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Journal Description

Batteries

Batteries is an international, peer-reviewed, open access journal on battery technology and materials published monthly online by MDPI. International Society for Porous Media (InterPore) is affiliated with Batteries and their members receive discounts on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, Ei Compendex, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q2 (Electrochemistry) / CiteScore - Q1 (Electrical and Electronic Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.5 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Sections: published in 7 topical sections.

Impact Factor: 4.8 (2024); 5-Year Impact Factor: 5.2 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2313-0105

Latest Articles

15 pages, 2042 KiB

Open AccessArticle

Predictive Framework for Lithium Plating Risk in Fast-Charging Lithium-Ion Batteries: Linking Kinetics, Thermal Activation, and Energy Loss

by Junais Habeeb Mokkath

Batteries 2025, 11(8), 281; https://doi.org/10.3390/batteries11080281 (registering DOI) - 22 Jul 2025

Fast charging accelerates lithium-ion battery operation but increases the risk of lithium (Li) plating—a process that undermines efficiency, longevity, and safety. Here, we introduce a predictive modeling framework that captures the onset and severity of Li plating under practical fast-charging conditions. By integrating [...] Read more.

Fast charging accelerates lithium-ion battery operation but increases the risk of lithium (Li) plating—a process that undermines efficiency, longevity, and safety. Here, we introduce a predictive modeling framework that captures the onset and severity of Li plating under practical fast-charging conditions. By integrating an empirically parameterized SOC threshold model with time-dependent kinetic simulations and Arrhenius based thermal analysis, we delineate operating regimes prone to irreversible Li accumulation. The framework distinguishes reversible and irreversible plating fractions, quantifies energy losses, and identifies a critical activation energy (0.25 eV) associated with surface-limited deposition. Visualizations in the form of severity maps and voltage-zone risk classifications enable direct application to battery management systems. This approach bridges electrochemical degradation modeling with real-time charge protocol design, offering a practical tool for safe, high-performance battery operation. Full article

(This article belongs to the Section Battery Modelling, Simulation, Management and Application)

21 pages, 4023 KiB

Open AccessArticle

Numerical Study on the Thermal Behavior of Lithium-Ion Batteries Based on an Electrochemical–Thermal Coupling Model

by Xing Hu, Hu Xu, Chenglin Ding, Yupeng Tian and Kuo Yang

Batteries 2025, 11(7), 280; https://doi.org/10.3390/batteries11070280 - 21 Jul 2025

The escalating demand for efficient thermal management in lithium-ion batteries necessitates precise characterization of their thermal behavior under diverse operating conditions. This study develops a three-dimensional (3D) electrochemical–thermal coupling model grounded in porous electrode theory and energy conservation principles. The model solves multi-physics [...] Read more.

The escalating demand for efficient thermal management in lithium-ion batteries necessitates precise characterization of their thermal behavior under diverse operating conditions. This study develops a three-dimensional (3D) electrochemical–thermal coupling model grounded in porous electrode theory and energy conservation principles. The model solves multi-physics equations such as Fick’s law, Ohm’s law, and the Butler–Volmer equation, to resolve coupled electrochemical and thermal dynamics, with temperature-dependent parameters calibrated via the Arrhenius equation. Simulations under varying discharge rates reveal that high-rate discharges exacerbate internal heat accumulation. Low ambient temperatures amplify polarization effects. Forced convection cooling reduces surface temperatures but exacerbates core-to-surface thermal gradients. Structural optimization strategies demonstrate that enhancing through-thickness thermal conductivity reduces temperature differences. These findings underscore the necessity of balancing energy density and thermal management in lithium-ion battery design, proposing actionable insights such as preheating protocols for low-temperature operation, optimized cooling systems for high-rate scenarios, and material-level enhancements for improved thermal uniformity. Full article

(This article belongs to the Special Issue High-Performance Secondary Batteries: Recent Processes and Future Challenges)

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17 pages, 5522 KiB

Open AccessArticle

Control and Management of Multi-Agent Systems Using Fuzzy Logic for Microgrids

by Zineb Cabrane, Mohammed Ouassaid, Donghee Choi and Soo Hyoung Lee

Batteries 2025, 11(7), 279; https://doi.org/10.3390/batteries11070279 - 21 Jul 2025

The existing standalone microgrids (MGs) require good energy management systems (EMSs) to respond to energy needs. The EMS presented in this paper is used for an MG based on PV and wind energy sources. The energy storage system is implemented using three packs [...] Read more.

