Pub Date : 2025-12-04DOI: 10.1016/j.fub.2025.100128
Kun Zhang , Maojun Zhou , Xiaoyang Zhao , Zhean Bao , Guosheng Duan , Luzhen Sun , Chunlei Wei , Menglian Zheng
Metallic zinc is a compelling anode material for aqueous zinc‑ion batteries (AZIBs), offering high theoretical capacity, low cost, and intrinsic safety. Nevertheless, uneven Zn plating and parasitic side reactions remain persistent challenges that limit cycle life and energy efficiency. To mitigate these issues, organic additives are widely employed, such as fluorinated anions decomposing to form protective F‑based solid electrolyte interphase (SEI), while organic cations promoting uniform Zn²⁺ solvation and distribution at the electrode interface. Despite extensive study of these species in isolation, the synergistic and competitive interactions between cations and anions have received little attention. Here, we introduce a multi‑scale simulation framework that involves density functional theory (DFT), classical molecular dynamics (MD) and ab initio molecular dynamics (AIMD) to probe both long‑range ion distribution and atomistic interfacial decomposition. Based on these insights, we propose electrolyte additive design rules that account for cation–anion interactions, and further use the ways of experiments to verify these conclusions from simulations. Our work underscores the importance of integrated additive engineering.
{"title":"Unraveling cation–anion coupling via classic molecular dynamics and ab initio molecular dynamics simulation: Design rules of organic additives for building fluorinated solid electrolyte interphase layer in zinc-ion batteries","authors":"Kun Zhang , Maojun Zhou , Xiaoyang Zhao , Zhean Bao , Guosheng Duan , Luzhen Sun , Chunlei Wei , Menglian Zheng","doi":"10.1016/j.fub.2025.100128","DOIUrl":"10.1016/j.fub.2025.100128","url":null,"abstract":"<div><div>Metallic zinc is a compelling anode material for aqueous zinc‑ion batteries (AZIBs), offering high theoretical capacity, low cost, and intrinsic safety. Nevertheless, uneven Zn plating and parasitic side reactions remain persistent challenges that limit cycle life and energy efficiency. To mitigate these issues, organic additives are widely employed, such as fluorinated anions decomposing to form protective F‑based solid electrolyte interphase (SEI), while organic cations promoting uniform Zn²⁺ solvation and distribution at the electrode interface. Despite extensive study of these species in isolation, the synergistic and competitive interactions between cations and anions have received little attention. Here, we introduce a multi‑scale simulation framework that involves density functional theory (DFT), classical molecular dynamics (MD) and ab initio molecular dynamics (AIMD) to probe both long‑range ion distribution and atomistic interfacial decomposition. Based on these insights, we propose electrolyte additive design rules that account for cation–anion interactions, and further use the ways of experiments to verify these conclusions from simulations. Our work underscores the importance of integrated additive engineering.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"9 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685876","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}
Pub Date : 2025-12-01DOI: 10.1016/j.fub.2025.100123
G.S. Sathyanarayanan, Sanath Kumar, Yen-Pei Fu
A gel polymer-based electrolyte (GPE) is developed by infusing SiO₂ and cross-linking with polyethylene oxide (PEO) into a sodium polyacrylate (PANa)-bonded network, which significantly enhances the mechanical properties and ionic conductivity of GPE. Impressively, optimized GPE delivered room temperature ionic conductivity up to 353 mS/cm2 and showed the elongation capability of over 500 %, hence making it an efficient GPE for flexible electronics. An air cathode was also developed using a CoP-based electrocatalyst, which was confirmed through various characterization tools, including X-ray diffraction studies, X-ray photoelectron spectroscopic studies, and morphological analysis. Upon electrochemical analysis, the developed CoP-based electrocatalysts exhibited an OER overpotential of 1.47 V and an ORR half-wave potential of 0.65 V, which is comparable to that of standard RuO₂-Pt/C electrocatalysts. As developed CoP electrocatalysts surpassed standard electrocatalysts, the CoP-based air cathode was developed to create a flexible Zinc air battery (FZAB) with optimized GPE. The FZAB with optimized GPE and CoP-based air cathode exhibited an operating potential of 1.65 V, a peak power density of 93 mW/cm², and stability of over 95 h, which is also compared with RuO₂/Pt-C-based air cathode-based FZAB. The work here provides a promising approach to developing desirable hydrogel electrolytes for FZAB applications.
