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Wearable electronic energy storage devices usually require the integration of flexible mechanisms and uncompromising electrochemistry of lithium-ion batteries (LIBs). Most of the studies in this field have mainly focused on enhancing the electrochemical properties of the material, while the significance of its flexibility and mechanical properties has been relatively ignored. In this study, the 20 wt % carbon fibers (CFs) composite N-doped flexible carbon nanofiber paper (NFCFP) was prepared through a facile PFI refiner and Bauer sieve of pulping and papermaking machinery sieve-assisted methods combined with annealing technology. The NFCFP-700 with high pyridine N content shows good ion transport and storage performance, the specific capacity remained 308 mAh⸱g−1 after 100 cycles at 0.1 C, and even at 1 C, the specific capacity of the electrode material instead increases constantly at 142 mAh⸱g−1. According to density functional theory (DFT) calculations, the coexistence of pyridine N content and defects in carbon materials was found to enhance the adsorption energy (E ad) of lithium, and the N4V2 locally show the most negative electrostatic potential (ESP) distribution, which was more favorable for the adsorption of Li+ being positively charged. This insight from DFT studies combined with experimental data emphasizes the favorable impact of pyridine N content and defects on the lithium adsorption process in carbon materials, also providing valuable information for the design and optimization of materials for lithium storage applications. [ABSTRACT FROM AUTHOR]

Cellulose is now considered as an appealing environment friendly material for the development of sustainable separators for rechargeable batteries. This study thus aims to develop high performance paper based ceramic separators with superior electrolyte wettability (>170 %), high thermal stability (>200 °C) and excellent flame safety. While developing paper based ceramic separator, extensive studies are carried out to understand thenature and functionality of different nano-structured ceramic materials (Al 2 O 3 , BaTiO 3 and ZrO 2) impregnated in paper matrix. Nano-ZrO 2 facilitates an effective pathway for Li +-ion transport (t Li + = 0.51), whereas BaTiO 3 and Al 2 O 3 ceramics show moderate Li +-ion transport properties (t Li+ of 0.29 and 0.30 respectively). Electrochemical Impedance Spectroscopy (EIS) measurement clearly reveals that ZrO 2 exhibits more interfacial compatibility with the electrodes (MCMB and LiNi 1/3 Mn 1/3 Co 1/3 O 2) compared to the other two ceramic separators during full cell operation. The developed paper separators show excellent electrochemical properties in terms of cycling (tested 300 cycles), multi-electrode compatibility and rate capabilities at different current densities of 0.1–1.2 mAcm−2. The pre- and post-electrochemical EIS data reveal that ZrO 2 based impregnation offers significantly lower charge transfer resistance compared to the other two separators. The study thus demonstrates a real promise for its successful application in Li-ion battery towards achieving sustainability and safety. [Display omitted] • Sustainable, flexible and high safety ceramic based paper separators for LIBs. • Separators were developed using easy to scale up wet-coating process. • Ceramic impregnation affects physical and electrochemical properties of the separators. • Paper separators show superior electrolyte wettability and high thermal stability. • Better electrochemical performances and flame safety are other important advantages. [ABSTRACT FROM AUTHOR]

• A method is proposed to find sleeping beauty papers and their princes in technology. • This method could consider both direct and indirect citations. • 22 sleeping beauty papers in technology are detected. • 9 sleeping beauty papers are among the top 10 papers with the highest contribution. • The princes of 9 typical sleeping beauty papers in technology are all papers. In science-technology research, papers and patents are used to represent science and technology, respectively. Detecting sleeping beauty papers and their princes in technology (patent field) could uncover dynamic knowledge contributions from science (paper field) to technology (patent field). However, previous studies have mainly focused on sleeping beauty in science. Some studies have examined SB patents in technology, but SB papers in patents are rarely studied and need to be further discussed. In addition, knowledge could flow along citations. Thus, if one paper is cited by one patent's reference (indirect citation), it also contributes to the patent, even though the patent does not directly cite it. At the same time, indirect citations are rarely discussed in sleeping beauty studies. This could lead to a loss of significant information. Therefore, to reveal the dynamic knowledge contribution from science to technology considering indirect citations, this study proposed a new method of mining sleeping beauty papers in technology and their princes. The lithium-ion battery domain is selected as a case study. The findings are as follows: (1) Most papers do not contribute knowledge to technology continuously, even when considering indirect citations, and the time-varying knowledge contribution strength changes significantly overtime. (2) The knowledge contribution strength with a time delay of more than 11 years occupies 80% of the total knowledge contribution strength. It is suggested that the window period of paper publication evaluation be extended. (3) 22 sleeping beauty papers in technology are detected. Nine papers are among the top 10 regarding the total knowledge contribution strength. (4) The princes of 9 typical sleeping beauty papers in technology are all papers. This implies that the awakening of these papers in technology was all provoked by scientific development. [ABSTRACT FROM AUTHOR]

MoO 3 -based anodes for lithium storage have attracted more attentions due to the relatively high specific capacity of 1117 mAh g–1 and good chemical stability. Compound MoO 3 with other functional materials and construction of unique micromorphologies have been proved to be efficient strategies for improving the electrochemical performances of MoO 3 -based anodes. Herein, MoO 3 /TiO 2 composites were successfully biosynthesized with different micromorphologies. In the preparation processes, the hydrothermal and solvothermal treatments were carried out successively in the presence of cellulosic filter paper as the biotemplate, followed by calcination of the as-obtained composites in air, resulting in the final MoO 3 /TiO 2 composite materials. The two microstructures of the MoO 3 microfiber with entire TiO 2 nanoparticles coating and the MoO 3 microrod formed by stacking the microsheets with loosely TiO 2 nanoparticles covering were prepared under different pH values of the precursor. Compared with the bare MoO 3 materials, the corresponding MoO 3 /TiO 2 composites showed enhanced electrochemical performances as anode materials for lithium-ion batteries. Especially the MoO 3 /TiO 2 composite with core-shell microfibrous structure derived from cellulosic filter paper presented relatively better test results for lithium storage, delivering an initial discharge capacity of 1297.6 mAh g−1 with the first Coulombic efficiency of 65.8%, and a reversible specific capacity of 403.6 mAh g−1 after 200 cycles. It is demonstrated that a well-defined TiO 2 coating layer can effectively inhibit the volume variations of MoO 3 matter in the lithiation/delithiation processes. Combined with the porous microfibrous structure that alleviates the stress, provides more reaction sites and reduces diffusion paths of lithium-ion, a promising MoO 3 -based anode material was achieved. This work provides a facile biotemplate-based strategy for the design and fabrication of metal oxide-based composites with adjustable microstructures for energy storage. [Display omitted] • Cellulosic filter paper derived MoO 3 /TiO 2 composites with variable micromorphologies were biosynthesized. • Microfibrous MoO 3 was coated by TiO 2 nanoparticles uniformly using the precursor with original pH value. • It showed enhanced reversible capacity, cycling stability and rate capability as an anode material for lithium-ion batteries. • The microstructural feature of biotemplate and the entire TiO 2 coating contribute to the improved lithium storage property. [ABSTRACT FROM AUTHOR]

Separators are indispensable components in lithium-ion batteries (LIBs), providing efficient pathways for lithium ions to travel and isolating the positive and negative electrodes to avoid short circuits. However, traditional polyolefin-based separators exhibit inferior electrolyte affinities, limited porosities, and low thermal stabilities. In this study, a novel method was developed to prepare chitosan micro/nanofiber membranes as LIB separators using natural materials. The pore sizes of the chitosan micro/nanofibers separators were modulated by changing the diameters of the chitosan fibers. The results demonstrated that the chitosan nanofiber separators (CSNFs) had superior electrolyte uptake (281 %), excellent thermal dimensional stability, and electrochemical performance in LiFePO 4 /Li half-cell, as indicated by the higher discharge capacity after 100 cycles, and higher rate capacity than commercial Celgard2325 separator. This study paves the way for the fabrication of eco-efficient and environment-friendly separators for high-performance LIBs. [Display omitted] [ABSTRACT FROM AUTHOR]

