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Transfer Learning-Enhanced Safety Modeling for Lithium-Ion Batteries Under Mechanical Abuse
Condition-Dependent Rate Capability of Laser-Structured Hard Carbon Anodes in Sodium-Based Batteries

Journal Description

Batteries

Batteries is an international, peer-reviewed, open access journal on battery technology and materials published monthly online by MDPI. The International Society for Porous Media (InterPore) is affiliated with Batteries and its 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 19.2 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second 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 10 topical sections.
Journal Cluster of Energy and Fuels: Energies, Batteries, Hydrogen, Biomass, Electricity, Wind, Fuels, Gases, Solar, ESA, Bioresources and Bioproducts and Methane.

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

Imprint Information Journal Flyer Open Access ISSN: 2313-0105

Latest Articles

32 pages, 5560 KB

Open AccessArticle

MTEC-SOC: A Multi-Physics Aging-Aware Model for Smartphone Battery SOC Estimation Under Diverse User Behaviors

by Yuqi Zheng, Yao Li, Liang Song and Xiaomin Dai

Batteries 2026, 12(4), 130; https://doi.org/10.3390/batteries12040130 - 8 Apr 2026

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior [...] Read more.

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

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12 pages, 4382 KB

Open AccessArticle

Advanced Lithium-Ion Battery Enhanced by Silver-Cooperated LiFe_0.6Mn_0.4PO₄ Cathode

by Wenyu Liang, Wanwei Zhao, Guangyao Jin and Rui Xu

Batteries 2026, 12(4), 129; https://doi.org/10.3390/batteries12040129 - 8 Apr 2026

To address the inherent low voltage and poor energy density of LiFePO₄, LiFe_0.6Mn_0.4PO₄ (LFMP) has emerged as a promising cathode for next-generation lithium-ion batteries. However, its practical application is severely hindered by intrinsic limitations such as [...] Read more.

To address the inherent low voltage and poor energy density of LiFePO₄, LiFe_0.6Mn_0.4PO₄ (LFMP) has emerged as a promising cathode for next-generation lithium-ion batteries. However, its practical application is severely hindered by intrinsic limitations such as low electronic conductivity and sluggish Li⁺ diffusion. To address these challenges, this study investigates the effects of silver (Ag) doping on the structural and electrochemical performance of LFMP. Through a facile high-temperature solid-state approach, Ag⁺ ions are successfully incorporated into the LFMP matrix, and the resulting material (LFMP-Ag) is systematically characterized. The results reveal that partial Ag is doped into the LFMP lattice while an Ag-rich secondary phase within LFMP particles is detected, significantly enhancing the charge transfer kinetics. The Ag-doped LFMP cathodes exhibit superior discharge capacity of 142.1 mAh g⁻¹ at 0.1 C, enhanced rate capability, better cyclic stability (92.3% retention after 300 cycles) and enhanced thermal stability, surpassing the undoped LFMP counterparts. These findings demonstrate that Ag doping is an effective strategy for optimizing the electrochemical performance of LFMP cathodes, offering a viable pathway toward advanced battery technologies. Full article

(This article belongs to the Special Issue Surface Coating Technology for Electrode Materials)

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11 pages, 1771 KB

Open AccessArticle

Facile Synthesis of High Purity Li₂S by Titanothermic Reduction

by Xinyi Wang, Sha Li, Lingwen Zhang, Jun Li, Dan Guo, Qizhao Hu, Gang Tang and Hongxu Li

Batteries 2026, 12(4), 128; https://doi.org/10.3390/batteries12040128 - 7 Apr 2026

Lithium sulfide (Li₂S) is indispensable for lithium–sulfur batteries and sulfide solid-state batteries. However, its high preparation cost and strict process conditions represent core bottlenecks restricting large-scale commercial application. To address this issue, a novel process featuring a low-cost, high-safety, and controllable [...] Read more.

Lithium sulfide (Li₂S) is indispensable for lithium–sulfur batteries and sulfide solid-state batteries. However, its high preparation cost and strict process conditions represent core bottlenecks restricting large-scale commercial application. To address this issue, a novel process featuring a low-cost, high-safety, and controllable reaction is proposed in this work. Compared with the commercial H₂S-based route for Li₂S production, the developed process presents distinct advantages, including accessible raw materials, high safety, low overall cost, and low environmental load. Using Li₂SO₄·H₂O as the raw material and Ti as the reducing agent, high-purity T-Li₂S (>99.9%) is successfully synthesized via solid-state sintering and purification, yielding a higher purity level than that of commercial C-Li₂S (>99.7%). Furthermore, sulfide all-solid-state electrolytes T-Li_5.3PS_4.3ClBr_0.7 and C-Li_5.3PS_4.3ClBr_0.7 are prepared using the as-obtained T-Li₂S and commercial C-Li₂S as precursors, respectively. The room-temperature Li-ion conductivities are determined to be 14.5 mS/cm and 11.0 mS/cm, revealing faster ion migration and efficient ion transport in T-Li_5.3PS_4.3ClBr_0.7 without high-temperature assistance, which fully validates the feasibility of the proposed strategy. Overall, this work provides a new technical route for the preparation of high-purity Li₂S, showing promising application prospects. Full article

