Batteries

17 pages, 2827 KB

Open AccessArticle

Ionowaxes on Porous Polymer Supports Form Cheap, Robust and Exquisitely Selective Proton-Conducting Membranes

by Ro L. Dunlop, Thomas J. Grummitt, Joel C. Schuurman and Deborah L. Crittenden

Batteries 2026, 12(4), 148; https://doi.org/10.3390/batteries12040148 - 21 Apr 2026

Viewed by 414

Redox-flow batteries are a promising emerging technology for large-scale storage of renewable energy. However, existing ion-exchange membranes used for separating electrolytes are expensive and often ineffective at preventing crossover of redox-active species, leading to a decrease in battery capacity over time. Herein, we [...] Read more.

Redox-flow batteries are a promising emerging technology for large-scale storage of renewable energy. However, existing ion-exchange membranes used for separating electrolytes are expensive and often ineffective at preventing crossover of redox-active species, leading to a decrease in battery capacity over time. Herein, we introduce a new class of proton-conducting membranes formed by depositing highly alkylated waxy hydrophobic salts on porous polypropylene supports and demonstrate that they form self-assembled nanostructures which exclusively conduct protons via a unique mechanism of action. These new “ionowax” membranes display comparable proton conductivities to existing commercially available functionalized porous polymer membranes but are cheaper and easier to fabricate. We anticipate that these new membranes will facilitate future development of cheaper and/or longer-lasting aqueous redox-flow batteries. Full article

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

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28 pages, 16569 KB

Open AccessArticle

Performance Comparison of Intelligent Energy Management Strategies for Hybrid Electric Vehicles with Photovoltaic Fuel Cell and Battery Integration

by Mohammed A. Albadrani, Ragab A. Sayed, Sabry Allam, Hossam Youssef Hegazy, Md. Morsalin, Mohamed H. Abdelati and Samia Abdel Fattah

Batteries 2026, 12(4), 147; https://doi.org/10.3390/batteries12040147 - 21 Apr 2026

Viewed by 635

Abstract

This study presents an optimized and comparative investigation of four intelligent energy management strategies—Proportional–Integral–Derivative (PID), Fuzzy Logic Control (FLC), Equivalent Consumption Minimization Strategy (ECMS), and Artificial Neural Network (ANN)—applied to a photovoltaic–fuel cell–battery hybrid electric vehicle ( [...] Read more.

This study presents an optimized and comparative investigation of four intelligent energy management strategies—Proportional–Integral–Derivative (PID), Fuzzy Logic Control (FLC), Equivalent Consumption Minimization Strategy (ECMS), and Artificial Neural Network (ANN)—applied to a photovoltaic–fuel cell–battery hybrid electric vehicle (PV–FC–HEV). A high-fidelity MATLAB/Simulink model integrates a 6 kW proton-exchange membrane fuel cell (PEMFC), a 500 W photovoltaic subsystem with MPPT, and a lithium-ion battery (LiB) pack. While 1000 W/m² represents Standard Test Conditions (STC), the level of 400 W/m² was specifically selected to simulate average cloudy conditions common in urban driving environments, rather than standard NOCT (800 W/m²), to test the EMS’s robustness under significantly reduced PV support and stressed battery conditions (initial SOC = 30%). While surface contamination and the resulting performance degradation significantly impact real-world results, this study assumes a clean surface to establish an idealized performance baseline for the control algorithms. However, the authors acknowledge that contaminant accumulation is a key factor; future work will incorporate a degradation factor (e.g., a 10–15% efficiency penalty) to evaluate the reliability of these EMS strategies under actual operating conditions. ECMS achieved the lowest hydrogen consumption, saving up to 10 L compared with PID, while ANN maintained the most stable state of charge (SOC > 80%), minimizing deep discharge cycles and improving operational stability. FLC provided balanced operation under fluctuating irradiance. Overall, ANN offered the most harmonized energy flow and dynamic stability, whereas ECMS maximized fuel economy. The findings provide practical guidance for designing sustainable and intelligent control systems in next-generation hybrid electric vehicles. Full article

(This article belongs to the Special Issue Advances in Charging Systems and Charging Management Strategies for Battery Electric Vehicles)

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20 pages, 9556 KB

Open AccessArticle

Active Battery-Health Diagnostics for Real-World Applications Using a Bi-Directional Charger

by Tim Meulenbroeks, Thomas Köhler, Md. Mahamudul Hasan, Frédéric Reymond-Laruina, Thomas Geury, Omar Hegazy and Steven Wilkins

Batteries 2026, 12(4), 146; https://doi.org/10.3390/batteries12040146 - 21 Apr 2026

Viewed by 407

Abstract

Battery health data from real-world applications are vital for optimizing and predicting battery lifetime. This study presents the design and verification of an active battery-diagnostic system and method to collect such data. The system measures battery pack capacity and resistance by applying a [...] Read more.