• Variation of Young's modulus of Ga in the process of discharge (lithiation) was measured. • Self-healing ability of EGaIn to automatic repair the cracks resulted from charge/discharge. • The CNF/CNT@EGaIn NPs anode is free-standing, flexible and binder-free. Gallium-based liquid metals are considered as potential anode materials for lithium-ion batteries owing to their self-healing, non-poisonous advantages, as well as high theoretical capacity. However, due to the alloying/dealloying reaction mechanism to store lithium, Gallium-based alloys face huge volume change resulted in poor cycle life. Furthermore, the poor wettability of liquid metals on many kinds of substrates (carbon materials, stainless steel, etc.) and their strong ability to form alloys with many metallic current collectors (Cu, Al foil) make them hard to be designed into electrode with excellent electrochemical performance. One objective of this paper is to investigate Young's modulus of Ga discharged to different cut-off potentials, in order to gain a further understanding of mechanical property attenuation of Ga in the process of lithiation. Moreover, a three-dimensional (3D) free-standing electrode is fabricated by confining Ga-based liquid metal, EGaIn, in the matrix of carbon nanofibers/carbon nanotubes paper (CNF/CNT@EGaIn NPs). The network structure provides effective pathways for electrons and ions, as well as enough space to contain the volume expansion of EGaIn. The dense CNT layer plays a further role on preventing EGaIn from shedding of the conductive substrates. Obtained CNF/CNT@EGaIn NPs exhibits a good ionic/electronic conductivity and mechanical stability, delivers capacity of 351 mAh g−1 at 1.6 A g−1 and remains a reversible capacity of ∼420 mAh g−1 after 100 cycles at 800 mA g−1. This work could provide valuable insights into the development of Ga-based liquid metal anodes. [ABSTRACT FROM AUTHOR]

To increase the conductivity and overcome the solubility and agglomeration of polyoxometalate (POMs), herein, paper ball-like graphene (PBG, 100 mg) anchoring different amount (200 mg, 600 mg, 900 mg) of H 3 PMo 12 O 40 (PMo 12) nanocomposites (donated as PBG@PMo 12 (n) (n = 1, 2, 3, respectively)) were successfully fabricated, in which the PBG can not only improve conductivity of PMo 12 but also effectively prevent PMo 12 aggregation. Moreover, the PBG and PBG@PMo 12 (n) nanocomposites were characterized by PXRD, FT-IR, SEM and TEM. As a consequence, when the PBG@PMo 12 (n) (n = 1, 2, 3) nanocomposites were used as the anode materials for lithium-ion batteries (LIBs), PBG@PMo 12 (n) (n = 1, 2, 3) exhibit superior behavior of energy storage, especially, PBG@PMo 12 (2) exhibits a high reversible capacity of 583.2 mA h g−1 up to a cycle life of 100 cycles, being better than those of matrix and others. The results indicate that the PBG can provide a favored contact between POMs and electrolyte, resulting in the enhanced capacity. [Display omitted] • Paper ball-like graphene (PBG) anchoring different amount of H 3 PMo 12 O 40 were successfully fabricated. • In the PBG@PMo 12 (n), the PBG can not only improve conductivity of PMo 12 , but also effectively prevent PMo 12 aggregation. • PBG@PMo 12 (2) exhibits a high reversible capacity, being far greater than that of PMo 12. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel aramid nanofibers reinforced cellulose membrane was developed as an advanced LIB separator. • The large pores and insufficient thermomechanical properties of cellulose separator was improved. • The influence of aramid nanofibers on the comprehensive performance of cellulose composite separator was presented. • The composite separator achieved well balance between high safety and excellent electrochemical performance. Commercial polyolefin separators suffer from poor electrolyte wettability and low thermal stability, which give rise to unsatisfied electrochemical performance and severe safety hazards for lithium-ion batteries (LIBs). Herein, a novel paper-based composite separator composed of electrolyte-affinitive cellulose fibers (CFs) and thermally stable aramid nanofibers (ANFs) was successfully fabricated through the traditional papermaking method. The ANFs as a functional nanofiller played crucial roles in improving the defects of pure CF separator including large-sized pores, low mechanical strength and high flammability. Specifically, the CF/ANF composite separator with 20 wt% ANFs (CF/ANF-20) possessed narrow micropores, satisfied tensile strength (33 MPa), excellent thermal resistance (without dimensional shrinkage up to 200 °C) and flame retardancy, which greatly enhanced the safe operation of battery. Moreover, inheriting from the highly porous structure and exceptional electrolyte affinity of CF separator, the CF/ANF-20 composite separator exhibited appropriate porosity and superior electrolyte wettability, which brought about a high electrolyte uptake (157%), thus endowing it with better ionic conductivity (0.75 mS cm−1) and lower interfacial resistance compared to commercial polypropylene separator. Accordingly, the LiFePO4/Li half cells using CF/ANF-20 separator delivered outstanding rate capability and stable cycling performance. All results indicate that the CF/ANF-20 separator shown great balance between the electrochemical performance and safety is an intriguing candidate for advanced LIBs. [ABSTRACT FROM AUTHOR]

Recycling lithium-ion batteries (LIBs) has gained prominence in the last decade due to increasing supply chain constraints for critical materials (such as lithium and cobalt) and policy shift toward increased circularity of materials to mitigate environmental concerns. Conventional recycling methods (e.g., pyrometallurgical techniques) are suboptimal because of high-temperature (>1400 °C) processing with recovery yields ranging from 50% to 85%. On the other hand, optimal preprocessing/pretreatment of end-of-life (EoL) LIBs results in a) high (>90%) recovery yield, b) lower temperature processing (lower environmental footprint), c) high potential for commercial returns of materials, and d) lower safety risks. This paper reviews major preprocessing methods, including sorting, stabilization, dismantling and comminution, and separation for spent LIBs. The capabilities of major recycling firms and preferences for preprocessing in recycling methods are also reviewed, highlighting research and development (R&D) initiatives to allow more efficient and cleaner solutions for recycling spent LIBs. The industry-wide state-of-the-art recycling process is also detailed based on global practices, focusing on the highest yields and lowest environmental footprint. Finally, this paper provides policy recommendations to enable sustainable recycling of LIBs on a global scale, consequently reducing the environmental Impact of waste material and addressing the growing need for LIBs as a result of the increased demand for electric vehicles and stationary storage. [Display omitted] • This paper addresses waste management of Li-ion batteries (LIBs). • Outlines the pros and cons of the preprocessing approach in LIB recycling. • Reviews the commercial preprocessing techniques used by LIB recyclers. • Proposes an optimized recycling method to increase element recovery. • Highlights (and proposes) new regulations to make preprocessing viable. [ABSTRACT FROM AUTHOR]

Although SiO 2 as an anode for lithium-ion batteries has an attractive high theoretical capacity (1965 mAh g−1), violent volume changes and poor electrical conductivity during alloying reaction limit its lithium storage performance. Here, we report a SNTs@NC composite anode loaded on flexible CNTs conducting paper (CP). The conductive network of CP promotes the rapid migration of Li ions and electrons, and the multi-dimensional skeleton formed by CNTs and SNTs@NC is conducive to the steady deposition of Li-ions. The excellent flexibility effectively defuses the impact of internal stress on the pole plate structure, showing a longer service life and better cycling stability compared with the traditional copper foil. The composite anode has the advantages of simple preparation, low cost and little environmental pollution, and as an innovative collector, CP effectively eliminates the influence of the volume effect of the anode on the electrochemical performance. This work may provide a novel way for the commercial development of SiO 2 anode or the preparation of flexible self-supporting electrode. [Display omitted] [ABSTRACT FROM AUTHOR]

• A columnar liquid-crystalline triazine-based dendrimer has been incorporated as Li-ion battery anode. • The additional 1% carbon filler (CNT paper) in the triazine-based anode successfully boost the battery performance. • Systematical spectroscopic studies reveal that Li+ insertion in triazine and piperazine functional groups plays an important role in the Li+ storage mechanism. • The charge storage evaluation and kinetical studies unleashed the role of carbon filler on boosting capacitive contribution and accelerating Li+ transport. Owing to their highly tunable molecular structure and chemical stability, dendrimers consisting of triazine and piperazine moieties have been investigated. The present work describes the electrochemical performance of a columnar liquid-crystalline triazine-based dendrimer containing the linker with diaminopropane (DAP) as a lithium-ion battery anode. To elucidate the impact of conductive carbon filler in the electrode, a tiny amount of carbon nanotube (CNT) paper has been incorporated in the DAP anode. The battery performance revealed that the DAP anode has shown a high specific capacity of up to 444 mA h g−1 at 0.5 A g−1 after 200 cycles. Surprisingly, with an additional 1% CNT, the Li+ storage capacity of the dendrimer-based composite anode (DAP/CNT1%) significantly enhanced up to 656 mA h g−1. To the best of our knowledge, this is the first time that a triazine-based dendrimer has been utilized as an anode material in conjunction with a small amount of CNT to further boost its energy-storing capabilities through a synergy of capacitive storage mechanisms and improve Li+ transport. The efficient lithium storage of DAP/CNT composite further shows its amenability as organic electrode material for next-generation organic Li-ion batteries. [Display omitted] [ABSTRACT FROM AUTHOR]