(This article belongs to the Special Issue Multiscale Co-Design of Electrode Architectures and Electrolytes)

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14 pages, 16245 KB

Open AccessArticle

Aging State Classification of Lithium-Ion Batteries in a Low-Dimensional Latent Space

by Limei Jin, Franz Philipp Bereck, Rüdiger-A. Eichel, Josef Granwehr and Christoph Scheurer

Batteries 2026, 12(4), 127; https://doi.org/10.3390/batteries12040127 - 7 Apr 2026

Battery datasets, whether gathered experimentally or through simulation, are typically high-dimensional and complex, which complicates the direct interpretation of degradation behavior or anomaly detection. To overcome these limitations, this study introduces a framework that compresses battery signals into a low-dimensional representation using an [...] Read more.

Battery datasets, whether gathered experimentally or through simulation, are typically high-dimensional and complex, which complicates the direct interpretation of degradation behavior or anomaly detection. To overcome these limitations, this study introduces a framework that compresses battery signals into a low-dimensional representation using an autoencoder, enabling the extraction of informative features for state analysis. A central component of this work is the systematic comparison of latent representations obtained from two fundamentally different data sources: frequency-domain impedance data and time-domain voltage-current data. The close agreement of aging trajectories in both representations suggests that information traditionally derived from impedance analysis can also be captured directly from raw time-series signals. To better approximate real operating conditions, synthetic datasets are augmented with stochastic perturbations. In this context, latent spaces learned from idealized periodic inputs are contrasted with those derived from permuted and noise-contaminated signals. The resulting low-dimensional features are subsequently evaluated through a support vector machine with both linear and nonlinear kernel functions, allowing the categorization of battery states into fresh, aged and damaged conditions. The results demonstrate that the progression of battery degradation is consistently reflected in the latent space, independent of the input domain or signal quality. This robustness indicates that the proposed approach can effectively capture essential aging characteristics even under non-ideal conditions. Consequently, this framework provides a basis for developing advanced diagnostic strategies, including the design of pseudo-random excitation profiles for improved battery state assessment and optimized operational control. Full article

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22 pages, 3764 KB

Open AccessArticle

Capacity Enhancement and Structural Study of Fluorine-Doped Co-Free Li- and Mn-Rich Li_1.2[Mn_0.5Ni_0.2Fe_0.1]O_2(1−x)F_2x Layered Oxide Cathodes

by Kamil Kucuk, Shankar Aryal, Maziar Ashuri, Mohammadreza Esmaeilirad, Alireza Kondori, Ning Su, Elena V. Timofeeva and Carlo U. Segre

Batteries 2026, 12(4), 126; https://doi.org/10.3390/batteries12040126 - 6 Apr 2026

Both Co-free and lithium- and manganese-rich layered oxide Li(Li_0.2Mn_xNi_yFe_z)O₂ (MNF) cathodes have recently attracted attention in lithium-ion battery (LIB) research due to their high capacities of over 250 mAhg⁻¹, as well as [...] Read more.

Both Co-free and lithium- and manganese-rich layered oxide Li(Li_0.2Mn_xNi_yFe_z)O₂ (MNF) cathodes have recently attracted attention in lithium-ion battery (LIB) research due to their high capacities of over 250 mAhg⁻¹, as well as being more eco-friendly and inexpensive than commercial NMC and LiCoO₂. However, they still suffer from lower experimental capacity as well as capacity decay, voltage fade, poor rate capability, and thermal instability. In this paper, fluorine (F)-doped Li_1.2(Mn_0.5Ni_0.2Fe_0.1)O_2(1−x)F_2x (MNF502010, x = 0, 0.025, 0.05, 0.075, 0.1) cathode materials have been synthesized in the nanoscale via sol–gel and subsequent solid-phase calcination to address some of these problems. The resulting 5% F-doped MNF502010 cathode demonstrates the advantage of fluorine doping, which makes a significant contribution to the formation of a well-ordered layer structure with a minimal LiM₂O₄ spinel phase as an impurity. This composition achieves an initial discharge capacity of 252 mAhg⁻¹ (1C = 250 mAhg⁻¹) and a 156 mAhg⁻¹ discharge capacity at 0.3 C on the 100th discharge, with an average voltage fade of 0.24 V. The optimization of fluorine composition results in an enhancement in the activation of the Li₂MnO₃-type monoclinic phase, as well as an increase in the electronic conductivity compared to the fluorine-free cathode. To understand the structural origin of this improved performance, X-ray absorption spectroscopy (XAS) measurements were carried out on pristine and cycled MNF electrodes. Full article

(This article belongs to the Special Issue Development, Application, and Characterization of New Electrode Materials for Advanced Batteries)

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21 pages, 2891 KB

Open AccessArticle

Energy Emissions and Cost Impacts of Autonomous Battery Electric Vehicles in Riyadh

by Ali Louati, Hassen Louati and Elham Kariri

Batteries 2026, 12(4), 125; https://doi.org/10.3390/batteries12040125 - 1 Apr 2026

Autonomous battery electric vehicles (BEVs) have the potential to reshape urban mobility systems, yet their sustainability impacts remain underexplored in Gulf-region cities where traffic dynamics, land-use structures, and environmental conditions differ substantially from Western contexts. This study introduces a Saudi-specific assessment framework that [...] Read more.

