Future Batteries最新文献

{"title":"Sulfolane-ethylene carbonate hybrid LiNO3 electrolyte with LiDFOB as functional additive for safe and stable high-voltage lithium metal batteries","authors":"Chun-Jern Pan ,&nbsp;Cian-Ping Lin ,&nbsp;Shih-Che Lin ,&nbsp;Yi-Yu Chen ,&nbsp;Bing-Joe Hwang ,&nbsp;Chia-Hsin Wang ,&nbsp;Wei-Hsiang Huang ,&nbsp;Chun-I Lee","doi":"10.1016/j.fub.2025.100126","DOIUrl":"10.1016/j.fub.2025.100126","url":null,"abstract":"<div><div>Lithium-metal batteries (LMBs) offer higher energy density than lithium-ion batteries (LIBs) but suffer from dendrite growth, low coulombic efficiency, and safety concerns. This study introduces a deep eutectic electrolyte (DEE) composed of lithium nitrate (LiNO₃) as lithium salt and lithium difluoro(oxalato)borate (LiDFOB) as functional additives, and sulfolane (SL) mixed with ethylene carbonate (EC) as hybrid solvent. The optimized electrolyte composition, LSEC-B4, achieves moderate viscosity (27.5 cP) and the highest conductivity (1.15 mS/cm). Thermal analyses confirm its superior thermal stability and resistance to crystallization, attributed to the synergistic roles of EC and LiDFOB. Spectroscopic studies reveal that LSEC-B4 tailors Li⁺ solvation by regulating interactions with NO₃^-, SL (S<img>O), and EC (C<img>O), forming a stable coordination environment. This enhances ion transport and stabilizes the solid electrolyte interphase (SEI). LSEC-B4 exhibits outstanding performance in Li//Cu cells, delivering 98.27 % average coulombic efficiency with low overpotential, ensuring facile and high reversibility of Li deposition and stripping. In Li//Li cells, it sustains over 400 h of stable cycling with minimal voltage fluctuations, confirming long-term interfacial stability and suppressed dendrite growth. Paired with LiMn₂O₄ (LMO) cathode, Li//LMO cells achieve 84 % capacity retention (85 mAh g⁻¹) after 500 cycles at 300 mAg⁻¹ and ∼99 % average coulombic efficiency. Flammability tests highlight remarkable safety, unlike commercial electrolytes, LSEC-B4 resists ignition, benefiting from the flame-retardant nature of LiNO₃ and SL, while LiDFOB reinforces SEI stability and mitigates thermal runaway. Overall, LSEC-B4 combines conductivity, stability, safety, and cathode compatibility, providing a promising pathway toward practical, safe, and efficient LMBs.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bibliometric analysis of sulfurized polyacrylonitrile batteries","authors":"Mufeng Wei","doi":"10.1016/j.fub.2025.100125","DOIUrl":"10.1016/j.fub.2025.100125","url":null,"abstract":"<div><div>Sulfurized polyacrylonitrile (SPAN) is one of the most promising carbon-based materials with the potential to produce safe and low-cost lithium batteries. Its structural stability and strong electrochemical performance make it a compelling candidate for next-generation energy storage systems. Since the first report of a SPAN cathode by Jiulin Wang et al. in 2002, research activity has expanded significantly, resulting in 597 publications over the past two decades. In this study, we present a comprehensive bibliometric analysis of SPAN-based battery research using data retrieved from the Scopus database. VOSviewer software was employed to visualize collaboration networks, keyword clusters, and citation patterns. The analysis covers temporal publication trends, leading countries and institutions, influential authors, and co-authorship structures. Additionally, a keyword co-occurrence analysis highlights research hotspots and emerging directions in SPAN development. Our findings reveal that China leads in publication output, while the United States and Singapore achieve the highest citation impact. Collaboration networks indicate that China serves as a global hub for SPAN research, maintaining strong ties with the United States, Germany, and Australia. Thematic mapping identifies cathode modification, electrolyte engineering, and solid-state integration as active and growing areas of investigation. This bibliometric study not only documents the evolution of SPAN-based battery research but also provides strategic insights for advancing the field.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Faster, safer batteries: A smarter way to bring technology to market","authors":"Adeola Ajoke Oni ,&nbsp;Oluwafemi Babatunde Olasilola ,&nbsp;Francis T. Omigbodun ,&nbsp;Amirlahi Ademola Fajingbesi ,&nbsp;Funso P. Adeyekun","doi":"10.1016/j.fub.2025.100122","DOIUrl":"10.1016/j.fub.2025.100122","url":null,"abstract":"<div><div>Solid-state batteries (SSBs) offer higher energy density and superior safety compared to conventional lithium-ion systems, yet their commercialisation remains slow due to unresolved technical, manufacturing, and regulatory uncertainties. This study examined whether managerial innovation—specifically a hybrid Agile–Stage-Gate framework with embedded risk analytics—can accelerate SSB development. A simulation-based design