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Search Results (3,361)

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Keywords = Li–S batteries

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32 pages, 1435 KiB  
Review
Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies
by Emmanuel A. Merchán-Cruz, Samuel Moveh, Oleksandr Pasha, Reinis Tocelovskis, Alexander Grakovski, Alexander Krainyukov, Nikita Ostrovenecs, Ivans Gercevs and Vladimirs Petrovs
Sensors 2025, 25(15), 4834; https://doi.org/10.3390/s25154834 (registering DOI) - 6 Aug 2025
Abstract
Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused [...] Read more.
Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article
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41 pages, 7308 KiB  
Review
Challenges and Opportunities for Extending Battery Pack Life Using New Algorithms and Techniques for Battery Electric Vehicles
by Pedro S. Gonzalez-Rodriguez, Jorge de J. Lozoya-Santos, Hugo G. Gonzalez-Hernandez, Luis C. Felix-Herran and Juan C. Tudon-Martinez
World Electr. Veh. J. 2025, 16(8), 442; https://doi.org/10.3390/wevj16080442 - 5 Aug 2025
Abstract
The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs [...] Read more.
The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs by exploring advances in degradation modeling, adaptive Battery Management Systems (BMSs), electronic component simulations, and real-world usage profiling. The authors have systematically analyzed over 80 recent studies using a PRISMA-guided review protocol. A novel comparative framework highlights gaps in current literature, particularly regarding real-world driving impacts, ripple current effects, and second-life battery applications. This review article critically compares model-driven, data-driven, and hybrid model approaches, emphasizing trade-offs in interpretability, accuracy, and deployment feasibility. Finally, the review links battery life extension to broader sustainability metrics, including circular economy models and predictive maintenance algorithms. This review offers actionable insights for researchers, engineers, and policymakers aiming to design longer-lasting and more sustainable electric mobility systems. Full article
(This article belongs to the Special Issue Electric Vehicle Battery Pack and Electric Motor Sizing Methods)
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14 pages, 5700 KiB  
Article
The Design of Diatomite/TiO2/MoS2/Nitrogen-Doped Carbon Nanofiber Composite Separators for Lithium–Sulfur Batteries
by Wei Zhong, Wenjie Xiao, Jianfei Liu, Chuxiao Yang, Sainan Liu and Zhenyang Cai
Materials 2025, 18(15), 3654; https://doi.org/10.3390/ma18153654 - 4 Aug 2025
Viewed by 61
Abstract
Severe polysulfide shuttling and sluggish redox kinetics critically hinder lithium–sulfur (Li-S) battery commercialization. In this study, a multifunctional diatomite (DE)/TiO2/MoS2/N-doped carbon nanofiber (NCNF) composite separator was fabricated via hydrothermal synthesis, electrospinning, and carbonization. DE provides dual polysulfide suppression, encompassing [...] Read more.
Severe polysulfide shuttling and sluggish redox kinetics critically hinder lithium–sulfur (Li-S) battery commercialization. In this study, a multifunctional diatomite (DE)/TiO2/MoS2/N-doped carbon nanofiber (NCNF) composite separator was fabricated via hydrothermal synthesis, electrospinning, and carbonization. DE provides dual polysulfide suppression, encompassing microporous confinement and electrostatic repulsion. By integrating synergistic catalytic effects from TiO2 and MoS2 nanoparticles, which accelerate polysulfide conversion, and conductive NCNF networks, which facilitate rapid charge transfer, this hierarchical design achieves exceptional electrochemical performance: a 1245.6 mAh g−1 initial capacity at 0.5 C and 65.94% retention after 200 cycles. This work presents a rational multi-component engineering strategy to suppress shuttle effects in high-energy-density Li-S batteries. Full article
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18 pages, 1214 KiB  
Article
Predictive Maintenance System to RUL Prediction of Li-Ion Batteries and Identify the Fault Type of Brushless DC Electric Motor from UAVs
by Dragos Alexandru Andrioaia
Sensors 2025, 25(15), 4782; https://doi.org/10.3390/s25154782 - 3 Aug 2025
Viewed by 145
Abstract
Unmanned Aerial Vehicles have started to be used more and more due to the benefits they bring. Failure of Unmanned Aerial Vehicle components may result in loss of control, which may cause property damage or personal injury. In order to increase the operational [...] Read more.