Autonomous battery electric vehicles (BEVs) have the potential to reshape urban mobility systems, yet their sustainability impacts remain underexplored in Gulf-region cities where traffic dynamics, land-use structures, and environmental conditions differ substantially from Western contexts. This study introduces a Saudi-specific assessment framework that integrates monetised externalities with empirically calibrated traffic dynamics to evaluate how automation influences safety, congestion, land use, emissions, and noise. To the best of our knowledge, this is the first Riyadh-calibrated monetised external-cost evaluation of autonomous BEVs that couples externality valuation with simulation-validated time-varying traffic dynamics (SAR per vkm and SAR per pkm), enabling realistic peak-period sustainability assessment. The framework’s key contribution is linking external-cost modelling with spatiotemporal traffic behaviour derived from Riyadh’s 2023 mobility patterns, providing a more realistic basis for sustainability evaluation. Using national datasets from transport, energy, and statistical authorities, the model estimates substantial reductions in external costs when transitioning from human-driven to autonomous BEVs, driven primarily by lower crash exposure and smoother traffic flow. To validate these findings under real operating conditions, a dynamic analysis incorporating hourly and seasonal traffic variability was developed, revealing that automation delivers its strongest improvements during peak-demand periods where congestion externalities are highest. The integrated results demonstrate the relevance of autonomous BEVs for dense rapidly growing Saudi cities and provide actionable insights for future mobility planning. The study highlights the policy importance of coordinated transport, land-use, and energy strategies to ensure that automation contributes meaningfully to national sustainability goals under Vision 2030. Full article

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

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31 pages, 4715 KB

Open AccessArticle

PIDNN: A Hybrid Intelligent Prediction Model for UAV Battery Degradation

by Mengmeng Duan, Mingyu Lu and Huiqing Jin

Batteries 2026, 12(4), 124; https://doi.org/10.3390/batteries12040124 - 1 Apr 2026

The operational safety and endurance of unmanned aerial vehicles (UAVs) are strongly affected by lithium-ion battery degradation under extreme thermal environments. However, conventional physics-based models often rely on simplified assumptions, whereas purely data-driven methods usually lack physical interpretability and robust generalization. To address [...] Read more.

The operational safety and endurance of unmanned aerial vehicles (UAVs) are strongly affected by lithium-ion battery degradation under extreme thermal environments. However, conventional physics-based models often rely on simplified assumptions, whereas purely data-driven methods usually lack physical interpretability and robust generalization. To address these limitations, this study proposes a Physics-Informed Deep Neural Network (PIDNN) for predicting UAV battery degradation under complex environmental conditions. The proposed framework integrates thermodynamic and fluid dynamic principles with deep neural networks by incorporating physical constraints derived from heat generation, heat conduction, and convective heat transfer into the loss function. This design enables the model to capture nonlinear degradation patterns while maintaining consistency with fundamental physical laws. Comprehensive simulation-based experiments were conducted under high-temperature (45 °C), low-temperature (−20 °C), and room-temperature (25 °C) conditions, together with varying discharge rates, humidity levels, wind speeds, and multi-factor coupled scenarios. The results show that the proposed PIDNN consistently outperforms conventional physics-based models and several representative data-driven methods, including SVM, LSTM, and GAN-based approaches. It achieves lower prediction errors across all evaluated conditions, as reflected by reduced mean absolute error and root mean square error. By providing physically consistent predictions of capacity fade, internal resistance growth, and remaining useful life, the proposed framework supports degradation-aware monitoring and early warning for intelligent battery management systems. These findings provide a robust methodological basis for improving the reliability, safety, and service life of UAV power systems operating in complex climatic environments. Full article

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23 pages, 3020 KB

Open AccessArticle

A State of Health Estimation Method for Lithium-Ion Battery Packs Using Two-Level Hierarchical Features and TCN–Transformer–SE

by Chaolong Zhang, Panfen Yin, Kaixin Cheng, Yupeng Wu, Min Xie, Guoqing Hua, Anxiang Wang and Kui Shao

Batteries 2026, 12(4), 123; https://doi.org/10.3390/batteries12040123 - 1 Apr 2026

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle [...] Read more.