was applied across 20 synthetic project pathways informed by historical lithium-ion commercialisation patterns and Technology Readiness Level (TRL) benchmarks. The model compared a traditional stage-gate approach with an adaptive hybrid system, using Monte Carlo simulations (1000 iterations) and logistic regression for validation. Results indicate a 25-percentage-point improvement in successful project launch rates (65 % vs. 40 %), a 15.5 % reduction in average time-to-market (7.1 vs. 8.4 years), and a 14 % reduction in development expenditure (£168.3 M vs. £195.6 M). Safety approval odds increased 2.41-fold. Sensitivity analysis revealed minor timeline variability (±1.2 years) and error margin in compliance prediction (±4.8 %), demonstrating controlled uncertainty. Overall, the findings suggest that adaptive managerial practices can materially shorten SSB commercialisation cycles while safeguarding regulatory assurance.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-estimation of Li-ion battery states using an improved dynamic model for electric vehicles","authors":"Nouhaila Belmajdoub ,&nbsp;Rachid Lajouad ,&nbsp;Abdelmounime El Magri ,&nbsp;Soukaina Boudoudouh","doi":"10.1016/j.fub.2025.100119","DOIUrl":"10.1016/j.fub.2025.100119","url":null,"abstract":"<div><div>In electric vehicle applications, accurately estimating the states of lithium-ion batteries, particularly the state of charge (SoC) and state of health (SoH), is essential for optimizing performance, ensuring reliability, and maintaining precise control over all operating conditions. This paper presents an asymptotic observer for the real-time estimation of all battery states, accounting for nonlinearities caused by hysteresis, the polynomial relationship between internal voltage and SoC, and the effects of Warburg impedance.</div><div>The proposed estimation method is based on an advanced Extended Kalman Filter (EKF) that incorporates a third-order approximation of the Warburg impedance within a fully integrated lithium-ion battery model. In addition, the EKF performance is systematically compared with Particle Filtering (PF) and Long Short-Term Memory (LSTM) approaches, providing a benchmark against both advanced stochastic filtering and data-driven machine learning techniques. Simulation results provide a comparative analysis of the proposed filter’s accuracy, robustness, and computational efficiency in state estimation.</div><div>To validate the practical relevance of this approach, the paper compares simulation results with experimental data obtained from actual battery tests. Discrepancies between the simulated and experimental outcomes are analyzed, with particular attention given to model simplifications, sensor inaccuracies, and environmental influences. Furthermore, voltage and capacity estimation are investigated under three ambient temperature conditions (0 °C, 25 °C, and 45 °C), highlighting the influence of temperature on the accuracy and robustness of the estimation framework. This comparison underscores the strengths and limitations of the filtering methods and offers valuable insights into their applicability in real-world EV battery management systems (BMS).</div><div>The findings emphasize the critical importance of selecting suitable estimation techniques to enhance the performance, reliability, and lifespan of electric vehicle batteries. The integration of model-based and data-driven estimators, together with multi-temperature validation, demonstrates the robustness and adaptability of the proposed framework for practical BMS deployment.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current practices and advances in thermal runaway modelling: A detailed review","authors":"Pranav Cherukat ,&nbsp;Prabhu Selvaraj ,&nbsp;Srujan V.G. ,&nbsp;Balamurugan Rathinam ,&nbsp;Ratna Kishore Velamati","doi":"10.1016/j.fub.2025.100118","DOIUrl":"10.1016/j.fub.2025.100118","url":null,"abstract":"<div><div>Electric vehicles (EVs) have emerged as the future of automotive industry. Lithium-ion batteries (LIBs) have become the predominant energy storage solution for this purpose. Given the wide array of chemistries and geometries, thermal modelling of batteries have become critically important for addressing various abuse scenarios. Consequently, it leads researchers to undertake investigations and develops models to simulate thermal runaway phenomena in LIBs subjected to thermal and mechanical stresses. The paper provides an overview of thermal runaway of LIBs, starting with a brief introduction about the current state of LIBs, electrochemistry and fire accidents. This review provides a comprehensive analysis of thermal runaway, focusing on abuse conditions and experimental setups. It examines models dealing with thermal abuse and some addressing mechanical abuse. The review spans from early electrochemical models to the latest versions, highlighting key updates and distinctive features. It discusses major results and differences between thermal runaway models, categorizes and compares these models, and briefly addresses the importance and implementation of calibration. The review concludes by evaluating which models are best suited for specific needs, based on computational effort and accuracy.