Battery health data from real-world applications are vital for optimizing and predicting battery lifetime. This study presents the design and verification of an active battery-diagnostic system and method to collect such data. The system measures battery pack capacity and resistance by applying a diagnostic protocol via a bi-directional charger. This was demonstrated on a stationary-energy-storage application, under real-world conditions, to verify the system’s design requirements. Measurements at the start and the end of the demonstration period of a month resulted in an observed degradation of 1.79 ± 0.34% battery capacity and an increase of 1.42 ± 0.75% in battery resistance. The successful measurements of capacity and resistance prove the compatibility of the system with real-world battery systems and confirm the design requirements were met. The system was able to perform autonomous and in situ measurements while only requiring the addition of software to the battery management system and by using the bi-directional charger of the energy storage system. By repeatedly applying the same diagnostic protocol over time, this system enables consistent tracking of battery health. Full article

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

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13 pages, 2116 KB

Open AccessArticle

Rapid Estimation for the Maximum Remaining Capacity of Retired Lithium-Ion Batteries Based on CNN-CBAM-LSTM

by Aqing Li, Penghao Cui, Yifei Cao, Peng Zhou, Lei Yang, Guochen Bian and Zhendong Shao

Batteries 2026, 12(4), 145; https://doi.org/10.3390/batteries12040145 - 20 Apr 2026

Viewed by 321

Abstract

With the continuous increase in the number of Retired Lithium-Ion Batteries (RLBs), accurately estimating their Maximum Remaining Capacity (MRC) has become a key challenge for rapid sorting and secondary utilization. Conventional detection methods often suffer from low efficiency and limited scalability for large-scale [...] Read more.

With the continuous increase in the number of Retired Lithium-Ion Batteries (RLBs), accurately estimating their Maximum Remaining Capacity (MRC) has become a key challenge for rapid sorting and secondary utilization. Conventional detection methods often suffer from low efficiency and limited scalability for large-scale applications. To address these issues, this paper presents a rapid MRC estimation method using a hybrid Convolutional Neural Network (CNN), Conv Block Attention Module (CBAM), and Long Short-Term Memory (LSTM) architecture. The proposed approach extracts key voltage and capacity features from only the initial 30 min charging phase, integrating both factory and laboratory data. Specifically, the CNN captures local temporal patterns, the LSTM models long-term dependencies, and the CBAM adaptively emphasizes critical characteristics. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches, achieving a testing R² of 98.05% and a Mean Absolute Percentage Error (MAPE) of 1.60%. These results highlight the superior performance of the proposed framework, exhibiting strong potential for high-throughput battery sorting and large-scale health monitoring systems. Full article

(This article belongs to the Topic Advanced Energy Materials, Devices, and Intelligent Battery Management Systems)

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15 pages, 9699 KB

Open AccessArticle

Geometry-Regulated Thermal Performance of Sedimentation-Stable MicroPCM Composite Capsules for Battery Thermal Management Systems Fabricated via 3D Printing

by Xuguang Zhang, Michael C. Halbig, Mrityunjay Singh, Amjad Almansour and Yi Zheng

Batteries 2026, 12(4), 144; https://doi.org/10.3390/batteries12040144 - 18 Apr 2026

Viewed by 698

Abstract

Thermal management is critical for maintaining the safety and performance of lithium-ion batteries. Phase change materials (PCMs) have been widely studied as passive cooling media due to their high latent heat capacity, but major technical challenges remain due to their relatively low thermal [...] Read more.

Thermal management is critical for maintaining the safety and performance of lithium-ion batteries. Phase change materials (PCMs) have been widely studied as passive cooling media due to their high latent heat capacity, but major technical challenges remain due to their relatively low thermal conductivity and nanoparticle sedimentation in composite systems. In this work, a composite phase change material (PCM) consisting of paraffin wax, a microencapsulated phase change material (MicroPCM 28D), and nano carbon black is developed to enhance thermal stability and suppress particle sedimentation through increased viscosity of the PCM matrix. Five capsule geometries fabricated by fused filament fabrication (FFF) 3D printing are experimentally investigated under airflow velocities ranging from 0 to 10 m s⁻¹. Wind tunnel experiments with infrared thermography are used to evaluate the thermal response of the PCM capsules. The results show that airflow velocity and capsule geometry strongly influence heat dissipation behavior. Compared with conventional wax composites, the MicroPCM 28D composite capsules reduce peak temperature by approximately 2–4 °C under airflow velocities of 0–10 m/s. These findings provide insights into geometry-regulated convection and stable composite PCM design for lithium-ion battery thermal management systems. Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System: 3rd Edition)

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23 pages, 1607 KB

Open AccessArticle

Simulation and Optimization of V2G Energy Exchange in an Energy Community Using MATLAB and Multi-Objective Genetic Algorithm Optimization

by Mohammad Talha Yaar Khan and Jozsef Menyhart

Batteries 2026, 12(4), 143; https://doi.org/10.3390/batteries12040143 - 17 Apr 2026

Viewed by 308

Abstract

The Vehicle-to-Grid (V2G) technology is considered one of the best solutions for integrating renewable energy systems; however, most literature reports favorable economic results using synthetic data, without accounting for seasonal or market limitations. The current research presents the results of the MATLAB R2023b [...] Read more.