This study proposes a novel state of health (SOH) estimation method by extracting two-level hierarchical features linked to fundamental degradation mechanisms. At the module level, the length of the incremental power curve during constant current charging is extracted, capturing cumulative effects of subtle changes. At the cell level, a combined temperature-weighted voltage inconsistency curve is constructed. The state of charge (SOC) at its distinct knee point within the high-SOC range is a key indicator, signifying the accelerated failure stage where polarization and thermoelectric feedback intensify. This knee-point SOC quantitatively reflects the degree of SOH degradation, making it a valid feature for accurate SOH estimation. The proposed Temporal Convolutional Network–Transformer–Squeeze-and-Excitation (TCN–Transformer–SE) model assigns weights to these features via Squeeze-and-Excitation (SE) and uses Temporal Convolutional Network (TCN) and Transformer branches for parallel local and global temporal decisions. Aging experiments demonstrate the method’s superiority through multi-feature comparison, ablation studies, and benchmark evaluation, achieving a maximum mean absolute error (MAE) of 0.0031, a root mean square error (RMSE) of 0.0038, a coefficient of determination (R²) of 0.9937 and a mean absolute percentage error (MAPE) of 0.3820. The work provides a fusion estimation framework with enhanced interpretability grounded in electrochemical analysis. Full article

(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)

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14 pages, 3337 KB

Open AccessArticle

Investigation of Laser Intensity Profiles in the Laser Drying of Anodes for Lithium-Ion Battery Production

by Benedict Ingendoh, Vincent Gabor, Thomas Hanf, Sebastian Wolf, Henrik Born, Heiner Heimes and Achim Kampker

Batteries 2026, 12(4), 122; https://doi.org/10.3390/batteries12040122 - 1 Apr 2026

The growing demand for lithium-ion batteries, along with the imperative for sustainable and cost-efficient production, necessitates the exploration of innovative technological approaches. Among the most energy-intensive steps in battery manufacturing is the electrode drying process. This study examined the impact of rapid laser-based [...] Read more.

The growing demand for lithium-ion batteries, along with the imperative for sustainable and cost-efficient production, necessitates the exploration of innovative technological approaches. Among the most energy-intensive steps in battery manufacturing is the electrode drying process. This study examined the impact of rapid laser-based drying on critical quality parameters of anode electrodes. A vertical-cavity surface-emitting laser (VCSEL) was employed, enabling precise and independent control of the power distribution. By applying various intensity profiles, the influence of laser power modulation on electrode drying behaviour and resulting quality was systematically investigated. The outcomes were compared to both conventional convection drying and laser drying at constant power. The objective was to assess the viability of profile-controlled laser drying as a stand-alone alternative and to identify its benefits and limitations with regard to electrode quality and process efficiency. Full article

(This article belongs to the Section Battery Processing, Manufacturing and Recycling)

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28 pages, 5655 KB

Open AccessArticle

Degradation of a Lithium-Ion Battery Cell for Enhanced First and Second Life: Effects of Temperature, Orientation, C-Rate and State of Charge

by Ejikeme Raphael Ezeigwe, Sivert A. Woll, Lene T. B. Erichsen, Simon B. B. Solberg, Gareth M. Hughes, Wenjia Du, Jacob J. Lamb, Julia Wind, Torleif Lian, Paul R. Shearing, Odne Stokke Burheim and Preben J. S. Vie

Batteries 2026, 12(4), 121; https://doi.org/10.3390/batteries12040121 - 30 Mar 2026

Lithium-ion batteries (LIBs) can considerably improve their lifespan by optimising operating conditions. This may entail ensuring optimal operating temperature, limiting the state-of-charge (SoC) window, reducing cycling current, and changing the physical orientation of the uncompressed LIB cell. In this study, we examine how [...] Read more.

Lithium-ion batteries (LIBs) can considerably improve their lifespan by optimising operating conditions. This may entail ensuring optimal operating temperature, limiting the state-of-charge (SoC) window, reducing cycling current, and changing the physical orientation of the uncompressed LIB cell. In this study, we examine how these four conditions and some of their combinations impact degradation in both 1st life as well as in second life. The cell analysed in this investigation was the Xalt 31 HE cell, an energy-optimised Li-ion pouch cell with a capacity of 31 Ah and an NMC433-graphite chemistry. As a follow-up study of previously reported results, a total of 18 cells were investigated. We report results focusing on improving cycle life and ensuring safety before second life. The optimal conditions for first-life cycling in the full SoC window were found at room temperature, when cycled with a lower current and the cells oriented horizontally. We observed that under the same cycling conditions, a vertical alignment of cells resulted in an increased degradation rate compared to horizontal alignment. The best second-life capacity retention was found for cells initially cycled at room temperature, then later cycled with a reduced SoC window, at a lower current and in a horizontal orientation. If the cells were cycled at an elevated temperature in first life, the second-life compatibility was reduced considerably. An incremental capacity analysis (ICA) of the first-life ageing data revealed a possible indicator for ensuring safety and cycleability into second-life use. Full article

(This article belongs to the Special Issue Thermal Management System for Lithium-Ion Batteries: 2nd Edition)