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiating the effects of pressure in NMC-graphite lithium-ion batteries on cell and system level","authors":"Arber Avdyli ,&nbsp;Otto von Kessel ,&nbsp;Kai Peter Birke ,&nbsp;Alexander Fill","doi":"10.1016/j.fub.2025.100120","DOIUrl":"10.1016/j.fub.2025.100120","url":null,"abstract":"<div><div>This study investigates the influence of external pressure on the aging behavior of automotive lithium-ion pouch cells with a reference capacity of 76.6 Ah and NMC- graphite chemistry, and its transferability to the module level. Over a period of 1.5 years, four long-term ageing tests with more than 2000 cycles were conducted under constant-force conditions ranging from 0.14 to 0.44 MPa, until the cells reached a capacity-based state of health (SOHc) of 60 % These cell-level studies were complemented by five module tests with up to 2500 cycles, as well as mechanical compression experiments to characterize stiffness and pressure-dependent behavior. Electrochemical diagnostics, including capacity retention, internal resistance, and Differential Voltage Analysis (DVA), were combined with Differential Strain Analysis (DSA) to provide mechanical insight. Post-mortem investigations by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) were performed to identify local degradation mechanisms. The results demonstrate a strong dependence of aging on mechanical boundary conditions. At pressures above 0.16 MPa, resistance growth was significantly reduced compared to low-pressure conditions. Moreover, a partially reversible aging component was identified: increasing pressure from 0.16 to 0.28 MPa decreased internal resistance by approximately 40 %, attributed to the displacement of gas and restoration of particle contacts. Compression tests revealed a critical transition around 0.23 MPa from a gas-cushioned to a gas-displaced mechanical regime, consistent with the observed electrical reversibility. DSA proved to be a sensitive diagnostic tool for distinguishing reversible and irreversible aging effects, whereas DVA showed only minor dependence on pressure. Post-mortem analysis confirmed gas-induced degradation as a key mechanism, including SEI decomposition, particle exfoliation, separator deformation, and location-dependent damage concentrated in the central regions of the cells. These findings underline that pressure not only acts globally but also induces local stress distributions relevant for lifetime behavior. In summary, an optimal pressure window between 0.16 and 0.28 MPa was identified, which mitigates degradation at both cell and module level. The study highlights the dual role of pressure as both a stressor and a design parameter, offering a practical pathway to improve the durability and safety of future battery systems.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partial least squares model method for state of health prediction of lithium-ion batteries","authors":"Eugenio Sandrucci ,&nbsp;Sergio Brutti ,&nbsp;Federico Marini","doi":"10.1016/j.fub.2025.100116","DOIUrl":"10.1016/j.fub.2025.100116","url":null,"abstract":"<div><div>The practical operation of battery packs in electric vehicles requires a continuous and accurate estimate of the state of health (SOH) either at cell or pack levels. On the other hand the stochastic operation in real applications of any ì battery pack accelerates and diversifies the aging process of each specific battery thus making hard a precise estimate of SOH and the prediction of the remaining useful life (RUL). In this study, an effective estimation method based on machine learning is proposed to achieve reliable SOH. Here, charge-discharge potential curves of Li-ion pouch cells from an on-line freely available dataset, were used as inputs for a linear regression model to predict SOH. Our experimental findings demonstrate that the suggested computational technique can accurately, steadily, and robustly estimate the battery SOH with an error that is smaller or comparable to other modelling approach based on multi-model-based algorithms.