Unmanned Aerial Vehicles have started to be used more and more due to the benefits they bring. Failure of Unmanned Aerial Vehicle components may result in loss of control, which may cause property damage or personal injury. In order to increase the operational safety of the Unmanned Aerial Vehicle, the implementation of a Predictive Maintenance system using the Internet of Things is required. In this paper, the authors propose a new architecture of Predictive Maintenance system for Unmanned Aerial Vehicles that is able to identify the fault type of Brushless DC electric motor and determine the Remaining Useful Life of the Li-ion batteries. In order to create the Predictive Maintenance system within the Unmanned Aerial Vehicle, an architecture based on Fog Computing was proposed and Machine Learning was used to extract knowledge from the data. The proposed architecture was practically validated. Full article
(This article belongs to the Section Fault Diagnosis & Sensors)
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125 pages, 50190 KiB  
Review
Sulfurized Polyacrylonitrile for Rechargeable Batteries: A Comprehensive Review
by Mufeng Wei
Batteries 2025, 11(8), 290; https://doi.org/10.3390/batteries11080290 - 1 Aug 2025
Viewed by 167
Abstract
This paper presents a comprehensive review of research on sulfurized polyacrylonitrile (SPAN) for rechargeable batteries which was firstly reported by Jiulin Wang in July 2002. Spanning over two decades (2002–2025), this review cites over 600 publications, covering various aspects of SPAN-based battery systems. [...] Read more.
This paper presents a comprehensive review of research on sulfurized polyacrylonitrile (SPAN) for rechargeable batteries which was firstly reported by Jiulin Wang in July 2002. Spanning over two decades (2002–2025), this review cites over 600 publications, covering various aspects of SPAN-based battery systems. These include SPAN chemical structure, structural evolution during synthesis, redox reaction mechanism, synthetic conditions, cathode, electrolyte, binder, current collector, separator, anode, SPAN as additive, SPAN as anode, and high-energy SPAN cathodes. As this field continues to advance rapidly and garners significant interest, this review aims to provide researchers with a thorough and in-depth overview of the progress made over the past 23 years. Additionally, it highlights emerging trends and outlines future directions for SPAN research and its practical applications in energy storage technologies. Full article
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21 pages, 1573 KiB  
Review
A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
by Juliano Pimentel, Alistair A. McEwan and Hong Qing Yu
Appl. Sci. 2025, 15(15), 8538; https://doi.org/10.3390/app15158538 (registering DOI) - 31 Jul 2025
Viewed by 121
Abstract
This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered [...] Read more.
This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R2 values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. Full article
(This article belongs to the Special Issue Design and Applications of Real-Time Embedded Systems)
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18 pages, 3493 KiB  
Article
Red-Billed Blue Magpie Optimizer for Modeling and Estimating the State of Charge of Lithium-Ion Battery
by Ahmed Fathy and Ahmed M. Agwa
Electrochem 2025, 6(3), 27; https://doi.org/10.3390/electrochem6030027 - 31 Jul 2025
Viewed by 196
Abstract
The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique [...] Read more.
The energy generated from renewable sources has an intermittent nature since solar irradiation and wind speed vary continuously. Hence, their energy should be stored to be utilized throughout their shortage. There are various forms of energy storage systems while the most widespread technique is the battery storage system since its cost is low compared to other techniques. Therefore, batteries are employed in several applications like power systems, electric vehicles, and smart grids. Due to the merits of the lithium-ion (Li-ion) battery, it is preferred over other kinds of batteries. However, the accuracy of the Li-ion battery model is essential for estimating the state of charge (SOC). Additionally, it is essential for consistent simulation and operation throughout various loading and charging conditions. Consequently, the determination of real battery model parameters is vital. An innovative application of the red-billed blue magpie optimizer (RBMO) for determining the model parameters and the SOC of the Li-ion battery is presented in this article. The Shepherd model parameters are determined using the suggested optimization algorithm. The RBMO-based modeling approach offers excellent execution in determining the parameters of the battery model. The suggested approach is compared to other programmed algorithms, namely dandelion optimizer, spider wasp optimizer, barnacles mating optimizer, and interior search algorithm. Moreover, the suggested RBMO is statistically evaluated using Kruskal–Wallis, ANOVA tables, Friedman rank, and Wilcoxon rank tests. Additionally, the Li-ion battery model estimated via the RBMO is validated under variable loading conditions. The fetched results revealed that the suggested approach achieved the least errors between the measured and estimated voltages compared to other approaches in two studied cases with values of 1.4951 × 10−4 and 2.66176 × 10−4. Full article
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11 pages, 1401 KiB  
Communication
Graphene-Enhanced FePO4 Composites with Superior Electrochemical Performance for Lithium-Ion Batteries
by Jinde Yu, Shuchun Hu, Yaohan Zhang, Yin Liu, Wenjuan Ren, Aipeng Zhu, Yanqi Feng, Zhe Wang, Dunan Rao, Yuqin Yang, Heng Zhang, Runhan Liu and Shunying Chang
Materials 2025, 18(15), 3604; https://doi.org/10.3390/ma18153604 - 31 Jul 2025
Viewed by 210
Abstract
In this study, we successfully synthesized olivine-type FePO4 via an in situ oxidation method and further developed two composite cathode materials (o-FePO4-1/GR-1 and o-FePO4-1/GR-2) by incorporating graphene. The composites were characterized using scanning electron microscopy (SEM), X-ray diffraction [...] Read more.