The existing standalone microgrids (MGs) require good energy management systems (EMSs) to respond to energy needs. The EMS presented in this paper is used for an MG based on PV and wind energy sources. The energy storage system is implemented using three packs of batteries. Power smoothing is carried out via the introduction of supercapacitors (SCs) in parallel to the loads and sources. The distribution of energy of the presented MG is focused on the multi-agent system (MAS) using Fuzzy Logic Supervisor control. The MAS is used in order to leverage autonomous and interacting agents to optimize operations and achieve system objectives. To reduce the stress on batteries and avoid damaging all the batteries together by the charge and discharge cycles, one pack of batteries can usually be used. When this pack of batteries is fully discharged and there is a need for energy, it can be taken from another pack of batteries. The same analysis applies to the charge; when batteries of the first pack are fully charged and there is a surplus of energy, it can be stored in other packs of batteries. Two simulation results are used to demonstrate the efficiency of the EMS control used. These simulation tests are proposed with and without SCs. Full article

(This article belongs to the Section Battery Modelling, Simulation, Management and Application)

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15 pages, 2596 KiB

Open AccessArticle

Comprehensive Experimental Investigation of Operational Parameter Sensitivity in Proton Exchange Membrane Fuel Cell Performance

by Renhua Feng, Zhanye Hua, Jing Yu, Shaoyang Wang, Laihua Shi, Xing Shu, Ziyi Yan and Jiayi Guo

Batteries 2025, 11(7), 278; https://doi.org/10.3390/batteries11070278 - 21 Jul 2025

In this study, the sensitivity of operating parameters such as the hydrogen and air excess coefficient, cathode inlet pressure, intake relative humidity, and coolant inlet temperature and their effects on the performance of single proton exchange membrane fuel cells (PEMFCs) are experimentally assessed. [...] Read more.

In this study, the sensitivity of operating parameters such as the hydrogen and air excess coefficient, cathode inlet pressure, intake relative humidity, and coolant inlet temperature and their effects on the performance of single proton exchange membrane fuel cells (PEMFCs) are experimentally assessed. The results revealed that the fuel cell node voltage increases as the hydrogen and air excess coefficient increases, and the impact of the hydrogen and air excess coefficient on the fuel cell node voltage gradually increases as the current density increases. However, a higher hydrogen and air excess coefficient is not always better. The node voltage increases as the intake pressure increases. However, it is not that a higher intake pressure is always better, but rather that there is an optimal intake pressure value to achieve the best overall performance of the fuel cell. The node voltage increases as the coolant inlet temperature increases at most fuel cell current densities. However, the optimum fuel cell operating inlet temperature is not necessarily higher, as the coolant inlet temperature may have a strong coupling relationship with other operating conditions that will also affect the fuel cell performance. The fuel cell operating inlet temperature may have a strong coupling relationship with the intake relative humidity, and both of these parameters must be well-matched to achieve better fuel cell performance. Full article

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20 pages, 2939 KiB

Open AccessArticle

Investigations of Dongyue Series Perfluorosulfonic Acid Membranes for Applications in Proton Exchange Membrane Fuel Cells (PEMFCs)

by Ge Meng, Xiang Li, Mengjie Liu, Sergey A. Grigoriev, Ivan Tolj, Jiaqi Shen, Chaonan Yue and Chuanyu Sun

Batteries 2025, 11(7), 277; https://doi.org/10.3390/batteries11070277 - 20 Jul 2025

This study systematically investigated the physicochemical properties and proton exchange membrane fuel cell (PEMFC) performance of perfluorosulfonic acid (PFSA) membranes with different thicknesses, which were prepared based on the resins produced by Dongyue (China) in comparison with commercial Nafion membranes. It was found [...] Read more.

This study systematically investigated the physicochemical properties and proton exchange membrane fuel cell (PEMFC) performance of perfluorosulfonic acid (PFSA) membranes with different thicknesses, which were prepared based on the resins produced by Dongyue (China) in comparison with commercial Nafion membranes. It was found that the water uptake of Dongyue membranes is significantly higher than that of Nafion, showing a significant upward trend with the thickness increase. The ion exchange capacity (IEC) of these membranes is ca. 1 mmol·g⁻¹. Moreover, the tensile strength of the Dongyue membrane was positively correlated with the thickness and was significantly higher than that of recast Nafion. Under 80 °C, all Dongyue membranes with various thicknesses (15~45 μm) exhibited PEMFC single-cell performance superior to that of Nafion. The maximum power density is observed with a thickness of 25 μm, reaching 851.76 mW·cm⁻², which is higher than that of Nafion (635.99 mW·cm⁻²). However, the oxidative stability of the prepared Dongyue PFSA series membranes exhibits a slight deficit compared to commercial Nafion membranes. Subsequently, the modification and optimization of preparation processes can be employed to improve the mechanical and chemical stability of Dongyue PFSA membranes. Full article