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48 pages, 12876 KB

Open AccessReview

Comparative Study of Titanium Oxide Materials for Ultrafast Charging in Lithium-Ion Batteries

by Abderrahim Laggoune, Anil Kumar Madikere Raghunatha Reddy, Jeremy I. G. Dawkins, Thiago M. G. Selva, Jitendrasingh Rajpurohit and Karim Zaghib

Batteries 2026, 12(4), 120; https://doi.org/10.3390/batteries12040120 - 29 Mar 2026

The development of lithium-ion batteries (LIBs) capable of extreme fast charging (XFC) while preserving safety, durability, and practical energy density remains a central challenge for next-generation electric transportation and grid-scale storage. Conventional graphite anodes are fundamentally limited at high current densities by sluggish [...] Read more.

The development of lithium-ion batteries (LIBs) capable of extreme fast charging (XFC) while preserving safety, durability, and practical energy density remains a central challenge for next-generation electric transportation and grid-scale storage. Conventional graphite anodes are fundamentally limited at high current densities by sluggish intercalation kinetics, which cause lithium plating, motivating the exploration of alternative insertion materials. This review provides a comprehensive and internally consistent assessment of titanium-based oxide anodes, encompassing TiO₂ polymorphs, lithium titanate (Li₄Ti₅O₁₂), and Wadsley–Roth titanium niobium oxides, through the combined lenses of crystal topology, diffusion pathways, redox chemistry, interfacial behavior, and resource scalability. By systematically comparing structural frameworks and electrochemical mechanisms across these material classes, we demonstrate that fast-charging performance is governed not by nano-structuring alone, but by the intrinsic coupling between operating potential, framework rigidity, and multi-electron redox activity. While Li₄Ti₅O₁₂ establishes the benchmark for safety and cyclability, and TiO₂ polymorphs provide structural versatility, titanium niobium oxides uniquely reconcile high theoretical capacity with minimal lithiation strain and open diffusion channels, positioning them as highly promising candidates for sub-10 min charging without catastrophic degradation. This review highlights the persistent obstacles these materials suffer, such as limited round-trip energy efficiency (RTE), interfacial gas evolution, poor dopant stability, and unsustainable extraction, while simultaneously exploring targeted design strategies to overcome them. Finally, this review provides a materials design and comparison framework for the development of safe, high-power, and commercially viable ultrafast-charging LIBs. Full article

(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)

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17 pages, 5165 KB

Open AccessArticle

Assessing Solid Products in Nonaqueous Lithium-Oxygen Batteries Using Advanced Neutron Tomography and Titration Techniques

by Helen Ma, Amirhossein Sarabandi, Yousof Nayfeh, Yuxuan Zhang and Xianglin Li

Batteries 2026, 12(4), 119; https://doi.org/10.3390/batteries12040119 - 29 Mar 2026

This project investigates how the orientation of the carbon cathode with a single-sided microporous layer (MPL) affects battery performance through electrochemical tests, neutron tomography, and titration experiments. The titration experiment quantitatively assesses the amount of solid product (Li₂O₂) deposited [...] Read more.

This project investigates how the orientation of the carbon cathode with a single-sided microporous layer (MPL) affects battery performance through electrochemical tests, neutron tomography, and titration experiments. The titration experiment quantitatively assesses the amount of solid product (Li₂O₂) deposited on the electrode surface. In addition, neutron imaging with a 16 µm voxel resolution provides details on the spatial distribution of the solid product within the porous electrodes. Additionally, the performance impact of two electrolyte solvents, tetra ethylene glycol dimethyl ether (TEGDME) and dimethyl sulfoxide (DMSO), is evaluated when used to soak the carbon cathode. The cathode orientation where the MPL faces toward the electrolyte and separator reaches higher discharge and charge capacities and greater average discharge voltages compared to when the MPL faces away from the separator. Batteries discharged with DMSO as the solvent have a 64.86% decrease on average in discharge capacity compared to batteries using TEGDME as the solvent. Both the titration experiments and neutron imaging confirmed that the amount of solid products exhibits a linear correlation with the discharged capacity. Additionally, electrolytes with a high donor number, such as DMSO, were found to result in a smaller amount of Li₂O₂ deposited on the electrode surface. Full article

(This article belongs to the Collection Feature Papers in Batteries)

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17 pages, 3090 KB

Open AccessArticle

Recovery of Separator from Battery Waste by Supercritical Carbon Dioxide Extraction: Removal of Electrolyte and Electrode Contaminants

by Martin Östergren, Philipp Mikšovsky and Burçak Ebin

Batteries 2026, 12(4), 118; https://doi.org/10.3390/batteries12040118 - 28 Mar 2026

Hazardous compounds from used batteries pose a great threat to the environment. To prevent pollution and to recover critical materials from battery waste, efficient recycling is required. Until now, battery recycling has focused on the recovery of valuable metals from cathode materials, while [...] Read more.