The Vehicle-to-Grid (V2G) technology is considered one of the best solutions for integrating renewable energy systems; however, most literature reports favorable economic results using synthetic data, without accounting for seasonal or market limitations. The current research presents the results of the MATLAB R2023b (Version 23.2, MathWorks, Natick, MA, USA) simulation of the 100-household energy community in Debrecen, Hungary, with 30 electric vehicles (EVs) using entirely simulation-based Lithium Iron Phosphate (LiFePO4) batteries, a simulation-based 150 kW solar photovoltaic (PV) system, and a simulation-based 200 kW wind power system, using real meteorological data for January 2024. The optimization of charging/discharging for electric vehicles was performed using a multi-objective genetic algorithm (GA) over 30 days at a 15 min time resolution, accounting for stochastic loads and temperature effects on battery degradation, with a sensitivity analysis of key parameters. The results of the optimized solution for the electric vehicle charging/discharging were unexpected: the total energy cost increased by 68.9% ($4337.65 to $7327.54), the peak demand increased by 266.2% (31.9 to 116.9 kW), the degradation cost was $479.63, the load factor was reduced from 0.847 to 0.722, and the SOC constraint was violated for 0.758% of measurements. The V2G is not economically viable under current Hungarian pricing and Central Europe winter conditions. Results are robust for varying parameters using sensitivity analysis and Pareto front tracing. The break-even point is achieved when ratios of peak-to-off-peak prices are above 3.5:1. Seasonal policies and market reforms are critical for V2G viability. Importantly, the influence of inherent design deficiencies in the optimization model on the reported results cannot be ruled out. Full article

(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)

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11 pages, 19852 KB

Open AccessArticle

Fabrication of Thin Copper Anode Current Collectors on Ceramic Solid Electrolytes Using Atmospheric Plasma Spraying for Anode-Free Solid-State Batteries

by Andre Borchers, Timo Paschen, Manuela Ockel, Florian Vollnhals, Cornelius Dirksen, Martin Muckelbauer, Berik Uzakbaiuly, George Sarau, Jörg Franke and Silke Christiansen

Batteries 2026, 12(4), 142; https://doi.org/10.3390/batteries12040142 - 16 Apr 2026

Viewed by 461

Abstract

Metal anodes offer substantially higher specific and volumetric capacities than conventional anode materials such as graphite in lithium-ion batteries or hard carbon in sodium-ion batteries. However, the integration of metal anodes into solid-state batteries poses significant challenges, particularly with respect to processing, interfacial [...] Read more.

Metal anodes offer substantially higher specific and volumetric capacities than conventional anode materials such as graphite in lithium-ion batteries or hard carbon in sodium-ion batteries. However, the integration of metal anodes into solid-state batteries poses significant challenges, particularly with respect to processing, interfacial stability, and cell assembly. Anode-free solid-state batteries (AFSSBs) address these challenges by eliminating the pre-installed metal anode, instead forming the metal in situ during the initial charging (formation) step. In anode-free solid-state batteries, the quality of the interfacial contact is particularly critical, as insufficient contact can lead to locally increased current densities. Consequently, the initial metal plating during the formation step plays a decisive role in determining the homogeneity and stability of the anode interface. Furthermore, conventional battery-grade copper foils (~10 µm) are considerably thicker than required for the targeted C-rates and are difficult to use as stand-alone anode-free current collectors, thereby hindering the industrial production of anode-free solid-state batteries. In this publication, we demonstrate the application of atmospheric plasma spraying (APS) to fabricate thin copper current collectors directly on the ceramic solid electrolytes LAGP (lithium aluminium germanium phosphate) and BASE (beta-alumina solid electrolyte) with superior interface contact. No mechanical damage or diffusion of copper into the solid electrolyte nor formation of secondary phases at the interfaces were observed in SEM or EDS despite the elevated process temperature. LAGP with a thickness as low as 300 µm was successfully coated and subsequently used for plating/stripping experiments. Finally, dense sodium metal was plated at the copper-substrate interface of a 1.4 mm thick BASE sample. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Interface Science in Batteries)