• CaCl 2 is an additive suitable for pyrometallurgical recovery of spent LIBs. • CaCl 2 has good chlorination ability and slag optimization ability. • SiO 2 and CO 2 in the system contribute to the increase in HCl yield. The difficulty of separating Li during pyrometallurgical smelting of spent lithium-ion batteries (LIBs) has limited the development of pyrometallurgical processes. Chlorination enables the conversion of Li from spent LIBs to the gas phase during the smelting process. In this paper, the effects of four solid chlorinating agents (KCl, NaCl, CaCl 2 and MgCl 2) on Li volatilization and metal (Co, Cu, Ni and Fe) recovery were investigated. The four solid chlorinating agents were systematically compared in terms of the direct chlorination capacities, indirect chlorination capacities, alloy physical losses and chemical losses in the slag. CaCl 2 was better suited for use as a solid chlorinating agent to promote Li volatilization due to its excellent results in these indexes. The temperature required for the release of HCl from MgCl 2 , facilitated by CO 2 and SiO 2 , was lower than 500 °C. The prematurely released HCl failed to participate in the chlorination reaction. This resulted in approximately 12 % less Li volatilization when MgCl 2 was used as a chlorinating agent compared to when CaCl 2 was used. In addition, the use of KCl as a chlorinating agent decreased the chemical dissolution loss of alloys in the slag. The performance of NaCl was mediocre. Finally, based on evaluations of the four indexes, recommendations for the selection and optimization of solid chlorinating agents were provided. [ABSTRACT FROM AUTHOR]

• Physics-Constrained Bayesian Neural Network. • Failure prognostics of lithium-ion batteries. • Comparative study of physics-based, data-driven, and physics-constrained models. In order to predict the remaining useful life (RUL) of lithium-ion batteries, a capacity degradation model may be developed using either simplified physical laws or machine learning-based methods. It is observed that even though degradation models based on simplified physical laws are easy to implement, they may result in large error in the application of failure prognostics. While data-driven prognostics models can provide more accurate degradation forecasting, they may require a large volume of training data and may invoke predictions inconsistent with physical laws. It is also very challenging for existing methods to predict the RUL at the early stages of battery life. In this paper, we propose a Bayesian physics-constrained neural network for battery RUL prediction by overcoming limitations of the current methods. In the proposed method, a neural differential operator is learned from the first 100 cycles of data. The neural differential operator is modeled with a Bayesian neural network architecture that separates the fixed history dependence from the time dependence to isolate epistemic uncertainty quantification. Using the battery dataset presented in the paper by Severson et al. as an example, we compare our proposed method with a simplified physics-based degradation forecasting model and two data-driven prognostics models. The results show that the proposed physics-constrained neural network can provide more accurate RUL estimation than the other methods with the same group of training data. Most importantly, the proposed method allows for RUL prediction at earlier stages of the battery life cycle. [ABSTRACT FROM AUTHOR]

Biomass-derived carbon materials are considered potential lithium-ion anode materials because they are sustainable, renewable, economical, and environmentally friendly and have tunable microstructures. However, biomass-derived carbon materials have low specific capacity and show capacity degradation during long-term electrochemical cycling, which prevents their application in high-performance lithium-ion batteries. Hence, in this paper, nitrogen-doped hierarchical porous carbon (NHPC) is synthesized from a biomass waste material, corncob, through one-step activation and carbonization procedures. N-doping in the NHPC modifies the electron distribution on the surface of carbon, resulting in the generation of additional lithium-ion storage sites and enhancing electrode wettability to increase the conductivity. Additionally, the doping procedure results in a hierarchical porous structure with an increased specific surface area and pore volume, which facilitates ion transport and provides remarkable cycling stability. The electrochemical performance of NHPC is improved by the jointly beneficial combination of N-doping and the hierarchical porous structure. The optimized NHPC electrode provides a reversible specific capacity of 700 mAh g−1 after 100 cycles at 100 mA g−1. This study provides a simple and efficient method for improving the electrochemical properties of biomass-derived carbon materials through elemental doping and modulation of microscopic morphology and structure. • Nitrogen-doped hierarchical porous carbon was derived from biomass waste corncob. • The generated mesopores increased specific surface area and pore volume. • The combination of N-doping and hierarchical porous structure improved Li+ storage. • The large capacitive contribution in the mechanism resulted in good cycling stability. [ABSTRACT FROM AUTHOR]

The paper proposes the analysis of four different scenarios of renewables penetration (40%, 60%, 80%, and 100%) with the most affordable generators: photovoltaic panels and wind turbines. The synergy of two storage technologies, such as Li-Ion batteries and the hydrogen power-to-power solution (electrolyzer, H 2 tank, and fuel cells), is evaluated to ensure the balance of the power-grid. The paper discusses the numerical Trnsys-based modeling of the smart grid. A detailed performance evaluation of each component, according to operational maps provided by manufacturers, has been performed to meet an electrical load (10 MW peak - 54 GW h annual). The results of the analysis show the crucial role of the H 2 system in the goal of achieving a higher renewable fraction, mainly due to the possibility of seasonal storage without the self-discharge limitation of batteries. In the 100% renewable solution the contribution of H 2 is equal to 30% of the electric load. • The synergy of Li-Ion batteries and hydrogen is evaluated to ensure grid balance. • Hydrogen integration has been evaluated in the path to 100% renewable energy. • The optimum solution is a tradeoff between renewable share and system cost. • A fully renewable system would cost $0.42/kWh with a 30% hydrogen contribution. • Hydrogen power-to-power system is strongly limited by high cost. [ABSTRACT FROM AUTHOR]

Among the global carbon emissions in different fields, the carbon emissions in the transportation sector account for more than 1/4. Only the development of passenger car technology makes it difficult to achieve the climate goals. E-bike is considered to be one of the effective solutions. Hydrogen energy, as one of the representatives of new energy, has the advantages of cleanliness, high efficiency, and high energy density. Therefore, in this paper, a long-range hydrogen-electric hybrid bike and its energy management strategy are designed to achieve optimal performance. Herein, two energy management strategies, including the threshold-based and fuzzy logic optimized threshold-based strategy, are designed and compared by evaluating the parameters of equivalent hydrogen consumption, state of charge fluctuation, and the final state of the control system. The results show that with an initial SOC of 50% for Li-ion battery, the fuzzy logic optimized threshold-based strategy reduces equivalent hydrogen consumption by up to 16% compared to the threshold-based strategy. Besides, the fluctuation of the Li-ion battery's state of charge could be reduced by up to 54.7%, indicating that the fuzzy logic optimized threshold-based strategy is a promising energy management strategy for fuel cell hybrid E-bike. [Display omitted] • A long-range hydrogen-electric hybrid E-bike with metal hydride hydrogen storage is designed. • A fuzzy logic optimized threshold-based energy management strategy (EMS) is proposed. • Li-ion battery SOC fluctuations could be reduced by up to 54.7% via fuzzy logic optimized EMS. • Fuzzy logic optimized EMS shows self-adaptability and economic H 2 consumption for hybrid E-bike. [ABSTRACT FROM AUTHOR]