Hazardous compounds from used batteries pose a great threat to the environment. To prevent pollution and to recover critical materials from battery waste, efficient recycling is required. Until now, battery recycling has focused on the recovery of valuable metals from cathode materials, while organic fractions have often been neglected due to their low material value. New approaches to battery recycling are therefore necessary, where recycling methods based on supercritical carbon dioxide (SC-CO₂) extraction show great potential. In this work, a SC-CO₂ method was implemented to extract electrolyte solvents for the purification and recovery of a separator waste material (SWM) sorted out from lithium-ion battery (LIB)-based black mass. In addition, two other separation routes (ultrasonic washing and thermal treatment) were used for comparison. Based on the results from the three routes, mass balances revealed the gravimetric composition of the SWM, which includes separator, electrolyte, and electrode powder. The composition of electrolyte solvents was determined via Gas Chromatography-Mass Spectroscopy analysis. Furthermore, the polymeric separator was analyzed using Fourier Transform Infrared Spectroscopy, Thermogravimetric Analysis, and Differential Scanning Calorimetry analysis to evaluate the effects of SC-CO₂ extraction on the physicochemical properties. The recovery of electrolyte by the SC-CO₂ route is more efficient than the others, with extraction yields of 162 mg of electrolyte per gram of SWM. Moreover, no changes are observed in the analyzed properties of the polymeric separator material due to the SC-CO₂ extraction. Thus, the SC-CO₂ process proves to be a promising method for an efficient and sustainable recycling of electrolyte solvent and purifying of separator material from LIB waste. Full article

(This article belongs to the Special Issue Recycling and Reuse of End-of-Life Lithium-Ion Batteries: Challenges and Strategies—2nd Edition)

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13 pages, 10857 KB

Open AccessArticle

Interfacial Engineering of Fe₂VO₄ Nanoparticles on MXene Nanosheets for Ultra-Stable and Efficient Sodium Storage

by Yanteng Duan, Shaonan Qiu, Leichao Meng, Shuzhen Cui, Qianghong Wu, Yongfu Cui, Yali Wang, Li Zhao and Yingjie Zhao

Batteries 2026, 12(4), 117; https://doi.org/10.3390/batteries12040117 - 27 Mar 2026

Owing to its high theoretical sodium-storage capacity of approximately 1000 mAh g⁻¹ and cost-efficient characteristics, Fe₂VO₄ has emerged as a highly attractive anode material for sodium-ion batteries (SIBs). In this work, MXene-incorporated Fe₂VO₄ composites were successfully [...] Read more.

Owing to its high theoretical sodium-storage capacity of approximately 1000 mAh g⁻¹ and cost-efficient characteristics, Fe₂VO₄ has emerged as a highly attractive anode material for sodium-ion batteries (SIBs). In this work, MXene-incorporated Fe₂VO₄ composites were successfully synthesized. Comprehensive electrochemical characterization demonstrates that MXene incorporation significantly enhances the electronic conductivity and sodium-ion diffusion kinetics of Fe₂VO₄, while effectively mitigating volume expansion during cycling. The synthetic substantially improves its cycling stability and rate capability. When the MXene loading ratio is optimized at 5 wt%, the composite exhibits outstanding cyclic durability, with a remarkable reversible specific capacity of 323.3 mAh g⁻¹ maintained after 200 cycles at a current density of 0.1 A g⁻¹. Furthermore, the composite demonstrates outstanding rate performance, with a specific capacity of 164.5 mAh g⁻¹ achieved at a current density of 2 A g⁻¹. The synergistic integration of Fe₂VO₄ and MXene not only constructs a three-dimensional electrically conductive framework for efficient charge transport but also reinforces strong structural stability against cycling-induced degradation. This work proposes a versatile engineering strategy that can be adapted for other conversion-type electrode materials in the context of advanced energy storage technologies. Full article

(This article belongs to the Special Issue Multiscale Co-Design of Electrode Architectures and Electrolytes)

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36 pages, 3551 KB

Open AccessArticle

Early Detection of Short-Term Performance Degradation in Electric Vehicle Lithium-Ion Batteries via Physics-Guided Multi-Sensor Fusion and Deep Learning

by David Chunhu Li

Batteries 2026, 12(4), 116; https://doi.org/10.3390/batteries12040116 - 27 Mar 2026

Early detection of battery degradation is essential for ensuring the safety and reliability of electric vehicle (EV) systems under real-world operating variability. This paper proposes a physics-guided multi-sensor learning framework, termed SensorFusion-Former (SFF), for early warning of short-term EV battery performance degradation. The [...] Read more.