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16 pages, 3313 KB

Open AccessArticle

MOF-Derived Fe₂O₃@Fe₃O₄-Coated Carbon Fiber Fabric as a Negative Electrode for Flexible Supercapacitors

by Andrés González-Banciella, David Martinez-Diaz, Joaquín Artigas-Arnaudas, Bianca K. Muñoz, María Sánchez and Alejandro Ureña

Batteries 2026, 12(4), 141; https://doi.org/10.3390/batteries12040141 - 15 Apr 2026

Viewed by 291

Abstract

Owing to the increasing demand for wearable electronics, flexible energy storage devices, such as supercapacitors, have gained interest in the electronic industry. In this context, asymmetric configurations have emerged as a promising strategy for the development of wider potential window supercapacitors. On the [...] Read more.

Owing to the increasing demand for wearable electronics, flexible energy storage devices, such as supercapacitors, have gained interest in the electronic industry. In this context, asymmetric configurations have emerged as a promising strategy for the development of wider potential window supercapacitors. On the other hand, MOF-derived synthesis of transition metal oxides is known to result in porous materials, which exhibit better electrochemical performance. In this work, a MOF-derived Fe₂O₃ coating on carbon fiber woven substrate is proposed as a negative supercapacitor electrode for asymmetric flexible devices. Moreover, the MOF calcination time was evaluated in order to ensure the best electrochemical performance possible, achieving for the sample calcined for 2 h a specific capacitance of 18.8 F/g at a current density of 200 mA/g and an excellent rate capability. In addition, not only was this promising material obtained, but an asymmetric flexible supercapacitor based on two MOF-derived TMO coatings on carbon fiber woven electrodes was manufactured and characterized as a proof of concept. This supercapacitor displayed a specific capacitance of 229 mF/cm², an energy density of 0.067 mWh/cm² and a power density of 0.11 mW/cm² at 0.15 mA/cm². Moreover, the flexible supercapacitor retained 94.1% of its capacitance even after being bent to 90°. Full article

(This article belongs to the Special Issue Advanced Carbon-Based Materials for Next-Generation Batteries and Supercapacitors—2nd Edition)

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32 pages, 6357 KB

Open AccessArticle

HVC-NSGA-III with Thermal–Electrochemical Degradation Coupling for Four-Objective Day-Ahead BESS Dispatch and SOH-Adaptive Knee-Point Selection

by Jiachen Zhao, Hongjie Li, Linxuan Li and Dechun Yuan

Batteries 2026, 12(4), 140; https://doi.org/10.3390/batteries12040140 - 15 Apr 2026

Viewed by 402

Abstract

Isothermal dispatch models for battery energy storage systems (BESSs) systematically underestimate degradation costs because dispatch-induced Joule heating elevates cell temperature and accelerates ageing through Arrhenius-type kinetics. This paper proposes three integrated contributions. First, a thermal–electrochemical coupling loop embeds a first-order lumped thermal model [...] Read more.

Isothermal dispatch models for battery energy storage systems (BESSs) systematically underestimate degradation costs because dispatch-induced Joule heating elevates cell temperature and accelerates ageing through Arrhenius-type kinetics. This paper proposes three integrated contributions. First, a thermal–electrochemical coupling loop embeds a first-order lumped thermal model within the dispatch simulation: cell temperature is updated from I²R heat generation and Newton cooling at each time step, and the resulting temperature trajectory feeds into the Arrhenius stress factors of a semi-empirical degradation model combining

\sqrt{Δ t}

-based calendar ageing with Rainflow-based cycle ageing, enabling the optimiser to discover thermally self-regulating strategies. This coupling is critical because, as the results demonstrate, ignoring it leads to systematic underestimation of degradation costs by up to 13%. Second, the resulting four-objective problem (negative profit, thermally coupled degradation cost, SOC deviation, and CVaR imbalance penalty) is solved by a hypervolume-contribution-enhanced NSGA-III (HVC-NSGA-III), which augments reference-point selection with an archive pruned by removing the solution of the smallest individual hypervolume contribution, concentrating Pareto resolution in the knee region. Third, an SOH-adaptive knee-point selection assigns the degradation weight as a monotone function of ageing degree

(1 - SOH) / (1 - {SOH}_{EOL})

, automatically tightening dispatch conservatism as remaining useful life diminishes. Simulations on ENTSO-E data over 96 h show the following: (i) thermal coupling shifts the Pareto front by 8–15% in the degradation dimension with temperature excursions up to 7 K; (ii) HVC-NSGA-III improves hypervolume by 8.7% over standard NSGA-III; (iii) SOH-adaptive selection reduces capacity loss by 27.4% at only 9.1% revenue cost; and (iv) ablation confirms Rainflow (24.8%) and thermal coupling (13.1%) as the two largest contributors. Full article