• A life cycle analysis on recycling and secondary use of lithium-ion batteries. • Based on the recycling in China, the LCA of different methods has been established. • Compared to other recovery, the secondary use has the lowest environmental impact. • Secondary use has the greatest impact on assessment results in dynamic situations. With the rapid development of the global new energy vehicle industry, how to minimize the environmental impact of the recovery has become a common concern and urgent concern. China is a major production and consumption market for electric vehicles, there are no specific and extensive resource and environmental assessment system for batteries. In this paper, the retired Electric vehicles lithium-ion batteries (LIBs) was the research object, and a specific analysis of the recycling treatment and gradual use stages of power batteries were based on life cycle assessment. Different battery assessment scenarios were established according to the development of battery recycling in China. The results showed that the secondary use has the optimal performance compared to the full-component physical, pyrometallurgical and hydrometallurgy recycling. The results showed that direct recycling has a GWP of 0.037 kg-CO 2 eq·kg LIB-1, which is lower than others. Secondary use of LIB accounts for the most emission reductions with Global warming (GWP) as 12.134 kg-CO 2 eq·kg LIB-1. The secondary use has the greatest impact on the assessment results, especially in dynamic scenarios. Through a comprehensive comparison of different recycling technologies, the secondary use, increasing the recycling rate, reducing resource, energy consumption and pollution emissions. [ABSTRACT FROM AUTHOR]

Several factors are influencing the spread of Electric Vehicles (EVs) in the automotive market. However, while battery-electric vehicles emit no tailpipe emissions, the manufacturing phase, particularly the manufacturing of the battery packs, can have significant environmental impacts. In addition, as the EV market expands, there will be a significant increase in demand for critical materials used in lithium-ion batteries, such as lithium, cobalt, and nickel. These materials are essential for producing high-performance batteries, and their global demand is expected to rise rapidly to meet the demands of the expanding market. This paper investigates the main challenges that need to be tackled to reach a sustainable path in the battery industry. A cradle-to-gate boundary is set to focus on raw material extraction, production of precursors, cell and module production, and battery pack assembly. In addition, because 7.8 million tons of EV batteries per year are expected to reach the end-of-life phase by 2040, a brief overview of the recycling issue is provided to investigate the potential usage of recycled material in the early stages of battery production. [ABSTRACT FROM AUTHOR]

To address the limitations of battery-powered unmanned surface vehicles (USVs) in terms of endurance mileage, this study introduces proton exchange membrane fuel cells (PEMFCs) into the power system of USVs. This paper focuses on the design and testing of a hybrid power system and its energy management strategy for a lightweight USV. The test results demonstrate that the hybrid power system effectively fulfills the power requirements of the USV across various operational conditions. Specifically, the amplitudes of bus voltage overshoot and undershoot remain below 0.31 V even during sudden changes in working conditions. The energy management strategy, in conjunction with an auxiliary battery, successfully limits the PEMFC power fluctuation to 200 W/s, thereby enhancing the lifespan of the PEMFC. Moreover, the study reveals that replacing Li-ion batteries with the PEMFC system as the primary power source can significantly enhance the USV's endurance mileage by 1.33–1.70 times carrying the same weight and 1.07–1.36 times carrying the same volume, respectively. And with increased hydrogen storage, the PEMFC system's energy density advantage over Li-ion batteries becomes more pronounced. These findings contribute to the exploration of PEMFCs as a promising solution for addressing the endurance limitations of battery-powered USVs, leading to their improved performance and extended operational range. • A whole vessel model including electrical, dynamic and control system is established. • A strategy based on bus voltage and PEMFC operating stability is proposed. • The energy management strategy has good adaptability to operating conditions. • Replacing Li-ion batteries with PEMFCs effectively increases the range of USVs. [ABSTRACT FROM AUTHOR]

The demand for rounded graphite for lithium-ion batteries will increase rapidly in the next years, mainly driven by the growing number of electric vehicles. Due to the different particle morphology, synthetic and natural graphite materials have different processing requirements to produce battery-grade materials. However, similar rounding processes with batch-wise operating classifier mills or continuously running classifier mill cascades are used today. For natural graphite in particular, these processes are not very efficient due to their low yield and high energy demand. In this paper, we present a newly developed rounding process for natural graphite, with lower specific energy consumption and higher yield compared to the existing processes. These advantages make the proposed process more economical especially in periods of time with high energy prices. The quality of the material produced by the new process was investigated and compared with the material produced by the existing process, by showing similar graphite properties. Furthermore, the scalability of the new spheroidization process is demonstrated in this paper. Finally, the good cycling performance of the rounded natural graphite material produced by the new process is demonstrated in stacked pouch cells assembled with double side coated anodes. The tested pouch cells still had a capacity retention of over 88% after more than 1000 charge and discharge cycles. [Display omitted] [ABSTRACT FROM AUTHOR]

Battery state of charge (SOC) estimation is crucial for assessing electric vehicle safety and evaluating the remaining driving range. Owing to the complexity, variability of operating conditions, and the highly nonlinear internal mechanisms of batteries, accurate SOC estimation remains a focal point of current research. Therefore, this paper proposes a random search optimization-based Long Short-Term Memory (RS-LSTM) neural network for precise SOC estimation. The paper firstly uses the CALCE dataset, extracting discharge capacity and discharge energy as critical features from six battery parameters by employing the random forest algorithm. The Look-back, Epoch, Batch size, and Learning rate parameters in the LSTM neural network optimized by random search algorithm. The study result reveals optimal settings (Look back: 45, Epoch: 177, Batch size: 64, Learning rate: 0.0026) achieving superior estimation accuracy, evidenced by mean average error（MAE） and root mean square error（RMSE）of 0.221 % and 0.262 %, respectively. Furthermore, the method's superiority, effectiveness, robustness, and applicability were verified by conducting tests across various estimation methods, various SOC estimation intervals, various temperature conditions, the addition of Gaussian noise, and tests on experimental and real-world vehicle data. The research process demonstrates that the proposed method has superior precision and indicates promising potential for future applications. • The SOC estimation is crucial to ensure the safety of electric vehicle operation. • A novel SOC estimation based on LSTM improved by Random search is proposed. • A innovative parameter downscale through random forest algorithm is implemented. • The accuracy and robustness of the method under various conditions is validated. [ABSTRACT FROM AUTHOR]

Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization (IDBO) is proposed. First, the original data is decomposed using VMD to reduce the non-smoothness of the data and to reduce the impact of non-smoothness on the prediction performance. The prediction is then carried out using the IDBO-HKLSSVR model, where the parameters in the prediction model are optimized using the IDBO optimization algorithm. Finally, all prediction components are superimposed to obtain the final results. The experimental results show that the coefficients of determination of the SOH of the six batteries predicted by the model are above 0.98388, which are higher than those of the other algorithms, confirming the high accuracy of the model in predicting the SOH of lithium-ion batteries. Meanwhile, compared with the existing prediction methods, the VMD-IDBO-HKLSSVR model proposed in this paper can predict the SOH of lithium-ion batteries more accurately. • The algorithm was validated using both NASA and CALCE datasets. • Improving the dung beetle optimization algorithm using three strategies. • The superiority, robustness and stability of the algorithm are verified. [ABSTRACT FROM AUTHOR]

With the new round of technology revolution and lithium-ion batteries decommissioning tide, how to efficiently recover the valuable metals in the massively spent lithium iron phosphate batteries and regenerate cathode materials has become a critical problem of solid waste reuse in the new energy industry. In this paper, we review the hazards and value of used lithium iron phosphate batteries and evaluate different recycling technologies in recent years from the perspectives of process feasibility, environment, and economy, including traditional processes such as mechanical milling, magnetic separation, and flotation, as well as pyrometallurgical and hydrometallurgical processes. This paper also introduces the highly promising salt electrolysis and direct regeneration restoration technologies, and we pay special attention to simple and efficient new methods for lithium replenishment restoration, which will provide new insights for a wide range of researchers to implement in-depth experiments. • Comprehensive introduction to LFP characteristics and mainstream recycling methods. • LFP regeneration and Li supplementation are clearly categorized, breaking ambiguity. • Clarify the roles, advantages and links of pyro- and hydro-metallurgy in recycling. • Highly summarizes the current direction of lithium-ion battery improvement. [ABSTRACT FROM AUTHOR]