{"title":"A flexible zinc–air battery with a high ionic conductivity gel polymer electrolyte and CoP-based bifunctional air cathode","authors":"G.S. Sathyanarayanan, Sanath Kumar, Yen-Pei Fu","doi":"10.1016/j.fub.2025.100123","DOIUrl":"10.1016/j.fub.2025.100123","url":null,"abstract":"<div><div>A gel polymer-based electrolyte (GPE) is developed by infusing SiO₂ and cross-linking with polyethylene oxide (PEO) into a sodium polyacrylate (PANa)-bonded network, which significantly enhances the mechanical properties and ionic conductivity of GPE. Impressively, optimized GPE delivered room temperature ionic conductivity up to 353 mS/cm<sup>2</sup> and showed the elongation capability of over 500 %, hence making it an efficient GPE for flexible electronics. An air cathode was also developed using a CoP-based electrocatalyst, which was confirmed through various characterization tools, including X-ray diffraction studies, X-ray photoelectron spectroscopic studies, and morphological analysis. Upon electrochemical analysis, the developed CoP-based electrocatalysts exhibited an OER overpotential of 1.47 V and an ORR half-wave potential of 0.65 V, which is comparable to that of standard RuO₂-Pt/C electrocatalysts. As developed CoP electrocatalysts surpassed standard electrocatalysts, the CoP-based air cathode was developed to create a flexible Zinc air battery (FZAB) with optimized GPE. The FZAB with optimized GPE and CoP-based air cathode exhibited an operating potential of 1.65 V, a peak power density of 93 mW/cm², and stability of over 95 h, which is also compared with RuO₂/Pt-C-based air cathode-based FZAB. The work here provides a promising approach to developing desirable hydrogel electrolytes for FZAB applications.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624155","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}
Lithium-ion batteries are vital for large-scale industries, especially in transport and renewable energy applications, due to their high energy density, extended cycle life, and low self-discharge rate as compared to other battery types. With increasing demand for sustainable and energy-efficient solutions, it is critical to study, understand, and improve the performance of batteries. Monitoring degradation is important, but acquiring real-time sensor data over lengthy periods is challenging due to the longer life cycles of batteries. This makes data collection costly and time-consuming. Cell-based open-source datasets provide a viable alternative, allowing researchers to estimate the degradation of battery cells without the requirement for constant, real-time testing. Furthermore, estimating degradation factors is crucial for forecasting Remaining Useful Life and extending battery lifespan. Methods such as adaptive filtering techniques, machine learning approaches, etc., have demonstrated reliable solutions in simulating battery degradation. This paper reviews the battery cell degradation mechanisms, followed by the prediction of battery health parameters and relevant degradation modelling approaches for individual cells. The purpose of this review is to provide a structured analysis of how different modelling methods capture degradation behavior, to identify their strengths and limitations, and to clarify how they can be applied for battery health prediction. It also highlights the importance of datasets required for developing predictive models and summarizes open-source datasets based on the chemistry, cycling process, and their key features.
{"title":"Predicting remaining useful life of lithium-ion batteries: A review of degradation mechanisms and open-source data availability","authors":"Kishan Patel , Vaidehi Gosala , Merten Stender , Moritz Braun , Sören Ehlers","doi":"10.1016/j.fub.2025.100124","DOIUrl":"10.1016/j.fub.2025.100124","url":null,"abstract":"<div><div>Lithium-ion batteries are vital for large-scale industries, especially in transport and renewable energy applications, due to their high energy density, extended cycle life, and low self-discharge rate as compared to other battery types. With increasing demand for sustainable and energy-efficient solutions, it is critical to study, understand, and improve the performance of batteries. Monitoring degradation is important, but acquiring real-time sensor data over lengthy periods is challenging due to the longer life cycles of batteries. This makes data collection costly and time-consuming. Cell-based open-source datasets provide a viable alternative, allowing researchers to estimate the degradation of battery cells without the requirement for constant, real-time testing. Furthermore, estimating degradation factors is crucial for forecasting Remaining Useful Life and extending battery lifespan. Methods such as adaptive filtering techniques, machine learning approaches, etc., have demonstrated reliable solutions in simulating battery degradation. This paper reviews the battery cell degradation mechanisms, followed by the prediction of battery health parameters and relevant degradation modelling approaches for individual cells. The purpose of this review is to provide a structured analysis of how different modelling methods capture degradation behavior, to identify their strengths and limitations, and to clarify how they can be applied for battery health prediction. It also highlights the importance of datasets required for developing predictive models and summarizes open-source datasets based on the chemistry, cycling process, and their key features.</div></div>","PeriodicalId":100560,"journal":{"name":"Future Batteries","volume":"8 ","pages":"Article 100124"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624156","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}
Pub Date : 2025-12-01DOI: 10.1016/j.fub.2025.100126
Chun-Jern Pan , Cian-Ping Lin , Shih-Che Lin , Yi-Yu Chen , Bing-Joe Hwang , Chia-Hsin Wang , Wei-Hsiang Huang , Chun-I Lee
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 (SO), and EC (CO), 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.