In this study, we successfully synthesized olivine-type FePO4 via an in situ oxidation method and further developed two composite cathode materials (o-FePO4-1/GR-1 and o-FePO4-1/GR-2) by incorporating graphene. The composites were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray Photoelectron Spectroscopy (XPS), revealing a three-dimensional porous layered structure with an enhanced surface area and strong interaction between FePO4 nanoparticles and graphene layers. Electrochemical tests demonstrated that the composite electrodes exhibited significantly improved performance compared to pristine FePO4, with discharge capacities of 147 mAh g−1 at 1C and 163 mAh g−1 at 0.1C for o-FePO4-1/GR-2, approaching the level of LiFePO4. The incorporation of graphene effectively enhanced the electrochemical reaction kinetics, highlighting the innovation of our method in developing high-performance cathode materials for lithium-ion batteries. Full article
(This article belongs to the Section Electronic Materials)
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14 pages, 2351 KiB  
Article
Facile SEI Improvement in the Artificial Graphite/LFP Li-Ion System: Via NaPF6 and KPF6 Electrolyte Additives
by Sepehr Rahbariasl and Yverick Rangom
Energies 2025, 18(15), 4058; https://doi.org/10.3390/en18154058 - 31 Jul 2025
Viewed by 325
Abstract
In this work, graphite anodes and lithium iron phosphate (LFP) cathodes are used to examine the effects of sodium hexafluorophosphate (NaPF6) and potassium hexafluorophosphate (KPF6) electrolyte additives on the formation of the solid electrolyte interphase and the performance of [...] Read more.
In this work, graphite anodes and lithium iron phosphate (LFP) cathodes are used to examine the effects of sodium hexafluorophosphate (NaPF6) and potassium hexafluorophosphate (KPF6) electrolyte additives on the formation of the solid electrolyte interphase and the performance of lithium-ion batteries in both half-cell and full-cell designs. The objective is to assess whether these additives may increase cycle performance, decrease irreversible capacity loss, and improve interfacial stability. Compared to the control electrolyte (1.22 M Lithium hexafluorophosphate (LiPF6)), cells with NaPF6 and KPF6 additives produced less SEI products, which decreased irreversible capacity loss and enhanced initial coulombic efficiency. Following the formation of the solid electrolyte interphase, the specific capacity of the control cell was 607 mA·h/g, with 177 mA·h/g irreversible capacity loss. In contrast, irreversible capacity loss was reduced by 38.98% and 37.85% in cells containing KPF6 and NaPF6 additives, respectively. In full cell cycling, a considerable improvement in capacity retention was achieved by adding NaPF6 and KPF6. The electrolyte, including NaPF6, maintained 67.39% greater capacity than the LiPF6 baseline after 20 cycles, whereas the electrolyte with KPF6 demonstrated a 30.43% improvement, indicating the positive impacts of these additions. X-ray photoelectron spectroscopy verified that sodium (Na+) and potassium (K+) ions were present in the SEI of samples containing NaPF6 and KPF6. While K+ did not intercalate in LFP, cyclic voltammetry confirmed that Na+ intercalated into LFP with negligible impact on the energy storage of full cells. These findings demonstrate that NaPF6 and KPF6 are suitable additions for enhancing lithium-ion battery performance in the popular artificial graphite/LFP system. Full article
(This article belongs to the Special Issue Research on Electrolytes Used in Energy Storage Systems)
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12 pages, 1828 KiB  
Article
Preparation of Comb-Shaped Polyether with PDMS and PEG Side Chains and Its Application in Polymer Electrolytes
by Tomoya Enoki, Ryuta Kosono, Nurul Amira Shazwani Zainuddin, Takahiro Uno and Masataka Kubo
Molecules 2025, 30(15), 3201; https://doi.org/10.3390/molecules30153201 - 30 Jul 2025
Viewed by 264
Abstract
Polyethylene oxide (PEO) is the most well-studied polymer used in solid polymer electrolytes (SPEs) for lithium ion batteries (Li-ion batteries). However, ionic conductivity is greatly reduced in the low temperature range due to the crystallization of PEO. Therefore, methods to suppress the crystallization [...] Read more.