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

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22 pages, 3514 KB

Open AccessArticle

Collaborative Control Strategy of Megawatt-Level Zinc–Iron Flow Battery Energy Storage System Based on Source–Grid–Load–Storage Integration

by Shaopeng Wang, Laiqiang Kong, Puiki Leung, Sidun Fang, Ke Yang and Xinhui Fan

Batteries 2026, 12(4), 139; https://doi.org/10.3390/batteries12040139 - 14 Apr 2026

Viewed by 418

Abstract

The zinc–iron redox battery (ZIRB) has become one of the hot technologies of electrochemical energy storage due to its safety, stability and low cost of the electrolyte. In this paper, a collaborative control strategy for an MW-level zinc–iron flow battery energy storage system [...] Read more.

The zinc–iron redox battery (ZIRB) has become one of the hot technologies of electrochemical energy storage due to its safety, stability and low cost of the electrolyte. In this paper, a collaborative control strategy for an MW-level zinc–iron flow battery energy storage system is studied, and the operation control and management of the MW-level zinc–iron flow battery energy storage system are coordinated and optimized to improve the operation efficiency of the whole system. The model of the megawatt zinc–iron flow battery energy storage system is established in this paper. A ZIRB state of charge (SOC) estimation method based on least squares (LS) and an extended Kalman filter (EKF) is proposed. Experiments under constant-current discharge show that the proposed LS-EKF method can achieve accurate SOC estimation for the tested ZIRB system, with a maximum estimation error of approximately 2.3%. Experiments show that the proposed algorithm has good accuracy, rapidity and robustness at different SOC initial values. According to SOC differences between battery cells, the coordination strategy of each cell is designed to meet the requirements of frequency modulation while taking into account the safety of battery operation. On this basis, the optimization problem is designed and solved with the goal of optimal frequency modulation effect and battery energy loss, and the collaborative control of the MW-level ZIRB energy storage system is realized. Full article

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25 pages, 3666 KB

Open AccessArticle

Toward Safe and Reliable Batteries: Multi-Objective Optimization of a Serpentine Cooling Channel for Battery Thermal Management Using GPR and NSGA-II

by Nguyen Minh Chau, Le Van Quynh, Nguyen Manh Quang, Nguyen Thi Hong Ngoc, Nguyen Thanh Cong and Nguyen Trong Hieu

Batteries 2026, 12(4), 138; https://doi.org/10.3390/batteries12040138 - 14 Apr 2026

Viewed by 503

Abstract

Thermal management plays a critical role in maintaining the safety and reliability of lithium-ion batteries by limiting excessive temperature rise and reducing non-uniform temperature distribution within battery packs. This study proposes a geometry-driven multi-objective optimization framework for a serpentine liquid-cooling channel to enhance [...] Read more.

Thermal management plays a critical role in maintaining the safety and reliability of lithium-ion batteries by limiting excessive temperature rise and reducing non-uniform temperature distribution within battery packs. This study proposes a geometry-driven multi-objective optimization framework for a serpentine liquid-cooling channel to enhance the thermal behavior of a battery module under fixed operating conditions. A three-dimensional computational fluid dynamics (CFD) model was developed for a 40-cell battery module, and Latin hypercube sampling was employed to generate training data for Gaussian Process Regression (GPR) surrogate models. Three geometric design variables, namely, channel thickness (t_c), wall thickness (t_w), and contact surface angle (θ), were considered, while the maximum battery temperature (T_max) and the maximum temperature difference within the battery pack (ΔT_max) were selected as optimization objectives. Sensitivity analysis showed that wall thickness was the dominant parameter, contributing 65.41% and 64.77% to the variations in T_max and ΔT_max, respectively, followed by channel thickness, whereas the influence of the contact surface angle was comparatively limited. The trained GPR models were then coupled with the non-dominated sorting genetic algorithm (NSGA-II) to identify the optimal channel geometry. The optimal design was obtained at t_c = 2.95 mm, t_w = 0.949 mm, and θ = 60°. CFD validation confirmed that the optimized design reduced T_max from 307.639 K to 306.653 K, corresponding to a temperature drop of 0.986 K, while ΔT_max decreased from 8.752 K to 7.887 K, representing a reduction of 9.88%. Although the reduction in T_max is modest, the improvement in temperature uniformity is meaningful, which benefits cell consistency and long-term reliability. These results demonstrate that geometric optimization of cooling channels can provide an effective and energy-efficient approach to improving thermal uniformity in lithium-ion battery systems. Full article

(This article belongs to the Special Issue Thermal Runaway and Thermal Management: Toward Safe and Reliable Batteries)

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21 pages, 11025 KB

Open AccessArticle

A Multi-Step RUL Prediction Method for Lithium-Ion Batteries Based on Multi-Scale Temporal Features and Frequency-Domain Spectral Interaction

by Ye Tu, Shixiong Xu, Jie Wang and Mengting Jin

Batteries 2026, 12(4), 137; https://doi.org/10.3390/batteries12040137 - 14 Apr 2026

Viewed by 415

Abstract

With the rapid development of new energy vehicles and energy storage systems, accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is of great importance for predictive maintenance and operational safety. However, battery degradation during cycling usually exhibits multi-scale characteristics, including [...] Read more.