锂金属电池（lmb）比锂离子电池（lib）具有更高的能量密度，但存在枝晶生长、库仑效率低和安全问题。本研究介绍了一种以硝酸锂（LiNO3）为锂盐，以二氟（草酸）硼酸锂（LiDFOB）为功能添加剂，以亚砜（SL）与碳酸乙烯（EC）混合为混合溶剂的深共晶电解质（DEE）。优化后的电解质组成LSEC-B4具有中等粘度（27.5 cP）和最高电导率（1.15 mS/cm）。热分析证实了其优越的热稳定性和抗结晶性，归因于EC和LiDFOB的协同作用。光谱研究表明，LSEC-B4通过调节与NO3-、SL （SO）和EC （CO）的相互作用来调节Li+的溶剂化，形成稳定的配位环境。这增强了离子传输并稳定了固体电解质界面（SEI）。LSEC-B4在Li/ Cu电池中表现出优异的性能，平均库仑效率为98.27 %，过电位低，保证了Li沉积和剥离的便捷性和可逆性。在Li//Li电池中，它能维持400 h以上的稳定循环，电压波动最小，证实了长期的界面稳定性和抑制枝晶生长。与LiMn2O4 （LMO）阴极配对，Li//LMO电池在300 mAg - 1下循环500次后，容量保持率为84% % (85 mAh g - 1)，平均库仑效率为~ 99 %。与商用电解质不同，LSEC-B4的可燃性测试突出了其卓越的安全性，得益于LiNO3和SL的阻燃特性，LSEC-B4能够抗燃，而LiDFOB则增强了SEI的稳定性，减轻了热失控。总体而言，LSEC-B4结合了导电性、稳定性、安全性和阴极兼容性，为开发实用、安全、高效的lmb提供了一条有希望的途径。
{"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 , Cian-Ping Lin , Shih-Che Lin , Yi-Yu Chen , Bing-Joe Hwang , Chia-Hsin Wang , Wei-Hsiang Huang , 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 (LiNO<sub>3</sub>) 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<sup>+</sup> solvation by regulating interactions with NO<sub>3</sub><sup>-</sup>, 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 LiMn<sub>2</sub>O<sub>4</sub> (LMO) cathode, Li//LMO cells achieve 84 % capacity retention (85 mAh g<sup>−1</sup>) after 500 cycles at 300 mAg<sup>−1</sup> and ∼99 % average coulombic efficiency. Flammability tests highlight remarkable safety, unlike commercial electrolytes, LSEC-B4 resists ignition, benefiting from the flame-retardant nature of LiNO<sub>3</sub> 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}
Pub Date : 2025-12-01DOI: 10.1016/j.fub.2025.100125
Mufeng Wei
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.
{"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}
Pub Date : 2025-11-17DOI: 10.1016/j.fub.2025.100122
Adeola Ajoke Oni , Oluwafemi Babatunde Olasilola , Francis T. Omigbodun , Amirlahi Ademola Fajingbesi , Funso P. Adeyekun
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.
{"title":"Faster, safer batteries: A smarter way to bring technology to market","authors":"Adeola Ajoke Oni , Oluwafemi Babatunde Olasilola , Francis T. Omigbodun , Amirlahi Ademola Fajingbesi , 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}
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.
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.
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).
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.
{"title":"Co-estimation of Li-ion battery states using an improved dynamic model for electric vehicles","authors":"Nouhaila Belmajdoub , Rachid Lajouad , Abdelmounime El Magri , 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}
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.
{"title":"Current practices and advances in thermal runaway modelling: A detailed review","authors":"Pranav Cherukat , Prabhu Selvaraj , Srujan V.G. , Balamurugan Rathinam , 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}
Pub Date : 2025-11-05DOI: 10.1016/j.fub.2025.100120
Arber Avdyli , Otto von Kessel , Kai Peter Birke , Alexander Fill
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.
{"title":"Differentiating the effects of pressure in NMC-graphite lithium-ion batteries on cell and system level","authors":"Arber Avdyli , Otto von Kessel , Kai Peter Birke , 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}
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.
{"title":"Partial least squares model method for state of health prediction of lithium-ion batteries","authors":"Eugenio Sandrucci , Sergio Brutti , 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}
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