Battery thermal management systems play a crucial role in maintaining the optimal temperature range of lithium-ion batteries, ensuring their efficient operation and prolonged lifespan. Hybrid cooling (HC) techniques, which involve the utilization of hydrophilic fibers, have shown promise in enhancing the thermal performance of these systems. The present study employs an experimental approach to investigate the impact of selected fiber materials on the thermal performance of a hybrid cooling system designed for battery thermal management. Parameters such as the time required for complete wetting of the fiber and the coolant absorption capacity of the fiber are considered during the fiber selection process. Findings reveal that the tissue paper fiber displayed superior water transfer capabilities, achieving a minimum wetting time of 46 s for a 75 mm fiber length. This characteristic positions it as a promising candidate for the subsequent hybrid cooling experiment. Noteworthy is its water holding capacity per unit volume, which surpassed that of cotton fiber by 40%. HC demonstrates the superior temperature uniformity and average battery pack temperature compared to simple air cooling (SAC) under varying velocities, emphasizing the pivotal role of HC. A remarkable improvement of 41.86% in temperature uniformity is obtained in HC over SAC. [Display omitted] • Hydrophilic fiber-based hybrid cooling significantly enhances battery thermal management. • Tissue paper-based fibers exhibit superior water transport and 40% higher water holding capacity, influencing efficient cooling. • Hybrid cooling's thermal performance under various velocities underscores its adaptability to different operational conditions. • Hybrid cooling surpasses simple air cooling, achieving a notable 41.86% improvement in temperature uniformity. • Cumulative quantity of water evaporated indicates a stable and reliable evaporation rate, which leads to the sustained performance of the hybrid cooling system. [ABSTRACT FROM AUTHOR]

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the Osprey Optimization Algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by ±5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions. • This paper presents novel Osprey optimization algorithm-based optimal lithium-ion battery model. • The model can be used with electric vehicles and power grid applications. • Practical measurements are implemented to verify the battery model and sensitivity analyses. • The OOA approach is proposed to solve the optimization problem. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) methods are commonly used in the field of State of Health (SOH) estimation for Lithium-ion batteries (Libs). However, identifying their appropriate application scenarios can be challenging. This paper presents four FRA techniques, including electrochemical impedance spectra (EIS), mid-frequency and low-frequency domain equivalent circuit model (MLECM), distribution of relaxation time (DRT) and non-linear FRA (NFRA) technique. This paper proposes two estimation frameworks, machine learning and curve fitting, to be applied to each of the four techniques. Eight SOH estimation models are developed by linking the extracted feature parameters to the battery capacity variations. The paper compares the accuracy of estimation, estimation range, and other properties of the eight models. Application scenarios are identified for the techniques by using three classification methods: different estimation frameworks, frequency response linearity, and impedance technique. The results demonstrate that MLF is recommended for scenarios with a large amount of battery data, while CFF is recommended for scenarios with a small amount of data. NFRA could be applied to electric vehicle power batteries, while LFRA is recommended to be used for retired batteries. EIS method is recommended for complex and dynamic scenarios, while non-EIS method is recommended for scenarios that require high accuracy. • Four FRA techniques, Impedance, MLECM, DRT and NFRA-based method are developed. • Two frameworks, MLF and CFF, are applied to each of these four techniques. • The estimation accuracy and estimation range of the eight models are compared. • Application scenarios are identified by using three classification methods. [ABSTRACT FROM AUTHOR]

• An unsupervised feature extraction method of Li-ion batteries' EIS is proposed. • The latent variables are extracted automatically without prior knowledge. • The extracted latent variables are related to battery aging. • The capacity of batteries for uneven usage during degradation is estimated. • The proposed method generates reliable EIS data. In this paper, we propose an unsupervised feature extraction technique using a variational autoencoder and generative adversarial network (VAEGAN) that automatically extracts meaningful latent variables from the electrochemical impedance spectra (EIS) of lithium-ion (Li-ion) batteries. These variables accurately reflect the degree of Li-ion battery degradation and are closely related to its capacity. By analyzing the latent variables, it was found that the network can learn the effect of Li-ion battery capacity degradation on the EIS. The extracted latent variables are then used as input features for Gaussian Process Regression (GPR) to estimate the capacity of Li-ion batteries under uneven usage and with unknown historical data. The results show that the capacity prediction method proposed in this paper can significantly reduce prediction errors compared to the current state-of-the-art methods. The method not only provides a more reliable capacity estimation but is also robust to highly noisy EIS data, making it more suitable for practical application scenarios. [Display omitted] [ABSTRACT FROM AUTHOR]

Conventional online evaluation methods for the mechanical/thermal behaviour of lithium-ion batteries fall short in terms of efficiency and accuracy, especially under extreme conditions coupling high-temperature and fast charging/discharging. This paper proposes an electrochemical/thermal/mechanical analytical model for prismatic lithium-ion batteries to assess their temperature, stress, deformation, and gas evolution. An electrochemical submodel is formulated, covering the lithium intercalation/deintercalation, solid-electrolyte interphase (SEI) decomposition/regeneration, and electrolyte decomposition. A thermal submodel is created to simulate heat transfer between the cell and its environment, considering reaction and Joule heating as heat sources. A mechanical submodel is developed to reveal the effects of nonlinear elastic constitutive properties and the mechanical/thermal/electrical states on cell deformation and stress, incorporating the reaction gas evolution. A coupled multidisciplinary analytical model is formed, using the state variables of temperature, stress, and deformation to link the submodels. The model's performance was validated against numerical and experimental results under high-temperature charge/discharge cycle conditions, demonstrating efficiency at the level of seconds, temperature errors below 0.6 %, and pressure errors below 4.6 %. The advantages in efficiency, accuracy, and applicability highlight its excellent prospects in energy storage and electric vehicle applications. This paper proposes an electrochemical/thermal/mechanical analytical model for prismatic lithium-ion batteries to assess their temperature, stress, deformation, and gas evolution. The temperature/pressure/SOC-dependent cell deformation is analyzed based on the nonlinear elastic property. [Display omitted] • A mechanical/thermal analytical model for prismatic lithium-ion cells is proposed. • The model integrates submodels of electrochemistry, heat transfer, and mechanics. • The mechanical submodel incorporates a nonlinear elastic constitutive equation. • The approach evaluates the state evolution in temperature, stress, and deformation. • The accuracy is validated by high-temperature charge/discharge cycle experiments. [ABSTRACT FROM AUTHOR]

As one of the most important parts in lithium-ion batteries (LIBs), commercial polyolefin separators suffer from inherent drawbacks such as thermal-dimensional shrinkage, poor electrolyte wettability, and environmental pollution. In this study, a green bio-based composite separator with good flame retardancy and high ion conductivity is designed by a facile paper-making process and subsequently borate salts crosslinking, which consists of marine-derived calcium alginate fibers bridge and land-derived cellulose micro-fibers filler. The composite separator reveals a multi-hierarchical porous structure and homogeneous boron crosslinking, and consequently has a superior liquid electrolyte wettability and high strength. Impressively, the composite separator delivers better flame retardancy and higher lithium-ion transference number (t Li+ = 0.4) and ion conductivity (σ = 1.53 mS cm−1) compared with the commercial polypropylene separator, attributing to the abundant polar groups upon the fibers and the incorporation of sp3 Boron. As a result, the LIBs equipped with composite separator demonstrate excellent interface stability and rate capability. More importantly, the LIBs exhibit greater security at the elevated temperature of 140 °C. Additionally, the composite separator shows a potential degradation in natural environments. This eco-friendly bio-based separator paves a new insight for the design of heat-resistance separator as well as the safe running of LIBs. [Display omitted] • A bio-based fiber separator is designed by a facile paper-making method. • The composite separator exhibits superior flame retardancy. • The composite separator facilitates Li + transport with higher ion conductivity and Li + migration number. • LIBs equipped with the composite separator reveal enhanced safety at elevated temperature. [ABSTRACT FROM AUTHOR]

Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery's cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery's previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well. • Review of State of Health (SoH) estimation methods for lithium-ion battery pack translating from first life to second life. • Critical analysis of equipment's and test protocols subjected to cyclic ageing. • Classification of SoH estimation methods in the form of physical and chemical based approach, electrical and Artificial Intelligence (AI) based techniques. • Listed the parameters acquired from battery to be considered in second life SoH estimation. • Presented the challenges associated with SoH methods for Second Life Batteries (SLB). [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have emerged as an indispensable component in the development of green transportation such as electric vehicles (EVs) and large-scale applications of renewable energy such as smart grid energy storage systems. The detection, judgment, and prediction of various battery states such as State of Charge (SOC) and State of Health (SOH) in the battery management system (BMS) play a critical role in guaranteeing the LIBs work under a safe and reliable situation. After decades of intensive investigation, accompanied by the fast development of big-data techniques (BDT) and artificial intelligence (AI) algorithms, the framework of BMS is moving from the traditional onboard system towards the functional integrated scheme. This paper starts with a comprehensive overview of the underlying degradation mechanism of the battery and algorithm distinction and judgment of the battery states in BMS. Subsequently, the paper has systematically reviewed and discussed the most commonly used approaches and state-of-the-art algorithms for battery state estimation in BMS from the perspective of three different BMS configurations: onboard-BMS, cloud-BMS, and functional integrated-BMS. This review expects to stimulate more new insights and encourage more efforts to develop advanced BMS for intelligent and innovative battery control. • Various battery degradation phenomena and their model development are explained. • Three distinct BMS structures—onboard-BMS, cloud-BMS, and Fi-BMS—are explained. • The latest advancements in battery state estimation algorithms are reviewed. • Emerging technical innovation prospects are highlighted in four areas. [ABSTRACT FROM AUTHOR]

• The proposed method can detect lithium plating earlier in the charging process. • Compared with other methods, this method is more sensitive and simple. • Combined with the model, the source of lithium plating signal is analyzed. • The universality of the method is verified by model simulation. Lithium plating is one of the most severe problems threatening the durability and safety of lithium-ion batteries. Therefore, accurate detection of lithium plating is crucial for the health management and charging control of lithium-ion batteries. In this paper, an impedance-based method is proposed to detect lithium plating of lithium-ion battery by comparing the normalized charing internal resistance profiles. After verifying that the model-based method is effective and feasible, it was used to analyze the reason and internal mechanism of the detection signal compared with the more frequently used lithium plating detection procedures today. The proposed detection method is more sensitive and accurate and expected to detect lithium plating in real-time during the charging process. Finally, the model showed that the method remained effective and reliable for lithium plating detection when the charging conditions changed. This paper, through the analysis, simulation, and experimental verification of online lithium plating detection method based on charging internal resistance, provides innovative ideas for the subsequent development of lithium plating detection and stimulates potential technologies for future development. [ABSTRACT FROM AUTHOR]

Owing to its high capacity, silicon (Si) is a promising anode for meeting the escalating need for batteries with high energy density. Nonetheless, the substantial volumetric variation generated by lithiation/delithiation often results in the pulverization of Si, which substantially lowers its cycle stability. Graphene/graphene nanosheets (GNSs) with higher electrical conductivity and mechanical strength are anticipated to overcome these obstacles when employed as the coating matrix of silicon. Unfortunately, the majority of Si@graphene composites are not manufactured in situ , so that graphene is hardly to entirely encapsulate Si.The low-quality coating leads to the exposure of Si after cycles, resulting in a short cycle life. Herein, graphene nanosheets encapsulated silicon nanospheres (Si@GNSs) are synthesized in situ using a radio-frequency (RF) thermal plasma system, in which graphene and Si have strong interfacial chemical interactions. Further, free-standing Si@GNSs/reduced graphene oxide (Si@GNSs/rGO) paper was prepared using graphene oxide (GO) as a special 'binder'. When Si@GNSs/rGO paper is directly used as anode electrodes, it demonstrates a high reversible capacity (2270 mAh g−1 at 0.2 A g−1), outstanding rate performance (1569 mAh g−1 at 5.0 A g−1) and ultra-stable cycle performance (capacity retention of 98.55% for 2000 cycles at 3.0 A g−1). [Display omitted] • Si@GNSs composites were prepared in situ by the RF thermal plasma system. • The flexible Si@GNSs/rGO paper is prepared with GO as a special 'binder'. • Si@GNSs/rGO paper exhibits high capacity of 2250 mAh g−1 at 0.2 A g−1. • Si@GNSs/rGO paper show excellent rate performance of 1569 mAh g−1 at 5.0 A g−1. • After 2000 cycles, the capacity of Si@GNSs/rGO paper remains 98.55%. [ABSTRACT FROM AUTHOR]

This paper describes an accurate model of the Electrical Power Subsystem (EPS) of the Thermal Analysis Support and Environment Characterization Laboratory (TASEC-Lab), a university experiment developed for using in high-altitude balloon missions to measure and analyze the convection heat transfer. A Li-ion battery and two high efficient DC–DC converters have been characterized through laboratory tests and fitting to the experimental results. The developed mathematical models described in this paper are used to carry out accurate EPS simulations and are validated by comparison with the experimental data. • Design and modeling of the electrical power subsystem of a stratospheric balloon experiment. • New Li-lion battery discharging model that describes the three main parts: initial decay, linear, and exponential decay. • Formulation of a new DC/DC converter efficiency model. • Simulation and model validation of the electrical power subsystem. [ABSTRACT FROM AUTHOR]

• The emerging dependency on Electric vehicles and the role of Machine Learning in bolstering it. • Hybrid techniques tend to outwit different approaches. • Comprehensive review of various battery life estimation techniques about Machine Learning and Deep Learning. • Challenges faced and future scope of Deep Learning and Machine Learning in the State of Charge, knee-point, and Remaining Useful Life prediction of Lithium-ion batteries. Rapid adoption in the usage of li-ion battery-fueled vehicles provides a promising approach to assuage the impact of climate change. The implementation and increasing sales trends in the Electric vehicle industry have created positive results in various regions. The reliability and consistency of the Battery management system in new-age Electric vehicles to predict the remaining battery cycles, State of charge, and knee point prove to be a complex task citing the non-linear behavior of lithium-ion batteries throughout their complete lifecycle. However, to make sure that the batteries work as intended consistently, there arises a need to monitor the battery's health and performance regularly. By examining recent literature, this paper carries out a comparative study of various published research regarding estimating the State of charge for Lithium-ion batteries using multiple methods, classified into three categories: adaptive, data-driven, and hybrid approaches. We also conducted a comparative study on Knee-point and the number of charge-discharge cycles left about electric vehicles. This review intends to furnish a comparative analysis of Machine learning & Artificial intelligence-related estimation techniques and analyze their superiority over traditional data-driven methods. It was observed that hybrid techniques consisting of various Machine learning algorithms yielded good results. This paper aims to perceive the most accurate algorithms and methodologies used, which can be further used in battery management systems to improve battery prognostics drastically. The future works and challenges encountered in various researches have also been mentioned at the end of the review that will hopefully pave the way for increasing efforts towards the development of the advanced SOC, knee point, and RUL methods for future EV uses. [ABSTRACT FROM AUTHOR]

Lithium−ion batteries (LIBs) are one of the most important energy sources in modern society and are commonly used due to their high energy density and long life span. However, the management standards and regulations for marine transport and power applications of LIBs are not perfect, and accidents caused by LIBs in the process of marine transport and power applications are numerous. The causal factors of these accidents are mainly mechanical, electrical and thermal abuse. To better understand the failure mechanism and thermal runaway (TR) consequences of LIBs, this paper briefly introduces the disaster−causing mechanism, management regulations and transport standards of LIBs. On this basis, from the perspectives of marine transport and ship power applications, it is discussed that LIBs may encounter the risks of mechanical, electrical and thermal abuse when they are applied as marine dangerous goods and as ship power sources, and the factors affecting the occurrence of mechanical, electrical and TR are analyzed. The challenges and prospects of LIBs in marine transport and power applications are also highlighted, and guidance for further strengthening the safety management of LIBs in marine transport and ship power applications is provided. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] The exponential growth of stationary energy storage systems (ESSs) and electric vehicles (EVs) necessitates a more profound understanding of the degradation behavior of lithium-ion batteries (LIBs), with specific emphasis on their lifetime. Accurately forecasting the lifetime of batteries under various working stresses aids in optimizing their operating conditions, prolonging their longevity, and ultimately minimizing the overall cost of the battery life cycle. Accelerated aging, as an efficient and economical method, can output sufficient cycling information in short time, which enables a rapid prediction of the lifetime of LIBs under various working stresses. Nevertheless, the prerequisite for accelerated aging-based battery lifetime prediction is the consistency of aging mechanisms. This review, by comprehensively summarizing the aging mechanisms of various components within LIBs and the battery degradation mechanisms under stress-accelerated conditions, provides a reference for evaluating the consistency of battery aging mechanisms. Furthermore, this paper introduces accelerated aging-based lifetime prediction models and offers constructive suggestions for future research on accelerated lifetime prediction of LIBs. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) can ensure the safety and reliability of power batteries during operation, reduce the failure rate and operating costs, and enhance user experience. However, battery degradation is a complex, nonlinear dynamic process that is difficult to fully comprehend and predicting RUL remains a significant challenge. To address this issue, the hybrid data-driven prediction model PCA-CNN-BiLSTM was proposed in this paper, which combines principal component analysis (PCA), convolutional neural network (CNN), and bi-directional long short-term memory (Bi-LSTM) network. PCA was applied to downscale and whiten the health factor (HF) to maximize the extraction of important features of lifespan decay, while reducing the correlation between features. The convolution kernel of the CNN was used to explore the local region feature information of the input information and search for the common patterns among the neighboring data. Additionally, the model parameters and computational efforts were reduced through pooling. Finally, battery RUL prediction was achieved using Bi-LSTM, which has the advantages of effectively enhancing model accuracy and reducing the risk of over-fitting by taking into account both past and future data. The performance of the proposed model was evaluated utilizing NASA and CALCE's battery datasets, and the results suggest that it exhibits a high level of accuracy across various datasets. Compared to other methods, the PCA-CNN-BiLSTM method has the best performance indicators for predicting battery RUL, including RMSE, MAE, MAPE, RUL e and DOL. This indicates that the proposed model has better fitting performance, accuracy, robustness, and generalization ability. [Display omitted] • Propose a novel battery RUL prediction method based on hybrid data-driven model. • Nineteen health factors were extracted, including segmented discharge time and temperature. • The effectiveness of the PCA-CNN-BiLSTM was verified on multiple public datasets. [ABSTRACT FROM AUTHOR]