With the rapid development of new energy vehicles and energy storage systems, accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is of great importance for predictive maintenance and operational safety. However, battery degradation during cycling usually exhibits multi-scale characteristics, including long-term degradation trends, stage-wise drifts, and stochastic disturbances, which makes existing methods still face significant challenges in multi-step forecasting and cross-domain generalization. To address this issue, this paper proposes a time–frequency fusion model for multi-step RUL prediction, termed TF-RULNet (Time-Frequency RUL Network). The model takes cycle-level feature sequences as input and consists of three components: a multi-scale temporal convolution encoder (MSTC) for parallel extraction of degradation cues at different temporal scales; a multi-head spectral interaction module (MHSI), which performs 1D-FFT along the temporal dimension for each head and further applies adaptive band-wise mask refinement to capture local spectral structures and hierarchical band patterns with a computational complexity of

O (L log L)

; and a cross-gated fusion module (CGF), which generates gating signals from the summary of one domain to modulate the features of the other domain, thereby enabling dynamic balancing and complementary enhancement of time–frequency information. Experiments are conducted on the NASA dataset (B005/B007) for in-domain evaluation, and further cross-dataset tests from NASA to the Maryland dataset (CS-35/CS-37) are carried out to verify the robustness of the proposed model under distribution shifts. The results show that, compared with the strongest baseline PatchTST, TF-RULNet reduces RMSE and MAE by more than 38.23% and 50.51%, respectively, in cross-dataset generalization, while achieving an additional RMSE reduction of about 24% in in-domain prediction. In summary, TF-RULNet can effectively characterize the multi-scale time–frequency degradation patterns of batteries and improve cross-domain generalization, providing a high-accuracy and scalable modeling solution for practical battery health management and life prognostics. Full article

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45 pages, 21279 KB

Open AccessReview

Synergistic Heteroatom Doping in Hard Carbon Anodes: Unlocking High Performance in Potassium-Ion Batteries

by Narasimharao Kitchamsetti, Sungwook Mhin, HyukSu Han and Ana L. F. de Barros

Batteries 2026, 12(4), 136; https://doi.org/10.3390/batteries12040136 - 14 Apr 2026

Viewed by 680

Abstract

The increasing demand for energy storage systems that deliver both high performance and environmental sustainability has driven significant interest in potassium-ion batteries (PIBs) as viable substitutes for lithium-ion batteries (LIBs). However, their large-scale application remains constrained by inherent drawbacks, such as limited energy [...] Read more.

The increasing demand for energy storage systems that deliver both high performance and environmental sustainability has driven significant interest in potassium-ion batteries (PIBs) as viable substitutes for lithium-ion batteries (LIBs). However, their large-scale application remains constrained by inherent drawbacks, such as limited energy density, poor cycling stability, and suboptimal electrochemical performance. Among various anode candidates, hard carbon (HC) has attracted considerable attention owing to its structural robustness and large specific surface area (SSA). Recent research indicates that heteroatom doping is an effective strategy to enhance the electrochemical properties of HC, leading to improved potassium (K) storage capacity, superior rate performance, and extended cycle life. This review summarizes recent progress in doped HC anodes, with a particular focus on the transition from single-element doping to multi-element doping approaches. The synergistic interactions within multi-dopant systems are systematically discussed, highlighting their potential to overcome the shortcomings of single-doped materials and achieve comprehensive performance improvements. Finally, current challenges and future perspectives for the development of advanced doped HC anodes in PIBs are outlined. Full article

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27 pages, 3096 KB

Open AccessArticle

A Data-Driven Framework for Lithium-Ion Battery Remaining Useful Life Prediction Using CNN and Machine Learning Models

by Merve Yenioglu, Engin Aycicek and Ozan Erdinc

Batteries 2026, 12(4), 135; https://doi.org/10.3390/batteries12040135 - 13 Apr 2026

Viewed by 633

Abstract

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for improving the reliability, safety, and maintenance planning of electric vehicles and energy storage systems. However, battery degradation is a complex and nonlinear process influenced by multiple operational conditions, making [...] Read more.