(This article belongs to the Special Issue Batteries: 10th Anniversary)

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21 pages, 2547 KiB

Open AccessArticle

Remaining Available Energy Prediction for Energy Storage Batteries Based on Interpretable Generalized Additive Neural Network

by Ji Qi, Pengrui Li, Yifan Dong, Zhicheng Fu, Zhanguo Wang, Yong Yi and Jie Tian

Batteries 2025, 11(7), 276; https://doi.org/10.3390/batteries11070276 - 20 Jul 2025

Precise estimation of the remaining available energy in batteries is not only key to improving energy management efficiency, but also serves as a critical safeguard for ensuring the safe operation of battery systems. To address the challenges associated with energy state estimation under [...] Read more.

Precise estimation of the remaining available energy in batteries is not only key to improving energy management efficiency, but also serves as a critical safeguard for ensuring the safe operation of battery systems. To address the challenges associated with energy state estimation under dynamic operating conditions, this study proposes a method for predicting the remaining available energy of energy storage batteries based on an interpretable generalized additive neural network (IGANN). First, considering the variability in battery operating conditions, the study designs a battery working voltage threshold that accounts for safety margins and proposes an available energy state assessment metric, which enhances prediction consistency under different discharge conditions. Subsequently, 12 features are selected from both direct observation and statistical characteristics to capture the operating condition information of the battery, and a dataset is constructed using actual operational data from an energy storage station. Finally, the model is trained and validated on the feature dataset. The validation results show that the model achieves an average absolute error of 2.39%, indicating that it effectively captures the energy variation characteristics within the 0.2 C to 0.6 C dynamic current range. Furthermore, the contribution of each feature is analyzed based on the model’s interpretability, and the model is optimized by utilizing high-contribution features. This optimization improves both the accuracy and runtime efficiency of the model. Finally, a dynamic prediction is conducted for a discharge cycle, comparing the predictions of the IGANN model with those of three other machine learning methods. The IGANN model demonstrates the best performance, with the average absolute error consistently controlled within 3%, proving the model’s accuracy and robustness under complex conditions. Full article

(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)

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49 pages, 15060 KiB

Open AccessReview

A Comprehensive Review of Thermal Management Challenges and Safety Considerations in Lithium-Ion Batteries for Electric Vehicles

by Ali Alawi, Ahmed Saeed, Mostafa H. Sharqawy and Mohammad Al Janaideh

Batteries 2025, 11(7), 275; https://doi.org/10.3390/batteries11070275 - 19 Jul 2025

The transition to electric vehicles (EVs) is accelerating due to global efforts to reduce greenhouse gas emissions and reliance on fossil fuels. Lithium-ion batteries (LIBs) are the predominant energy storage solution in EVs, offering high energy density, efficiency, and long lifespan. However, their [...] Read more.

The transition to electric vehicles (EVs) is accelerating due to global efforts to reduce greenhouse gas emissions and reliance on fossil fuels. Lithium-ion batteries (LIBs) are the predominant energy storage solution in EVs, offering high energy density, efficiency, and long lifespan. However, their adoption is overly involved with critical safety concerns, including thermal runaway and overheating. This review systematically focuses on the critical role of battery thermal management systems (BTMSs), such as active, passive, and hybrid cooling systems, in maintaining LIBs within their optimal operating temperature range, ensuring temperature homogeneity, safety, and efficiency. Additionally, the study explores the impact of integrating artificial intelligence (AI) and machine learning (ML) into BTMS on thermal performance prediction and energy-efficient cooling, focusing on optimizing the operating parameters of cooling systems. This review provides insights into enhancing LIB safety and performance for widespread EV adoption by addressing these challenges. Full article

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16 pages, 2291 KiB

Open AccessArticle

State of Charge Estimation for Sodium-Ion Batteries Based on LSTM Network and Unscented Kalman Filter

by Xiangang Zuo, Xiaoheng Fu, Xu Han, Meng Sun and Yuqian Fan

Batteries 2025, 11(7), 274; https://doi.org/10.3390/batteries11070274 - 18 Jul 2025

With the increasing application of sodium-ion batteries in energy storage systems, accurate state of charge (SOC) estimation plays a vital role in ensuring both available battery capacity and operational safety. Traditional Kalman-filter-based methods often suffer from limited model expressiveness or oversimplified physical assumptions, [...] Read more.