Early detection of battery degradation is essential for ensuring the safety and reliability of electric vehicle (EV) systems under real-world operating variability. This paper proposes a physics-guided multi-sensor learning framework, termed SensorFusion-Former (SFF), for early warning of short-term EV battery performance degradation. The proposed approach integrates a physics-based baseline model for operational normalization, a multi-sensor fusion attention mechanism to model cross-modality interactions, and a lightweight transformer architecture for efficient temporal representation learning. Weak supervision is derived from physics-consistent residual analysis with temporal smoothing, enabling scalable training without dense manual annotations. To support reliable deployment, evidential uncertainty modeling and conformal calibration are incorporated to obtain statistically controlled decision thresholds. Experiments conducted on a real driving cycle dataset from IEEE DataPort demonstrate that SFF consistently outperforms classical machine learning methods, deep neural networks, and standard transformer models in terms of early-warning lead time, false alarm rate, and inference efficiency while maintaining competitive discriminative performance. Cross-scenario evaluations under diverse thermal conditions further confirm the robustness and generalization capability of the proposed framework. Full article

(This article belongs to the Section Energy Storage System Aging, Diagnosis and Safety)

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21 pages, 19856 KB

Open AccessArticle

An Adaptive-Weight Physics-Informed Neural Network Optimized by Grey Wolf Optimizer for Lithium-Ion Battery State of Health Estimation

by Runtong Wang, Jiakang Shen, Shupeng Liu and Hailin Rong

Batteries 2026, 12(4), 115; https://doi.org/10.3390/batteries12040115 - 26 Mar 2026

Reliable estimation of the State of Health (SOH) in lithium-ion batteries is critical to battery system security and dependability. However, existing Physics-Informed Neural Networks (PINNs) have drawbacks like single-feature physical constraints, rigid fixed-weight fusion of multi-feature constraints and insufficient time-series degradation modeling. To [...] Read more.

Reliable estimation of the State of Health (SOH) in lithium-ion batteries is critical to battery system security and dependability. However, existing Physics-Informed Neural Networks (PINNs) have drawbacks like single-feature physical constraints, rigid fixed-weight fusion of multi-feature constraints and insufficient time-series degradation modeling. To solve these problems, this study proposes an Adaptive-Weight PINN (AW-PINN) optimized by the Grey Wolf Optimizer (GWO) algorithm, which features a dual-LSTM parallel structure and takes incremental capacity peaks and charged capacity as dual physical constraints. A weight generator LSTM adaptively learns weights for monotonicity losses without manual intervention, and GWO globally optimizes physical loss weights to balance data fitting accuracy and prediction physical consistency. Validated on

{LiCoO}_{2}

, NCA, and NCM batteries from CALCE and Tongji University datasets via comparative, ablation, and small-sample experiments, AW-PINN shows superior predictive performance (average RMSE = 0.0076; MAE = 0.0065; MAPE = 0.0072), robustness, and generalization. It integrates battery degradation physics with deep learning, retaining strong fitting capability while enabling physical interpretability. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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23 pages, 3431 KB

Open AccessArticle

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on Health Features Extraction and Improved Stochastic Configuration Networks

by Xinlu Wang, Zhijun Gao, Xifeng Guo, Yi Ning and Yiyang Liu

Batteries 2026, 12(4), 114; https://doi.org/10.3390/batteries12040114 - 26 Mar 2026

The remaining useful life (RUL) of lithium-ion batteries plays a crucial role in fault prognosis and health management. Therefore, accurate RUL prediction can effectively improve equipment safety and mitigate operational risks. However, existing RUL prediction methods often exhibit limited accuracy caused by capacity [...] Read more.

The remaining useful life (RUL) of lithium-ion batteries plays a crucial role in fault prognosis and health management. Therefore, accurate RUL prediction can effectively improve equipment safety and mitigate operational risks. However, existing RUL prediction methods often exhibit limited accuracy caused by capacity regeneration and excessively long training times due to model complexity. In this study, Pearson and Spearman correlation analyses are employed to effectively extract health features that are highly correlated with battery capacity to characterize capacity degradation, and a lithium-ion battery RUL prediction model based on stochastic configuration networks (SCNs) optimized by sparrow search algorithm (SSA) is proposed. The battery datasets from NASA and CALCE are used for validation and testing. Experimental results demonstrate that the proposed SSA-SCN achieves a root mean squared error of 0.0036 and a mean absolute error of 0.0030, while exhibiting faster training time compared with other hybrid methods. The results verify that the proposed method can provide more accurate battery RUL predictions and effectively improve the accuracy and reliability of lithium-ion battery RUL estimation. Full article

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17 pages, 4205 KB

Open AccessArticle

Enhancing High-Temperature Cycling Stability and Rate Capability of LiNi_0.71Co_0.09Mn_0.2O₂ Cathodes via Al₂O₃/LiBO₂ Double Coatings

by Guozhen Wei, Suheng Wang, Yaobin Ye and Nengjian Xie

Batteries 2026, 12(4), 113; https://doi.org/10.3390/batteries12040113 - 26 Mar 2026

Layered nickel-rich cathodes are regarded as promising cathode materials for lithium-ion batteries (LIBs) due to their higher electrochemical capacities and lower cost. However, the development and commercial application of nickel-rich cathodes are severely hindered by significant capacity fading under a high charge cut-off [...] Read more.