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for improving the reliability, safety, and maintenance planning of electric vehicles and energy storage systems. However, battery degradation is a complex and nonlinear process influenced by multiple operational conditions, making reliable RUL estimation a challenging task. Although numerous data-driven approaches have been proposed in the literature, many studies focus primarily on improving prediction accuracy using a single modeling technique, while limited attention has been given to systematic comparisons of different algorithms and the quantification of prediction uncertainty. This study proposes a comprehensive data-driven framework for lithium-ion battery RUL prediction by integrating both traditional machine learning and deep learning approaches. A Convolutional Neural Network (CNN) model is developed to capture nonlinear degradation patterns from battery cycling data. The dataset was divided using a battery-wise validation strategy to evaluate model generalization. In addition, conventional machine learning algorithms, including k-Nearest Neighbors (KNNs), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), are implemented to perform a comparative analysis of different predictive models. Key degradation-related features derived from voltage, current, temperature, and cycle information are extracted through a structured preprocessing pipeline. Furthermore, prediction uncertainty is quantified by constructing confidence intervals around the estimated RUL values. The predictive performance of the models is evaluated using prognostic metrics such as Root Mean Square Error (RMSE), Relative Prediction Error (RPE), and Prognostic Horizon (PH). The performance of the models is evaluated using multiple prognostic metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²), to ensure a comprehensive assessment of prediction accuracy. The experimental results demonstrate that the proposed framework provides accurate RUL predictions. Among the evaluated models, the CNN achieved the best performance with a Mean Absolute Error (MAE) of 7.75 and a Root Mean Square Error (RMSE) of 10.80, outperforming traditional machine learning models such as Random Forest and XGBoost. The KNN model also showed competitive performance with an RMSE of 12.07 and an R² value of 0.64, indicating that similarity-based learning can effectively capture battery degradation patterns. Full article

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30 pages, 9201 KB

Open AccessArticle

Comparing Heating Systems for Degradation-Aware Battery Thermal Management in Electric Minibuses: PTC Heater vs. Heat Pump

by Lukas Acker, Luis Vincent Fiore, Erik Stenger and Johannes Konrad

Batteries 2026, 12(4), 134; https://doi.org/10.3390/batteries12040134 - 13 Apr 2026

Viewed by 635

Abstract

Battery thermal management is critical for electric vehicles operating in cold climates, where low temperatures reduce battery efficiency, limit regenerative braking and accelerate degradation. This study compares PTC heater and heat pump systems for battery thermal management in electric minibuses using optimization-based control [...] Read more.

Battery thermal management is critical for electric vehicles operating in cold climates, where low temperatures reduce battery efficiency, limit regenerative braking and accelerate degradation. This study compares PTC heater and heat pump systems for battery thermal management in electric minibuses using optimization-based control strategies. A control-oriented model of the vehicle thermal system, validated against chassis dynamometer measurements, and a heat pump system model, validated against testbed measurements, are used to optimize thermal management strategies via nonlinear programming, minimizing energy consumption while accounting for Joule losses, regenerative braking energy and thermal system consumption. Battery degradation is evaluated using a Doyle-Fuller-Newman (DFN) electrochemical model incorporating physics-based degradation mechanisms. Results show that optimized thermal management strategies can simultaneously reduce energy consumption and degradation. With the PTC heater system, only marginal improvements are achievable, and further reductions in degradation come at the cost of increased energy consumption. In contrast, the more efficient heat pump system enables simultaneous reductions in both degradation and energy consumption through optimized thermal management. These findings highlight that advanced thermal management leveraging heat pump systems can enhance both driving range and battery lifetime in cold-climate electric vehicle operations. Full article

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

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28 pages, 2709 KB

Open AccessReview

Review of Direct Lithium Extraction Methods: Recent Advances and Outlook

by Olukayode Fatoki, Santosh Kumar Parupelli, Manpreet Kaur, Alex Mathew, Amir Rehmat and Salil Desai

Batteries 2026, 12(4), 133; https://doi.org/10.3390/batteries12040133 - 12 Apr 2026

Viewed by 1834

Abstract

Lithium-ion batteries (LIBs) have become the prominent energy storage technology because of their high specific energy, longer lifespan, and excellent efficiency. Traditional lithium extraction processes are energy intensive and time-consuming. Direct lithium extraction (DLE) methods provide a more sustainable and efficient alternative. This [...] Read more.