With the increasing application of sodium-ion batteries in energy storage systems, accurate state of charge (SOC) estimation plays a vital role in ensuring both available battery capacity and operational safety. Traditional Kalman-filter-based methods often suffer from limited model expressiveness or oversimplified physical assumptions, making it difficult to balance accuracy and robustness under complex operating conditions, which may lead to unreliable estimation results. To address these challenges, this paper proposes a hybrid framework that combines an unscented Kalman filter (UKF) with a long short-term memory (LSTM) neural network for SOC estimation. Under various driving conditions, the UKF—based on a second-order equivalent circuit model with online parameter identification—provides physically interpretable estimates, while LSTM effectively captures complex temporal dependencies. Experimental results under CLTC, NEDC, and WLTC cycles demonstrate that the proposed LSTM-UKF approach reduces the mean absolute error (MAE) by an average of 2% and the root mean square error (RMSE) by an average of 3% compared to standalone methods. The proposed framework exhibits excellent adaptability across different scenarios, offering a precise, stable, and robust solution for SOC estimation in sodium-ion batteries. Full article

(This article belongs to the Section Battery Modelling, Simulation, Management and Application)

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15 pages, 1845 KiB

Open AccessArticle

Comparing the SEI Formation on Copper and Amorphous Carbon: A Study with Combined Operando Methods

by Michael Stich, Christian Leppin, Falk Thorsten Krauss, Jesus Eduardo Valdes Landa, Isabel Pantenburg, Bernhard Roling and Andreas Bund

Batteries 2025, 11(7), 273; https://doi.org/10.3390/batteries11070273 - 18 Jul 2025

The solid electrolyte interphase (SEI) on the anode of lithium-ion batteries (LIBs) has been studied thoroughly due to its crucial importance to the battery’s long-term performance. At the same time, most studies of the SEI apply ex situ characterization methods, which may introduce [...] Read more.

The solid electrolyte interphase (SEI) on the anode of lithium-ion batteries (LIBs) has been studied thoroughly due to its crucial importance to the battery’s long-term performance. At the same time, most studies of the SEI apply ex situ characterization methods, which may introduce artifacts or misinterpretations as they do not investigate the SEI in its unaltered state immersed in liquid battery electrolyte. Thus, in this work, we focus on using the non-destructive combination of electrochemical quartz crystal microbalance with dissipation monitoring (EQCM-D) and impedance spectroscopy (EIS) in the same electrochemical cell. EQCM-D can not only probe the solidified products of the SEI but also allows for the monitoring of viscoelastic layers and viscosity changes of the electrolyte at the interphase during the SEI formation. EIS complements those results by providing electrochemical properties of the formed interphase. Our results highlight substantial differences in the physical and electrochemical properties between the SEI formed on copper and on amorphous carbon and show how formation parameters and the additive vinylene carbonate (VC) influence their growth. The EQCM-D results show consistently that much thicker SEIs are formed on carbon substrates in comparison to copper substrates. Full article

(This article belongs to the Special Issue Electrocrystallization in Rechargeable Batteries)

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23 pages, 7016 KiB

Open AccessArticle

SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM

by Panpan Hu, Chun Yin Li and Chi Chung Lee

Batteries 2025, 11(7), 272; https://doi.org/10.3390/batteries11070272 - 17 Jul 2025

Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage [...] Read more.

Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R² of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs. Full article

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19 pages, 15854 KiB

Open AccessArticle

Failure Analysis of Fire in Lithium-Ion Battery-Powered Heating Insoles: Case Study

by Rong Yuan, Sylvia Jin and Glen Stevick

Batteries 2025, 11(7), 271; https://doi.org/10.3390/batteries11070271 - 17 Jul 2025

This study investigates a lithium-ion battery failure in heating insoles that ignited during normal walking while powered off. Through comprehensive material characterization, electrical testing, thermal analysis, and mechanical gait simulation, we systematically excluded electrical or thermal abuse as failure causes. X-ray/CT imaging localized [...] Read more.

This study investigates a lithium-ion battery failure in heating insoles that ignited during normal walking while powered off. Through comprehensive material characterization, electrical testing, thermal analysis, and mechanical gait simulation, we systematically excluded electrical or thermal abuse as failure causes. X-ray/CT imaging localized the ignition source to the lateral heel edge of the pouch cell, correlating precisely with peak mechanical stress identified through gait analysis. Remarkably, the cyclic load was less than 10% of the single crush load threshold specified in safety standards. Key findings reveal multiple contributing factors as follows: the uncoated polyethylene separator’s inability to prevent stress-induced internal short circuits, the circuit design’s lack of battery health monitoring functionality that permitted undetected degradation, and the hazardous placement inside clothing that exacerbated burn injuries. These findings necessitate a multi-level safety framework for lithium-ion battery products, encompassing enhanced cell design to prevent internal short circuit, improved circuit protection with health monitoring capabilities, optimized product integration to mitigate mechanical and environmental impact, and effective post-failure containment measures. This case study exposes a critical need for product-specific safety standards that address the unique demands of wearable lithium-ion batteries, where existing certification requirements fail to prevent real-use failure scenarios. Full article