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-speed X-ray imaging of thermal runaway in large-format lithium-ion cells: In-situ analysis of structural failure","authors":"Jonas Pfaff,&nbsp;Jürgen Kuder,&nbsp;Gregor Popko,&nbsp;Thomas Kisters,&nbsp;Siegfried Nau,&nbsp;Sebastian Schopferer","doi":"10.1016/j.fub.2025.100117","DOIUrl":"10.1016/j.fub.2025.100117","url":null,"abstract":"<div><div>Thermal runaway (TR) in lithium-ion batteries remains a major safety concern, particularly for large-format cells in electric vehicles and stationary storage systems. This study presents a novel lab-based diagnostic approach that combines high-speed full-cell X-ray radiography with abuse testing to resolve the internal dynamics of a TR in real time. Synchronized X-ray imaging and external measurements (temperature, voltage, nail penetration) were applied to a 50 Ah prismatic NMC cell. The method enabled time-resolved, full-field observation of structural failure progression. The experiment revealed, for the first time in a full-scale cell, the sequence of internal events from short-circuit initiation and gas evolution to CID activation, vent obstruction, and terminal ejection. X-ray image-based analysis allowed both qualitative and quantitative assessment of material motion and gas pathways, while correlation with external sensor data established the timing of critical events. This combined method yields new insights into the mechanisms of a TR as a scalable diagnostic tool for structural failure assessment and safety evaluation of large-format lithium-ion cells. It is expected to be instrumental for the development of safer cell designs.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust hybrid Neural–Kalman filter for real-time supercapacitor state-of-charge estimation in electric vehicles","authors":"Islam A. Sayed ,&nbsp;Yousef Mahmoud","doi":"10.1016/j.fub.2025.100115","DOIUrl":"10.1016/j.fub.2025.100115","url":null,"abstract":"<div><div>Accurate estimation of supercapacitor state-of-charge (SOC) is vital for optimal energy management in electric vehicles (EVs), particularly within hybrid energy storage systems (HESS). Challenges arise from nonlinear dynamics, self-discharge, temperature sensitivity, and aging-induced parameter drift. This study introduces KalmanNet, a neural network-enhanced Kalman filter, for supercapacitor SOC estimation. The approach integrates a three-branch equivalent circuit model with a data-driven Kalman gain learner trained solely on voltage and current measurements, without requiring synthetic Kalman gain ground truth. KalmanNet adapts dynamically to system uncertainties while preserving the recursive nature of traditional Kalman filters. Validation using experimental data from commercial supercapacitors under standard EV driving cycles, aging effects, disturbances, and computational load demonstrates its effectiveness. KalmanNet achieves a root mean square error (RMSE) of 0.35%, outperforming the Extended Kalman Filter (2%), Sigma-Point/Unscented Kalman Filters (1.1%), Particle Filters (0.8%), and Recurrent Neural Networks (2.2%). Processor-in-the-loop (PIL) tests confirm real-time feasibility with execution times well below task periods and CPU usage under 0.1%. The results demonstrate KalmanNet’s superior accuracy, robustness, and computational efficiency for real-time EV applications.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Battery-Insight-PSO: A machine learning model for accurate prediction of state of health and remaining useful life in lithium-ion batteries","authors":"Md Fazle Hasan Shiblee,&nbsp;Hannu Laaksonen","doi":"10.1016/j.fub.2025.100114","DOIUrl":"10.1016/j.fub.2025.100114","url":null,"abstract":"<div><div>Condition based monitoring (CBM) of the lithium-ion (Li-ion) battery has become very popular in recent years because of its wide usage as an energy storage for smart grids, power sources in various industrial equipment, electric vehicles (EVs), etc. As a result, predicting the state of health (SOH) and the remaining useful life (RUL) of Li-ion batteries with high accuracy ensures optimal performance and safe utilization, preventing non-scheduled failures and saving maintenance costs. This paper illustrates the significance of highly accurate SOH and RUL prediction for Li-ion batteries. This paper proposes a model called Battery-Insight-PSO, which employs the Extreme Gradient Boosting Regression (XGBoost) machine learning algorithm to forecast SOH and RUL. In this study, the Particle Swarm Optimization Algorithm (PSO) is used to optimize different parameters of XGBoost for ensuring precise and reliable predictions of SOH and RUL for Li-ion batteries. In this study, the National Aeronautics and Space Administration (NASA) Li-ion Battery Aging Datasets and the NMC LCO 18650 battery dataset from the Hawaii Natural Energy Institute (HNEI) were analyzed. Additionally, the performance of Battery-Insight-PSO was compared with other machine learning algorithms. Machine learning models were evaluated using various performance metrics. The estimation errors of Battery-Insight-PSO are very low, which means that this model can be highly accurate in predicting SOH and RUL. Moreover, the R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> scores for the training and testing sets of this model also show high consistency with 0.9998 for each dataset, demonstrating high accuracy and reliable performance.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}