Layered nickel-rich cathodes are regarded as promising cathode materials for lithium-ion batteries (LIBs) due to their higher electrochemical capacities and lower cost. However, the development and commercial application of nickel-rich cathodes are severely hindered by significant capacity fading under a high charge cut-off voltage (4.5 V), which arises from interfacial instability and bulk structural degradation during charge–discharge processes. In this study, a two-step double-coating strategy was innovatively adopted to successfully synthesize Al₂O₃/LiBO₂ co-coated LiNi_0.71Co_0.09Mn_0.2O₂ cathode material (denoted as NCM-Al/B). X-ray photoelectron spectroscopy (XPS) verified that Al existed stably in the form of Al³⁺, and B formed B-O-M covalent bonds with transition metals (Ni/Co/Mn), constructing a dual-element synergistic interface. This interface significantly reduced the surface Ni³⁺ content and enhanced the structural stability by suppressing the H₂→H₃ phase transition. The NCM-Al/B material exhibits excellent electrochemical performance: it maintains a remarkable cycling stability with a capacity retention of 91.6% after 100 cycles at 1 C and 25 °C and delivers a discharge capacity of 156.6 mAh·g⁻¹ with a capacity retention of 75.4% after 100 cycles at a high rate of 1 C. This work establishes a chemically driven double-coating strategy and provides a new paradigm for optimizing the performance of high-nickel cathode materials. Full article

(This article belongs to the Special Issue Advanced Electrode Materials and Electrolytes for Next-Generation Rechargeable Batteries)

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19 pages, 1849 KB

Open AccessArticle

Stochastic Robust Trading Strategy for Multiple Virtual Power Plants Led by a Public Energy Storage Station

by Yanjun Dong, Tuo Li, Juan Su, Bo Zhao and Songhuai Du

Batteries 2026, 12(4), 112; https://doi.org/10.3390/batteries12040112 - 25 Mar 2026

With the rapid development of smart cities, coordinating diverse distributed energy resources through storage-centric shared management has become a critical challenge. This paper proposes a bi-level energy management framework to support peer-to-peer energy trading among multiple virtual power plants (VPPs) under multidimensional uncertainties. [...] Read more.

With the rapid development of smart cities, coordinating diverse distributed energy resources through storage-centric shared management has become a critical challenge. This paper proposes a bi-level energy management framework to support peer-to-peer energy trading among multiple virtual power plants (VPPs) under multidimensional uncertainties. The interaction is modeled as a Stackelberg–Nash equilibrium framework, in which OK, we will make the necessary revisions as per the requirements.a public energy storage operator and a natural gas company act as leaders to maximize social welfare and design differentiated trading strategies for VPPs. The VPPs act as followers and participate in cooperative energy trading based on a generalized Nash equilibrium scheme, sharing surplus energy and allocating cooperative benefits according to their contributions. To address uncertainty, Conditional Value at Risk (CVaR) is adopted to quantify the expected loss of the upper-level decision makers. The lower-level VPP problem is formulated as a three-stage stochastic robust optimization model considering renewable generation uncertainty. To solve the resulting nonlinear bi-level problem, a two-stage solution approach combining particle swarm optimization and KKT-based reformulation is developed to transform it into a tractable mixed-integer linear programming model. Numerical case studies verify the effectiveness of the proposed framework. Full article

(This article belongs to the Topic Smart Energy Systems, 2nd Edition)

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15 pages, 2869 KB

Open AccessArticle

Analysis of the Failure Modes, Mechanisms, and Effects of Potassium Acetate Water-in-Salt Electrolyte-Based Supercapacitor

by Jose Miguel Delgado, Joan Ramon Morante and Jordi Jacas Biendicho

Batteries 2026, 12(4), 111; https://doi.org/10.3390/batteries12040111 - 24 Mar 2026

Water-In-Salt (WIS) electrolytes are expected to replace expensive, environmentally harmful organic electrolytes while delivering high voltages and improving system safety. In this study, analysis of the failure modes, mechanisms, and effects of a highly concentrated potassium acetate (KAc) electrolyte was conducted through electrolyte [...] Read more.

Water-In-Salt (WIS) electrolytes are expected to replace expensive, environmentally harmful organic electrolytes while delivering high voltages and improving system safety. In this study, analysis of the failure modes, mechanisms, and effects of a highly concentrated potassium acetate (KAc) electrolyte was conducted through electrolyte degradation at 2 V in a conventional EDLC carbon-based symmetric configuration. The adopted method provides a simplified yet effective approach for assessing the complexity and interconnectivity of degradation mechanisms in a WIS supercapacitor. The effects analysis included electrochemical stability studies, post-mortem characterizations (SEM-EDS and XPS), low-frequency impedance fitting, and cell reassembly using end-of-life electrodes. Among the failure modes analyzed, electrolyte decomposition and pore blocking exhibit strong physicochemical correlations and high failure rates. Therefore, they should be prioritized in the design of new WIS electrolyte compositions for next-generation energy storage systems. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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