(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)

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17 pages, 4099 KiB

Open AccessArticle

Tetramethylene Sulfone (TMS) as an Electrolyte Additive for High-Power Lithium-Ion Batteries

by Wenting Liu, Gangxin Chen, Ningfeng Wang, Xianzhong Sun, Chen Li, Yanan Xu, Xiaohu Zhang, Xiong Zhang and Kai Wang

Batteries 2025, 11(7), 270; https://doi.org/10.3390/batteries11070270 - 17 Jul 2025

High-power lithium-ion batteries impose stringent requirements on output power. Tetramethylene sulfone (TMS), serving as a novel electrolyte additive, effectively enhances the stability of electrolytes under high-voltage conditions due to its high flash point and high dielectric constant, thereby boosting the output performance of [...] Read more.

High-power lithium-ion batteries impose stringent requirements on output power. Tetramethylene sulfone (TMS), serving as a novel electrolyte additive, effectively enhances the stability of electrolytes under high-voltage conditions due to its high flash point and high dielectric constant, thereby boosting the output performance of lithium-ion batteries. In this work, we selected lithium hexafluorophosphate (LiPF₆) as the lithium salt, using a solvent carrier consisting of a mixture of ethylene carbonate (EC), dimethyl carbonate (DMC), and ethyl methyl carbonate (EMC). TMS was added as an additive to create a novel high-power electrolyte system. We prepared five electrolytes with different TMS concentrations and conducted in-depth investigations into their impacts on the performance of lithium-ion batteries. The findings indicate that the electrolytes with TMS ratios of 2 wt% and 5 wt% demonstrated good synergistic cathode–anode stability in the NCM//soft carbon system, and the electrolyte with a 5 wt% TMS ratio demonstrated the most significant improvement in the overall performance of the full battery. Full article

(This article belongs to the Special Issue Lithium-Ion Batteries and Li-Ion Capacitors: From Fundamentals to Practical Applications: 2nd Edition)

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24 pages, 2152 KiB

Open AccessReview

A Concise Overview of the Use of Low-Dimensional Molybdenum Disulfide as an Electrode Material for Li-Ion Batteries and Beyond

by Mattia Bartoli, Meltem Babayiğit Cinali, Özlem Duyar Coşkun, Silvia Porporato, Diego Pugliese, Erik Piatti, Francesco Geobaldo, Giuseppe A. Elia, Claudio Gerbaldi, Giuseppina Meligrana and Alessandro Piovano

Batteries 2025, 11(7), 269; https://doi.org/10.3390/batteries11070269 - 16 Jul 2025

The urgent demand for sustainable energy solutions in the face of climate change and resource depletion has catalyzed a global shift toward cleaner energy production and more efficient storage technologies. Lithium-ion batteries (LIBs), as the cornerstone of modern portable electronics, electric vehicles, and [...] Read more.

The urgent demand for sustainable energy solutions in the face of climate change and resource depletion has catalyzed a global shift toward cleaner energy production and more efficient storage technologies. Lithium-ion batteries (LIBs), as the cornerstone of modern portable electronics, electric vehicles, and grid-scale storage systems, are continually evolving to meet the growing performance requirements. In this dynamic context, two-dimensional (2D) materials have emerged as highly promising candidates for use in electrodes due to their layered structure, tunable electronic properties, and high theoretical capacity. Among 2D materials, molybdenum disulfide (MoS₂) has gained increasing attention as a promising low-dimensional candidate for LIB anode applications. This review provides a comprehensive yet concise overview of recent advances in the application of MoS₂ in LIB electrodes, with particular attention to its unique electrochemical behavior at the nanoscale. We critically examine the interplay between structural features, charge-storage mechanisms, and performance metrics—chiefly the specific capacity, rate capability, and cycling stability. Furthermore, we discuss current challenges, primarily poor intrinsic conductivity and volume fluctuations, and highlight innovative strategies aimed at overcoming these limitations, such as through nanostructuring, composite formation, and surface engineering. By shedding light on the opportunities and hurdles in this rapidly progressing field, this work offers a forward-looking perspective on the role of MoS₂ in the next generation of high-performance LIBs. Full article

(This article belongs to the Section Battery Mechanisms and Fundamental Electrochemistry Aspects)