In this paper, manganese is used as a medium to connect pyrolusite with lithium-ion batteries, which not only meets the needs of comprehensive utilization of global strategic mineral resources, but also maximizes the performance of lithium-ion batteries under the goal of global carbon neutralization and carbon peak. Firstly, the optimum experimental conditions for sulfuric acid roasting of pyrolusite were investigated by single factor experiment and orthogonal experiment: the roasting temperature was 650 °C, the roasting time was 4 h, the acid-to-ore ratio was 2:1, and the water-to-ore ratio was 0.6:1. The roasting temperature plays a major role. Then, LMF x P-C (x = 0, 1/4, 1/8, 1/12, 1/24) cathode materials were prepared by adding different mass Fe elements with manganese element purified from pyrolusite as manganese source. Its electrochemical analysis was carried out: the capacity retention rate of LMF 1/4 P–C cathode material is 92.54% after 500 cycles. After a total of 150 times of rate tests at different rates, the discharge specific capacity of LMF 1/4 P–C can still reach 148.5 mAh g−1. At the same time, the EIS results show that the values of R e and R ct of LMF 1/4 P–C cathode material are the smallest compared with other materials. Finally, the LMF x P-C cathode material was analyzed by first-principles, and the optimal Fe doping amount was theoretically predicted. The crystal structure, bonding and density of states of the LMF x P-C cathode material were calculated and discussed. The results show that when the Fe doping amount is 1/4, the LMF x P-C system has the highest conductivity and the best electrochemical performance. The calculation results are consistent with the experimental results. [ABSTRACT FROM AUTHOR]

A new method is developed in the paper to estimate the maximum available capacity which is an important basis for indicating the State of Health (SOH) of lithium-ion batteries. Firstly, a data reconstruction approach is proposed to pre-process the acquired data to suppress the influence of measurement noise and reduce the negative impact on estimation precision when measuring equipment adopts different sampling frequencies. Then, the variation trend of the incremental capacity curve obtained based on the reconstructed data with the battery aging is analyzed, and a health indicator (HI) including multi-view features is put forward to characterize the battery degradation more comprehensively. The multi-view features are coming from the capacity increment curve versus voltage and time, including the maximum value of the capacity increment curve, the voltage corresponding to the maximum value, other values surrounding the maximum value and so on. Finally, Support Vector Regression is used to establish a model between the extracted HI and the maximum available capacity, and two types of open source data are used to verify the performance. The experimental results show that the data reconstruction method and multi-view health indicator proposed in the paper can obtain high precision estimation results. [Display omitted] • The problems of ICA in practical application are deeply analyzed. • A data reconstruction method is proposed. • The reconstruction charging voltage curve is monotonically increasing. • The capacity estimation results are not affected by the sampling frequency. • A new HI including multi-view features is put forward. [ABSTRACT FROM AUTHOR]

Recently, lithium-ion battery storage system has become increasingly popular due to its enormous potential and capacity in renewable energy integration and e-mobility applications leading to achieve global carbon neutrality by 2050. However, the performance of lithium-ion batteries has been affected due to the material degradation as well as varying environmental conditions. Thus, this paper presents analytical evaluation, aiming to investigate the advancement on the state-of-the-art of lithium-ion battery material potential that has been mapped from the hot papers. Accordingly, 73 hot papers (top 0.1% highly cited) have been found using the keyword search on lithium-ion batteries from the Web of Science database published in last 2 years between 2019 and 2021. These hot papers are evaluated using various key factors, including state-of-the-art of lithium-ion battery materials followed by analytical evaluation. The analytical assessment comprises numerous crucial aspects including keyword co-occurrence network analysis, distribution of articles over countries, journals, research types, and subject categories. Moreover, the review explores the concerns, issues and challenges to identify the existing research gaps. Finally, the review uncovers promising research opportunities and directions that will support the researchers towards energy efficiency, environmental sustainability of advanced lithium-ion battery management system. [Display omitted] • Lithium-ion battery material potential is analytically evaluated towards future research directions. • Hot paper earns a significant number of citations which is top 0.1 percent on the field. • The analytical evaluation is comprehensively done based on keyword co-occurrence network analysis. • Existing research gaps, issues and challenges on LIB materials are highlighted for future development. • This study will assist researchers and industries to develop an energy efficient advanced LIB. [ABSTRACT FROM AUTHOR]

State-of-Health (SOH) estimation of lithium-ion batteries is critical for reliable and efficient energy storage and usage. Traditional SOH estimation approaches either compromise on real-time performance or on estimation accuracy. This paper introduces an end-cloud collaboration approach that synergistically combines a cloud-based deep learning model with an end-based empirical model to achieve both high-accuracy and real-time SOH estimation. Utilizing the Transformer architecture, the cloud-side model employs multiple features extracted through signal analysis technology. On the other hand, the end-side employs a fast, iterative double exponential empirical model that provides real-time estimates. Two methods of end-cloud collaborative updating are presented, both leveraging Kalman Filter and Unscented Kalman Filter (UKF) to integrate and iteratively update the models. Evaluation shows that the cloud-side model achieves a Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of around 0.8 %, while the end-cloud collaboration system maintains these metrics at 1 %, successfully capturing nonlinear capacity rebound phenomena. The work contributes to the domain by offering an approach that balances computational load and latency, making it suitable for online applications. Under the Cyber Hierarchy and Interactional Network (CHAIN) framework, this end-cloud collaboration architecture holds promise for high-accuracy, real-time health management of lithium-ion batteries across their full lifecycle. The entire end-cloud collaboration system includes a cloud based deep learning model, an end-side fast empirical model and an end-cloud collaboration algorithm. On the cloud-side, firstly, data preprocessing such as data cleaning and filtering is performed on the data. Then, multiple features are extracted based on various signal analysis methods, including differential thermal voltammetry analysis, incremental capacity analysis and equal voltage discharge interval discharge time analysis. The deep learning model based on Transformer is constructed. On the end-side, the fast model is constructed based on a dual exponential empirical model. And the end-cloud collaboration algorithm is arranged on the end-side. Two end-cloud collaboration methods are used simultaneously, including result fusion and iterative update based on Kalman filter, and online parameter identification based on UKF by sequentially linking end and cloud models. Finally, under the cyber hierarchy and interactional network framework, the end-cloud collaboration approach proposed in this paper has the potential to realize high-accuracy and high-real-time health management of lithium batteries throughout the full life cycle. [Display omitted] • A high accuracy and real-time SOH estimation method is presented. • Deep learning model on cloud-side is constructed with Transformer model as the core. • Feature extraction is based on multi feature collaborative analysis. • Two end-cloud collaboration methods are proposed to update the end-side. [ABSTRACT FROM AUTHOR]

