster heating due to longer thermal interaction time with the battery; however, localized boiling points were observed at these low flow rates, potentially leading to efficiency losses and thermal instability. At −15 °C and 400 W, the battery temperature reached 25 °C in 383 s at 0.22 L/min, while at 0.5 L/min, the same temperature was achieved in 463 s. Higher flow rates improved temperature uniformity but slightly reduced heating efficiency due to increased heat dissipation. Internal resistance measurements revealed a substantial decrease as battery temperature increased, further validating the effectiveness of the system. These findings present a viable alternative for heating electric vehicle batteries in sub-zero environments, thereby optimizing battery performance and extending operational lifespan.

The persistent emphasis on safety issues in lithium-ion batteries (LIBs) with organic liquid electrolytes revolves around thermal runaway and dendrite formation. The high thermal stability and non-leakage properties of polymer electrolytes (PEs) make them attractive as next-generation electrolytes for LIBs. This study presents a blended terpolymer electrolyte (BTPE) membrane, integrating the high ionic conductivity of dual ion conducting polymer electrolytes (DICPEs) with the elevated lithium transference number (t+) of single-ion conducting polymer electrolytes (SICPEs). The BTPE was synthesized by blending PAA–PVA with lithiated acrylic acid (LiAA), lithiated 2–acrylamido–2–methylpropane sulfonic acid (LiAMPS), and a 2–hydroxyethyl methacrylate (HEMA)–based terpolymer, using lithium bis(fluorosulfonyl)imide (LiFSI) as the lithium salt. The synthesized BTPE showed excellent physical and electrochemical stability; it also exhibited an enhanced lithium transference number (t+ = 0.47) and high ionic conductivity (5.21 × 10−4 S cm−1 at 30 °C), attributed to the interaction between the FSI anion and the NH group of AMPS. This research presents an innovative strategy for the design of next-generation LIB electrolytes by integrating polymer electrolytes.

The electricity landscape is constantly evolving, with intermittent and distributed electricity supply causing increased variability and uncertainty. The growth in electric vehicles, and electrification on the demand side, further intensifies this issue. Managing the increasing volatility and uncertainty is of critical importance to secure and minimize costs for the energy supply. Smart neighborhoods offer a promising solution to locally manage the supply and demand of energy, which can ultimately lead to cost savings while addressing intermittency features. This study assesses the impact of different electric vehicle charging strategies on smart grid energy costs, specifically accounting for battery degradation due to cycle depths, state of charge, and uncertainties in charging demand and electricity prices. Employing a comprehensive evaluation framework, the research assesses the impacts of different charging strategies on operational costs and battery degradation. Multi-stage stochastic programming is applied to account for uncertainties in electricity prices and electric vehicle charging demand. The findings demonstrate that smart charging can significantly reduce expected energy costs, achieving a 10% cost decrease and reducing battery degradation by up to 30%. We observe that the additional cost reductions from allowing Vehicle-to-Grid supply compared to smart charging are small. Using the additional flexibility aggravates degradation, which reduces the total cost benefits. This means that most benefits are obtainable just by optimized the timing of the charging itself.