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16 pages, 4059 KiB

Open AccessArticle

Surface Fluorination for the Stabilization in Air of Garnet-Type Oxide Solid Electrolyte for Lithium Ion Battery

by Michael Herraiz, Saida Moumen, Kevin Lemoine, Laurent Jouffret, Katia Guérin, Elodie Petit, Nathalie Gaillard, Laure Bertry, Reka Toth, Thierry Le Mercier, Valérie Buissette and Marc Dubois

Batteries 2025, 11(7), 268; https://doi.org/10.3390/batteries11070268 - 16 Jul 2025

After reviewing the state of the art of the fluorination of inorganic solid electrolytes, an application of gas/solid fluorination is given and how it can be processed. Garnet-type oxide has been chosen. These oxides with an ideal structure of chemical formula A₃ [...] Read more.

After reviewing the state of the art of the fluorination of inorganic solid electrolytes, an application of gas/solid fluorination is given and how it can be processed. Garnet-type oxide has been chosen. These oxides with an ideal structure of chemical formula A₃B₂(XO₄)₃ are mainly known for their magnetic and dielectric properties. Certain garnets may have a high enough Li⁺ ionic conductivity to be used as solid electrolyte of lithium ion battery. The surface of LLZO may be changed in contact with the moisture and CO₂ present in the atmosphere that results in a change of the conductivity at the interface of the solid. LiOH and/or lithium carbonate are formed at the surface of the garnet particles. In order to allow for handling and storage under normal conditions of this solid electrolyte, surface fluorination was performed using elemental fluorine. When controlled using mild conditions (temperature lower or equal to 200 °C, either in static or dynamic mode), the addition of fluorine atoms to LLZO with Li_6,4Al_0,2La₃Zr₂O₁₂ composition is limited to the surface, forming a covering layer of lithium fluoride LiF. The effect of the fluorination was evidenced by IR, Raman, and NMR spectroscopies. If present in the pristine LLZO powder, then the carbonate groups disappear. More interestingly, contrary to the pristine LLZO, the contents of these groups are drastically reduced even after storage in air up to 45 days when the powder is covered with the LiF layer. Surface fluorination could be applied to other solid electrolytes that are air sensitive. Full article

(This article belongs to the Special Issue Electrolyte and Electrode Design for Next-Generation Rechargeable Batteries)

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18 pages, 4053 KiB

Open AccessArticle

Comprehensive Study of the Gas Volume and Composition Produced by Different 3–230 Ah Lithium Iron Phosphate (LFP) Cells Failed Using External Heat, Overcharge and Nail Penetration Under Air and Inert Atmospheres

by Gemma E. Howard, Jonathan E. H. Buston, Jason Gill, Steven L. Goddard, Jack W. Mellor and Philip A. P. Reeve

Batteries 2025, 11(7), 267; https://doi.org/10.3390/batteries11070267 - 16 Jul 2025

This paper reports on the failure of cells with lithium iron phosphate (LFP) chemistry tested under a range of conditions to understand their effect on the volume and composition of gas generated. Cells of the following formats, 26,650, pouch, and prismatic, and capacities [...] Read more.

This paper reports on the failure of cells with lithium iron phosphate (LFP) chemistry tested under a range of conditions to understand their effect on the volume and composition of gas generated. Cells of the following formats, 26,650, pouch, and prismatic, and capacities ranging from 3 to 230 Ah, were subjected to external heat, overcharge, and nail penetration tests. Gas volume was calculated, and the following gases analysed: H₂, CO₂, CO, CH₄, C₂H₄, C₂H₆, C₃H₆, and C₃H₈. Cells that failed via external heating under inert conditions (N₂ or Ar atmosphere) at 100% state of charge (SoC) typically generated 0.7 L/Ah of gas; overcharged cells, 0.11–0.68 L/Ah; and nail penetration between 0.3 and 0.5 L/Ah. In general, for all test configurations, regardless of atmosphere, the total gas volume contained a 40% concentration of H₂, 15% of CO₂, and the remaining gas consisted of varying concentrations of CO and flammable hydrocarbons. This demonstrates that despite differences in gas volume, the failure gas composition of LFP cells remains similar. Full article

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20 pages, 1818 KiB

Open AccessArticle

Interfacial Layer (“Interlayer”) Addition to Improve Active Material Utilisation in Lithium–Sulfur Batteries: Use of a Phenylsulfonated MWCNT Film

by Luke D. J. Barter, Steven J. Hinder, John F. Watts, Robert C. T. Slade and Carol Crean

Batteries 2025, 11(7), 266; https://doi.org/10.3390/batteries11070266 - 16 Jul 2025

Films of functionalised multiwalled carbon nanotubes (MWCNTs) were fabricated as interlayers (interfacial layers between the cathode and separator) in a lithium–sulfur battery (LSB). Phenylsulfonate functionalisation of commercial MWCNTs was achieved via diazotisation to attach lithium phenylsulfonate groups and was characterised by IR and [...] Read more.