Lithium-ion batteries (LIBs) have become the prominent energy storage technology because of their high specific energy, longer lifespan, and excellent efficiency. Traditional lithium extraction processes are energy intensive and time-consuming. Direct lithium extraction (DLE) methods provide a more sustainable and efficient alternative. This review offers a comprehensive overview of lithium-ion battery resources and direct lithium extraction methods. The detailed discussion of the DLE methods, which include adsorption, ion exchange, solvent extraction, membranes separation, and electro-chemical systems is presented. A comprehensive analysis of the recent technological advances of the direct lithium extraction processes in terms of technology readiness levels, and commercial potential is reported. The advantages and the technical challenges of the DLE methods are also reported. Finally, the review outlines the artificial intelligence outlook of the DLE processes. The review aims to provide deeper insights into the limitations and the opportunities of DLE methods towards crucial future research efforts for lithium-ion batteries advancements. Full article

(This article belongs to the Special Issue Battery Manufacturing: Current Status, Challenges, and Opportunities: 2nd Edition)

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23 pages, 4036 KB

Open AccessArticle

A Comprehensive Study of Large-Format Pouch Cell Thermal Behaviour and Electrical Performance When Incorporating Cell Clamping

by Xujian Zhang, Giles Prentice, David Ainsworth and James Marco

Batteries 2026, 12(4), 132; https://doi.org/10.3390/batteries12040132 - 10 Apr 2026

Viewed by 353

Abstract

In battery systems, external mechanical compression is commonly applied to pouch/prismatic cells to improve their electrical performance and mechanical integrity. However, cell clamping can hinder system heat rejection by introducing an additional thermal insulation layer. A novel battery clamping scheme was designed with [...] Read more.

In battery systems, external mechanical compression is commonly applied to pouch/prismatic cells to improve their electrical performance and mechanical integrity. However, cell clamping can hinder system heat rejection by introducing an additional thermal insulation layer. A novel battery clamping scheme was designed with reduced contact area to explore the system thermal behaviour under different cooling regimes. Experimental data obtained from battery characterisation and performance tests is analysed with a thermal-coupled equivalent circuit model to quantify changes in cell impedance and system thermal properties. By reducing the clamping area by 70%, the temperature rise of the cell was decreased by 0.5 °C in comparison to the reference condition of a cell with no clamping during a 1C discharge under natural convection. Under immersion cooling using BOT2100 dielectric liquid, the thermal benefit was amplified, resulting in temperature reductions of 0.9 °C at 1C and 4 °C at 3C. The principal conclusion of this work is that reshaping the clamping plate has the potential to reduce ohmic heating by lowering battery internal resistance, which outweighs the additional thermal resistance introduced by partial surface coverage. This novel experimental approach demonstrates the potential to improve battery thermal management through geometry-optimised cell clamping, particularly for high-power applications, and further directs the community towards cell clamping solution designed to optimise both thermal and mechanical cell performance. Full article

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34 pages, 1805 KB

Open AccessReview

Sodium-Ion Batteries: Advances, Challenges, and Roadmap to Commercialization

by Abniel Machín and Francisco Márquez

Batteries 2026, 12(4), 131; https://doi.org/10.3390/batteries12040131 - 9 Apr 2026

Viewed by 3762

Abstract

Sodium-ion batteries (SIBs) have emerged as one of the most promising alternatives to lithium-ion systems, driven by the abundance and low cost of sodium resources as well as the urgent demand for sustainable large-scale energy storage. In recent years, remarkable advances have been [...] Read more.

Sodium-ion batteries (SIBs) have emerged as one of the most promising alternatives to lithium-ion systems, driven by the abundance and low cost of sodium resources as well as the urgent demand for sustainable large-scale energy storage. In recent years, remarkable advances have been achieved in electrode materials, electrolytes, and interfacial engineering, which have significantly improved the electrochemical performance of SIBs. Hard carbons and alloy-type anodes have shown encouraging progress in balancing capacity and stability, while layered oxides, polyanionic compounds, and Prussian blue analogues are leading candidates for cathodes due to their structural diversity and tunable redox properties. Concurrently, the development of advanced liquid and solid electrolytes, together with strategies to control the solid–electrolyte interphase (SEI) and cathode–electrolyte interphase (CEI), is enhancing safety and long-term cycling. Despite these achievements, critical challenges remain, including limited energy density, volumetric expansion in alloying anodes, interfacial instability, and scalability issues. This review provides a comprehensive overview of the fundamental principles, recent material innovations, and failure mechanisms of SIBs, and highlights the current status of industrial progress led by companies such as Faradion, HiNa Battery, CATL, and Tiamat. Finally, future perspectives are discussed, emphasizing the role of sodium-ion technology in grid-scale storage, renewable energy integration, and sustainable battery recycling. By bridging academic advances and industrial development, this article outlines the roadmap toward the commercialization of sodium-ion batteries. Full article

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

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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

Viewed by 489

Abstract

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

Viewed by 494

Abstract

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

Viewed by 528

Abstract

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

Viewed by 416

Abstract

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

Viewed by 760

Abstract

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

Viewed by 396

Abstract

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

Viewed by 588

Abstract

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

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Abstract

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

Viewed by 517

Abstract

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

Viewed by 1629

Abstract

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

Viewed by 1482

Abstract

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

Viewed by 534

Abstract

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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