Polyethylene oxide (PEO) is the most well-studied polymer used in solid polymer electrolytes (SPEs) for lithium ion batteries (Li-ion batteries). However, ionic conductivity is greatly reduced in the low temperature range due to the crystallization of PEO. Therefore, methods to suppress the crystallization of PEO at room temperature by cross-linking or introducing a branched structure are currently being investigated. In this study, we synthesized new comb-type ion-conducting polyethers with two different side chains such as polydimethylsiloxane (PDMS) and polyethylene glycol monomethyl ether (mPEG) segments as flexible and ion-conducting segments, respectively. The introduction of the PDMS segment was found to prevent a decrease in ionic conductivity in the low-temperature region, but led to an ionic conductivity decrease in the high temperature region. On the other hand, the introduction of mPEG segments improved ionic conductivity in the high-temperature region. The introduction of mPEG segments with longer chains resulted in a significant decrease in ionic conductivity in the low-temperature region. Full article
(This article belongs to the Special Issue Materials for Emerging Electrochemical Devices—2nd Edition)
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22 pages, 16421 KiB  
Article
Deep Neural Network with Anomaly Detection for Single-Cycle Battery Lifetime Prediction
by Junghwan Lee, Longda Wang, Hoseok Jung, Bukyu Lim, Dael Kim, Jiaxin Liu and Jong Lim
Batteries 2025, 11(8), 288; https://doi.org/10.3390/batteries11080288 - 30 Jul 2025
Viewed by 507
Abstract
Large-scale battery datasets often contain anomalous data due to sensor noise, communication errors, and operational inconsistencies, which degrade the accuracy of data-driven prognostics. However, many existing studies overlook the impact of such anomalies or apply filtering heuristically without rigorous benchmarking, which can potentially [...] Read more.
Large-scale battery datasets often contain anomalous data due to sensor noise, communication errors, and operational inconsistencies, which degrade the accuracy of data-driven prognostics. However, many existing studies overlook the impact of such anomalies or apply filtering heuristically without rigorous benchmarking, which can potentially introduce biases into training and evaluation pipelines. This study presents a deep learning framework that integrates autoencoder-based anomaly detection with a residual neural network (ResNet) to achieve state-of-the-art prediction of remaining useful life at the cycle level using only a single-cycle input. The framework systematically filters out anomalous samples using multiple variants of convolutional and sequence-to-sequence autoencoders, thereby enhancing data integrity before optimizing and training the ResNet-based models. Benchmarking against existing deep learning approaches demonstrates a significant performance improvement, with the best model achieving a mean absolute percentage error of 2.85% and a root mean square error of 40.87 cycles, surpassing prior studies. These results indicate that autoencoder-based anomaly filtering significantly enhances prediction accuracy, reinforcing the importance of systematic anomaly detection in battery prognostics. The proposed method provides a scalable and interpretable solution for intelligent battery management in electric vehicles and energy storage systems. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Battery Systems)
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11 pages, 7608 KiB  
Article
A Theoretical Raman Spectra Analysis of the Effect of the Li2S and Li3PS4 Content on the Interface Formation Between (110)Li2S and (100)β-Li3PS4
by Naiara Leticia Marana, Eleonora Ascrizzi, Fabrizio Silveri, Mauro Francesco Sgroi, Lorenzo Maschio and Anna Maria Ferrari
Materials 2025, 18(15), 3515; https://doi.org/10.3390/ma18153515 - 26 Jul 2025
Viewed by 365
Abstract
In this study, we perform density functional theory (DFT) simulations to investigate the Raman spectra of the bulk and surface phases of β-Li3PS4 (LPS) and Li2S, as well as their interfaces at varying compositional ratios. This analysis is [...] Read more.