Films of functionalised multiwalled carbon nanotubes (MWCNTs) were fabricated as interlayers (interfacial layers between the cathode and separator) in a lithium–sulfur battery (LSB). Phenylsulfonate functionalisation of commercial MWCNTs was achieved via diazotisation to attach lithium phenylsulfonate groups and was characterised by IR and XPS spectroscopies. SEM-EDX showed sulfur and oxygen colocations due to the sulfonate groups on the interlayer surface. However, CHNS elemental microstudies showed a low degree of functionalisation. Without an interlayer, the LSB produced stable cycling at a capacity of 600 mA h g⁻¹_sulfur at 0.05 C for 40 cycles. Using an unfunctionalised interlayer as a control gave a capacity of 1400 mA h g⁻¹_sulfur for the first cycle but rapidly decayed to the same 600 mA h g⁻¹_sulfur at the 40th cycle at 0.05 C, suggesting a high degree of polysulfide shuttling. Adding a lithium phenylsulfonated interlayer gave an initial capacity increase to 1100 mA h g⁻¹_sulfur that lowered to 800 mA h g⁻¹_sulfur at 0.05 C by the 40th cycle, showing an increase in charge storage (33%) relative to the other cells. This performance increase has been attributed to lessened polysulfide shuttling due to repulsion by the phenylsulfonate groups, increased conductivity at the separator-cathode interface and an increase in surface area. Full article

(This article belongs to the Special Issue Advanced Carbon-Based Materials for Next-Generation Batteries and Supercapacitors)

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16 pages, 2528 KiB

Open AccessArticle

An Adaptable Capacity Estimation Method for Lithium-Ion Batteries Based on a Constructed Open Circuit Voltage Curve

by Linjing Zhang, Xiaoqian Su, Caiping Zhang, Yubin Wang, Yao Wang, Tao Zhu and Xinyuan Fan

Batteries 2025, 11(7), 265; https://doi.org/10.3390/batteries11070265 - 14 Jul 2025

The inevitable decline in battery performance presents a major barrier to its widespread industrial application. Adaptive and accurate estimation of battery capacity is paramount for battery operation, maintenance, and residual value evaluation. In this paper, we propose a novel battery capacity estimation method [...] Read more.

The inevitable decline in battery performance presents a major barrier to its widespread industrial application. Adaptive and accurate estimation of battery capacity is paramount for battery operation, maintenance, and residual value evaluation. In this paper, we propose a novel battery capacity estimation method based on an approximate open circuit voltage curve. The proposed method is rigorously tested using both lithium–iron–phosphate (LFP) and nickel–cobalt–manganese (NCM) battery packs at multiple charging rates under varied aging conditions. To further enhance capacity estimation accuracy, a voltage correction strategy is implemented utilizing the incremental capacity (IC) curve. This strategy also verifies the potential benefits of increasing the charging rate to shorten the overall test duration. Eventually, the capacity estimation error is consistently controlled within 2%. With optimal state of charge (SOC) interval selection, the estimation error can be further reduced to 1%. Clearly, our proposed method exhibits excellent compatibility across diverse battery materials and degradation states. This adaptability holds substantial scientific value and practical importance. It contributes to the safe and economic utilization of Li-ion batteries throughout their entire lifespan. Full article

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22 pages, 3348 KiB

Open AccessArticle

Integrated Machine Learning Framework Combining Electrical Cycling and Material Features for Supercapacitor Health Forecasting

by Mojtaba Khakpour Komarsofla, Kavian Khosravinia and Amirkianoosh Kiani

Batteries 2025, 11(7), 264; https://doi.org/10.3390/batteries11070264 - 14 Jul 2025

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the [...] Read more.