Lithium-ion battery (LIB) occupies a major position in rechargeable batteries. However, if the state of charge (SOC) estimation of LIB is not accurate, it will lead to the decline of battery life. SOC is often estimated by the extended Kalman filter (EKF) algorithm, but this method depends on the accuracy of model parameters. The sampling point interval time can affect the accuracy of SOC estimation. In this paper, the first-order RC model parameters are identified by two methods and the SOC is estimated by the EKF algorithm. Using this method, the battery parameters are received more accurate and the SOC estimation error is smaller. When the sampling interval is 0.03 s, the SOC estimation error is the smallest. And the second-order RC model can receive the same result. When the sampling interval is 0.03 s, the SOC estimation error of the 1RC-1 method is less than 1.4 %, the SOC estimation error of the 1RC-2 method is less than 0.017, and the root mean square error (RMSE) is 0.0087 and 0.01084. The conclusion is also used in the second-order RC model and contributes to the improvement of the SOC estimation accuracy. An effective method to estimate the battery SOC range is also proposed. This paper provides an effective method for obtaining more accurate SOC. [ABSTRACT FROM AUTHOR]

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (Gene Expression Programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness. • Establishing a high-precision battery SOH estimation model based on the GEP algorithm. • The relationship between ICA and DTV curves with aging evolution laws is revealed. • Fusion of GEP model with existing vehicle-end and cloud-end models using stacking strategy. [ABSTRACT FROM AUTHOR]

In recent years, the demand for high performance rechargeable lithium-ion batteries has grown dramatically and numerous initiatives were taken to enhance the usage of cutting edge electrical conducting materials. The graphene and graphene-based materials have undergone further investigation since their discovery, and as a result, they are now recognized as high performance electro-conductance materials. Graphene is known as a 2-dimensional carbon-based material with an existing of high Young's modulus, outstanding electrical conductivity, amazing intrinsic electron mobility, enhanced mechanical strength, enhanced photosensitive transmittance, catalytic activity and chemical stability. However, in the past ten years, both the applied materials industry and academia have achieved remarkable innovations as a result of the use of graphene, despite some challenges that include high production cost that are incurred at the expense of compromised high-quality materials. Also, scarce availability in large amounts necessary for mass distribution is among the challenges is among the graphene producing challenges. In this review paper, we emphasize the recent functionalization developments, controlled product materials, and the usage of graphene in rechargeable lithium-ion batteries. In order to describe the future development of graphene for lithium-ion batteries, we will discuss the most encouraging outcomes, advantages, difficulties, crucial problems, research areas, and perspectives in this review. [ABSTRACT FROM AUTHOR]

Because of the instability and susceptibility to thermal runaway of lithium-ion batteries (LIBs), their storage has always been at high risk of fire. Numerous studies have analyzed the risk of fire in lithium-ion battery (LIB) warehouses. Still, most of them have focused on the factors influencing the fire following the thermal runaway of a LIB occurs, and there has been a lack of research on the causes of the LIB thermal runaway being occurred. To address this shortcoming, this paper proposed a comprehensive framework considering multiple influencing factors for fire risk assessment in LIB warehouses that combines Bayesian networks (BNs) and expert evaluation. The proposed framework adopts the Bayesian network (BN) model that considers three main types of parameters: fire causes, fire spread influencing factors, and fire consequences. The main objective was to analyze the evolution and consequences of LIB warehouse fires dynamically and provide effective suggestions for fire risk reduction. The results showed that the proposed framework was a reliable avenue for fire risk assessment of LIB warehouses. In addition, it was demonstrated that human factors commonly cause the LIB warehouse open fires. Thus, to prevent open fires from continuing spreading, the LIB warehouses should avoid storing batteries with high state of charge values; furthermore, if it is necessary to store them, automatic sprinklers and other firefighting facilities should be installed. In addition, timely firefighter response and the installation of mechanical ventilation systems are essential to substantially curtail casualties. [ABSTRACT FROM AUTHOR]

Overcharging, overheating and other abnormal conditions are important causes of thermal runaway of Li-ion batteries, and a major safety issue for Li-ion batteries. Therefore, it is critical to detect these faults as soon as they are triggered. In this paper, the actual usage scenario of the battery was simulated by setting the appropriate preloading force. And the characteristics of the changes of the battery expansion force under abnormal conditions were studied by battery charging and discharging, overcharging, overdischarging and thermal runaway experiments. In particular, we proposed a fast and high confidence battery fault detection method by the characteristics of the expansion force change under various faults, which could provide early warning of multiple faults. This work provided a reliable method of detecting battery failures at an early stage and ensured process safety and reduced environmental pollution caused by lithium-ion battery accidents through reasonable fire prevention measures. [ABSTRACT FROM AUTHOR]

Prognostics and health management (PHM) has emerged as a vital research discipline for optimizing the maintenance of operating systems by detecting health degradation and accurately predicting their remaining useful life. In the context of lithium-ion batteries, PHM methodologies have gained significant attention due to their potential for enhancing battery maintenance and ensuring safe and reliable operation. Among the various approaches, data-driven methodologies, particularly those leveraging machine learning (ML) models, have gained interest for their accuracy and simplicity. To develop an optimized data-driven PHM system for batteries, a comprehensive understanding of each step involved in the PHM process is crucial. This review paper aims to address this need by providing a thorough analysis of the different phases of battery PHM, encompassing data acquisition, feature engineering, health diagnosis, and health prognosis. In contrast to previous review papers that primarily focused on battery health diagnosis and prognosis methods, this work goes beyond by encompassing all essential steps necessary for developing a tailored PHM methodology specific to lithium-ion batteries. By covering data acquisition methods, feature engineering techniques, as well as health diagnosis and prognosis methods, this paper fills a significant gap in the existing literature. It serves as a comprehensive roadmap for researchers and practitioners aiming to develop PHM systems for lithium-ion batteries using ML techniques. With its in-depth analysis and critical insights, this review paper constitutes a substantial contribution to the field. It provides valuable guidance for designing effective PHM methodologies and paves the way for further advancements in battery maintenance and management. • Review of key steps in ML-based Prognostics & Health Management for Li-ion batteries. • Evaluation of data acquisition methods for battery health diagnosis. • Deep analysis of feature engineering for battery health representation. • Study of ML algorithms for accurate health diagnosis & RUL prediction. • Roadmap provided for optimized, data-driven PHM systems for Li-ion batteries. [ABSTRACT FROM AUTHOR]

The current work aims to understand and model thermal runaway (TR) events in lithium-ion (LIB) 18650 cells within the context of aircraft battery applications. The primary goal is to comprehend the phenomenon and discuss strategies for mitigating its consequences during aircraft operation. TR is modeled using Arrhenius kinetic equations and is implemented in both lumped parameters (Matlab SimulinkTM), and 3D CFD simulations (Ansys FluentTM) using User Defined Functions. To validate the thermochemical model, cells are initially simulated in an oven test, where a cell is exposed to a temperature-controlled atmosphere, triggering exothermic reactions. With a strong correlation between lumped parameters and 3D models, the latter is simulated under battery module installation conditions. An internal short-circuit is then implemented within the cell to observe how thermal runaway is triggered by an internal heat source. The trigger cell is subsequently placed in a battery module assembly to assess the dominant heat transfer modes and the likelihood of TR induction from one cell to its neighbors. This work's main objective and innovation are to compare different materials in which cells are immersed while observing the main heat transfer parameters for each material. Three conditions are tested: ceramic paper fiber and G7 as solid separators, and no separator material, where air fills the gaps between cells. The analysis of heat transfer modes reveals radiation's dominance in the case of air interstice, suggesting the possibility of using a special coating to reduce the cell surface emissivity as an alternative to decrease the likelihood of TR propagation. Thus, two values of surface emissivity were tested in the case of air. Considering a cell triggered by an internal short-circuit, a thermal runaway temperature spike is not observed in any of the four cases. However, the air interstice case with regular emissivity is the most critical one, with the closest cell reaching peak temperatures as high as 136 °C in 490 s. The ceramic paper fiber is considered the best separator material, as it postpones the temperature increase in the closest cell while also being lighter than G7. The results and discussions concerning heat propagation presented herein can serve as guidelines for developing strategies to mitigate thermal runaway in battery modules. [ABSTRACT FROM AUTHOR]