In this study, we perform density functional theory (DFT) simulations to investigate the Raman spectra of the bulk and surface phases of β-Li3PS4 (LPS) and Li2S, as well as their interfaces at varying compositional ratios. This analysis is relevant given the widespread application of these materials in Li–S solid-state batteries, where Li2S functions not only as a cathode material but also as a protective layer for the lithium anode. Understanding the interfacial structure and how compositional variations influence its chemical and mechanical stability is therefore crucial. Our results demonstrate that the LPS/Li2S interface remains stable regardless of the compositional ratio. However, when the content of both materials is low, the Raman-active vibrational mode associated with the [PS4]3− tetrahedral cluster dominates the interface spectrum, effectively obscuring the characteristic peaks of Li2S and other interfacial features. Only when sufficient amounts of both LPS and Li2S are present does the coupling between their vibrational modes become sufficiently pronounced to alter the Raman profile and reveal distinct interfacial fingerprints. Full article
(This article belongs to the Section Advanced Materials Characterization)
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42 pages, 10454 KiB  
Article
State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms
by Romel Carrera, Leonidas Quiroz, Cesar Guevara and Patricia Acosta-Vargas
Sensors 2025, 25(15), 4632; https://doi.org/10.3390/s25154632 - 26 Jul 2025
Viewed by 476
Abstract
This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under [...] Read more.
This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter α. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. Full article
(This article belongs to the Section Electronic Sensors)
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18 pages, 5066 KiB  
Article
Influence of Pulse Duration on Cutting-Edge Quality and Electrochemical Performance of Lithium Metal Anodes
by Lars O. Schmidt, Houssin Wehbe, Sven Hartwig and Maja W. Kandula
Batteries 2025, 11(8), 286; https://doi.org/10.3390/batteries11080286 - 26 Jul 2025
Viewed by 302
Abstract
Lithium metal is a promising anode material for next-generation batteries due to its high specific capacity and low density. However, conventional mechanical processing methods are unsuitable due to lithium’s high reactivity and adhesion. Laser cutting offers a non-contact alternative, but photothermal effects can [...] Read more.
Lithium metal is a promising anode material for next-generation batteries due to its high specific capacity and low density. However, conventional mechanical processing methods are unsuitable due to lithium’s high reactivity and adhesion. Laser cutting offers a non-contact alternative, but photothermal effects can negatively impact the cutting quality and electrochemical performance. This study investigates the influence of pulse duration on the cutting-edge characteristics and electrochemical behavior of laser-cut 20 µm lithium metal on 10 µm copper foils using nanosecond and picosecond laser systems. It was demonstrated that shorter pulse durations significantly reduce the heat-affected zone (HAZ), resulting in improved cutting quality. Electrochemical tests in symmetric Li|Li cells revealed that laser-cut electrodes exhibit enhanced cycling stability compared with mechanically separated anodes, despite the presence of localized dead lithium “reservoirs”. While the overall pulse duration did not show a direct impact on ionic resistance, the characteristics of the cutting edge, particularly the extent of the HAZ, were found to influence the electrochemical performance. Full article
(This article belongs to the Section Battery Processing, Manufacturing and Recycling)
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22 pages, 4225 KiB  
Article
One-Dimensional Simulation of Real-World Battery Degradation Using Battery State Estimation and Vehicle System Models
by Yuya Hato, Wei-hsiang Yang, Toshio Hirota, Yushi Kamiya and Kiyotaka Sato
World Electr. Veh. J. 2025, 16(8), 420; https://doi.org/10.3390/wevj16080420 - 25 Jul 2025
Viewed by 266
Abstract
This study aims to develop a method for analyzing real-world battery degradation in electric vehicles in order to identify the optimal battery management system (BMS) during the early digital phase of vehicle development. Battery management of lithium-ion batteries (LiBs) in electric vehicles is [...] Read more.
This study aims to develop a method for analyzing real-world battery degradation in electric vehicles in order to identify the optimal battery management system (BMS) during the early digital phase of vehicle development. Battery management of lithium-ion batteries (LiBs) in electric vehicles is important to ensure a stable output and to counteract degradation and thermal runaway. To design the optimal system, it is most effective to use a 1D (one-dimensional) vehicle system simulation model, which connects each unit model inside the vehicle, due to the system’s complexity. In order to create a long-term degradation simulation in a vehicle system model, it is important to reduce computational load. Therefore, in this paper, we studied a suitable battery degradation calculation for the vehicle system model based on an equivalent circuit model (ECM) and degradation approximation formulas. After implementing these models, we analyzed long-term degradation behavior through the real-world operation of an electric vehicle driver. We first implemented a high-accuracy ECM using transient charge–discharge tests and Bayesian Optimization. Next, we formulated approximation formulas for degradation prediction based on calendar and cycle degradation tests. Finally, we simulated real-world degradation behavior using these models. The simulation results revealed that even for users who frequently use electric vehicles, degradation under storage conditions is the dominant factor in overall degradation. Full article
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