The ability to predict capacity retention is critical for ensuring the long-term reliability of supercapacitors in energy storage systems. This study presents a comprehensive machine learning framework that integrates both electrical cycling data and experimentally derived material and structural features to forecast the degradation behavior of commercial supercapacitors. A total of seven supercapacitor samples were tested under various current and voltage conditions, resulting in over 70,000 charge–discharge cycles across three case studies. In addition to electrical measurements, detailed physical and material characterizations were performed, including electrode dimension analysis, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), and Thermogravimetric Analysis (TGA). Three machine learning models, Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP), were trained using both cycler-only and combined cycler + material features. Results show that incorporating material features consistently improved prediction accuracy across all models. The MLP model exhibited the highest performance, achieving an R² of 0.976 on the training set and 0.941 on unseen data. Feature importance analysis confirmed that material descriptors such as porosity, thermal stability, and electrode thickness significantly contributed to model performance. This study demonstrates that combining electrical and material data offers a more holistic and physically informed approach to supercapacitor health prediction. The framework developed here provides a practical foundation for accurate and robust lifetime forecasting of commercial energy storage devices, highlighting the critical role of material-level insights in enhancing model generalization and reliability. Full article

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30 pages, 4926 KiB

Open AccessArticle

Impact Testing of Aging Li-Ion Batteries from Light Electric Vehicles (LEVs)

by Miguel Antonio Cardoso-Palomares, Juan Carlos Paredes-Rojas, Juan Alejandro Flores-Campos, Armando Oropeza-Osornio and Christopher René Torres-SanMiguel

Batteries 2025, 11(7), 263; https://doi.org/10.3390/batteries11070263 - 13 Jul 2025

The increasing adoption of Light Electric Vehicles (LEVs) in urban areas, driven by the micromobility wave, raises significant safety concerns, particularly regarding battery fire incidents. This research investigates the electromechanical performance of aged 18650 lithium-ion batteries (LIBs) from LEVs under mechanical impact conditions. [...] Read more.

The increasing adoption of Light Electric Vehicles (LEVs) in urban areas, driven by the micromobility wave, raises significant safety concerns, particularly regarding battery fire incidents. This research investigates the electromechanical performance of aged 18650 lithium-ion batteries (LIBs) from LEVs under mechanical impact conditions. For this study, a battery module from a used e-scooter was disassembled, and its constituent cells were reconfigured into compact modules for testing. To characterize their initial condition, the cells underwent cycling tests to evaluate their state of health (SOH). Although a slight majority of the cells retained an SOH greater than 80%, a notable increase in their internal resistance (IR) was also observed, indicating degradation due to aging. The mechanical impact tests were conducted in adherence to the UL 2271:2018 standard, employing a semi-sinusoidal acceleration pulse. During these tests, linear kinematics were analyzed using videogrammetry, while key electrical and thermal parameters were monitored. Additionally, strain gauges were installed on the central cells to measure stress and deformation. The results from the mechanical shock tests revealed characteristic acceleration and velocity patterns. These findings clarify the electromechanical behavior of aged LIBs under impact, providing critical data to enhance the safety and reliability of these vehicles. Full article

(This article belongs to the Topic Batteries, Supercapacitors, Fuel Cells and Combined Energy/Power Supply Systems, 2nd Edition)

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23 pages, 2707 KiB

Open AccessArticle

Performance Analysis of Battery State Prediction Based on Improved Transformer and Time Delay Second Estimation Algorithm

by Bo Gao, Xiangjun Li, Fang Guo and Xiping Wang

Batteries 2025, 11(7), 262; https://doi.org/10.3390/batteries11070262 - 13 Jul 2025

As energy storage technology advances rapidly, the power industry demands accurate state estimation of lithium batteries in energy storage power stations. This study aimed to improve such estimations. An improved Transformer structure was employed to estimate the battery’s state of charge (SOC). The [...] Read more.

As energy storage technology advances rapidly, the power industry demands accurate state estimation of lithium batteries in energy storage power stations. This study aimed to improve such estimations. An improved Transformer structure was employed to estimate the battery’s state of charge (SOC). The Time Delay Second Estimation (TDSE) algorithm optimized the improved Transformer model to overcome traditional models’ limitations in extracting long-term dependency. Innovative particle filter algorithms were proposed to handle the nonlinearity, uncertainty, and dynamic changes in predicting remaining battery life. Results showed that for LiNiMnCoO₂ positive electrode datasets, the model’s max SOC estimation error was 2.68% at 10 °C and 2.15% at 30 °C. For LiFePO₄ positive electrode datasets, the max error was 2.79% at 10 °C (average 1.25%) and 2.35% at 30 °C (average 0.94%). In full lifecycle calculations, the particle filter algorithm predicted battery capacity with 98.34% accuracy and an RMSE of 0.82%. In conclusion, the improved Transformer and TDSE algorithm enable advanced battery state prediction, and the particle filter algorithm effectively predicts remaining battery life, enhancing the adaptability and robustness of lithium battery state analysis and offering technical support for energy storage station management. Full article

(This article belongs to the Topic Advanced Battery Thermal Management Solution for Electric Vehicles, 2nd Edition)

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