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Batteries, Volume 11, Issue 1 (January 2025) – 38 articles

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26 pages, 9543 KiB  
Article
Design Analysis of 26650 and 18650 LFP Cells for High Power and Low Temperature Use Cases
by Florian Wätzold, Anton Schlösser, Max Leistikow and Julia Kowal
Batteries 2025, 11(1), 38; https://doi.org/10.3390/batteries11010038 - 20 Jan 2025
Viewed by 732
Abstract
This study investigates the design and geometric properties of high-power and low-temperature 18650 and 26650 lithium iron phosphate (LFP) cells. The analysis focuses on the geometry and components’ thicknesses and deriving CAD models for both cell formats. Design variations were observed, even within [...] Read more.
This study investigates the design and geometric properties of high-power and low-temperature 18650 and 26650 lithium iron phosphate (LFP) cells. The analysis focuses on the geometry and components’ thicknesses and deriving CAD models for both cell formats. Design variations were observed, even within cells from the same manufacturer. For instance, one manufacturer’s 26650 cell was not a scaled-up version of their 18650 cell, and no equivalence was found between the designs of high-power and low-temperature cells from the same manufacturer. Thus, modifications are not purely chemistry based. The results also reveal deviations from the literature values for jelly roll component thicknesses, with anode current collectors averaging 61 µm and cathode current collectors averaging 60 µm. Coating thicknesses varied, with anode coatings averaging 32 µm and cathode coatings averaging 52 µm. These variations in current collector and coating thicknesses suggest that both high-power and low-temperature LFP cell designs differ from the typical literature values. Furthermore, a trade-off was observed between low-temperature operation with two-tab designs and high pulse capability with limited minimum operating temperatures. Additionally, smaller particle sizes in anode coatings were associated with lower impedance. Full article
(This article belongs to the Special Issue Battery Manufacturing: Current Status, Challenges, and Opportunities)
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25 pages, 8784 KiB  
Article
Composites Based on Poly(ortho-toluidine) and WS2 Sheets for Applications in the Supercapacitor Field
by Teodora Burlanescu, Ion Smaranda, Andreea Androne, Cristina Stefania Florica, Madalina Cercel, Mirela Paraschiv, Adelina Udrescu, Adam Lőrinczi, Petru Palade, Andrei Galatanu, Catalin Negrila, Elena Matei, Monica Dinescu, Radu Cercel and Mihaela Baibarac
Batteries 2025, 11(1), 37; https://doi.org/10.3390/batteries11010037 - 20 Jan 2025
Viewed by 462
Abstract
In this work, three methods for the synthesis of composites based on poly(ortho-toluidine) (POT) and WS2 are reported: (a) the solid-state interaction (SSI) of POT with WS2 nanoparticles (NPs); (b) the in situ chemical polymerization (ICP) of ortho-toluidine (OT); and (c) [...] Read more.
In this work, three methods for the synthesis of composites based on poly(ortho-toluidine) (POT) and WS2 are reported: (a) the solid-state interaction (SSI) of POT with WS2 nanoparticles (NPs); (b) the in situ chemical polymerization (ICP) of ortho-toluidine (OT); and (c) the electrochemical polymerization (ECP) of OT. The preparation of WS2 sheets was performed by the ball milling of the WS2 NPs followed by ultrasonication in the solvent N,N’-dimethyl formamide. During the synthesis of the POT/WS2 composites by SSI and ICP, an additional exfoliation of the WS2 NPs was reported. In this work, we demonstrated the following: (a) the ICP method leads to POT/WS2 composites, which contain repeating units of POT in the leucoemeraldine salt (LS) state, while (b) the ECP method leads to POT/WS2 composites, which contain repeating units of POT in the emeraldine salt (ES) state. Capacitances equal to 123.5, 465.76, and 751.6 mF cm−2 in the cases of POT-ES/WS2 composites, synthesized by SSI, ICP, and ECP, respectively, were reported. Full article
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11 pages, 5068 KiB  
Article
Modification of Cellulose by Esterification Crosslinking to Manipulate Its Microstructure for Enhanced Sodium Storage in Hard Carbon
by Xingyun Zhang, Yue Hu, Yan Wang, Ming Li, Cuiying Lu, Shixiong Sun and Junwei Lang
Batteries 2025, 11(1), 36; https://doi.org/10.3390/batteries11010036 - 20 Jan 2025
Viewed by 381
Abstract
The active hydroxyl group of cellulose plays a crucial role in regulating the microstructure of cellulose-derived hard carbon, which ultimately affects its sodium storage capacity. Through small-angle X-ray scattering (SAXS) and X-ray atomic pair distribution function (PDF) analysis, we proved that modification of [...] Read more.
The active hydroxyl group of cellulose plays a crucial role in regulating the microstructure of cellulose-derived hard carbon, which ultimately affects its sodium storage capacity. Through small-angle X-ray scattering (SAXS) and X-ray atomic pair distribution function (PDF) analysis, we proved that modification of cellulose by esterification crosslinking can introduce more closed pores into the carbonized hard carbon, which is beneficial for promoting sodium ion storage. Our results demonstrate that by optimizing the conditions used for esterification cross-linking modification, the sodium storage capacity of cellulose-derived hard carbon could be increased from 254 to 348 mAh g−1, with an increase in plateau capacity from 140 to 230 mAh g−1. This study makes a significant contribution towards establishing industrial applications for cellulose-derived hard carbon. Full article
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47 pages, 9815 KiB  
Review
Different Metal–Air Batteries as Range Extenders for the Electric Vehicle Market: A Comparative Study
by Yasmin Shabeer, Seyed Saeed Madani, Satyam Panchal, Mahboubeh Mousavi and Michael Fowler
Batteries 2025, 11(1), 35; https://doi.org/10.3390/batteries11010035 - 20 Jan 2025
Viewed by 937
Abstract
Metal–air batteries represent a category of energy storage system that leverages the reaction between metal and oxygen from the atmosphere to produce electricity. These batteries, known for their high energy density, have attracted considerable attention as potential solutions for extending the range of [...] Read more.
Metal–air batteries represent a category of energy storage system that leverages the reaction between metal and oxygen from the atmosphere to produce electricity. These batteries, known for their high energy density, have attracted considerable attention as potential solutions for extending the range of electric vehicles. Understanding the capabilities and limitations of metal-air batteries as range extenders is crucial for advancing electric vehicle technology, as these batteries could offer the additional energy needed to overcome current range limitations. This review paper provides a detailed overview of various metal-air battery technologies, delving into their design, functionality, and inherent challenges. By analyzing key theoretical and practical parameters, the study highlights how these factors influence overall battery performance. Additionally, the review addresses critical cost considerations, particularly the relationship between vehicle cost and driving range, uncovering the significant trade-offs involved in adopting metal-air batteries. Through an examination of nearly all the existing metal-air batteries, this paper sheds light on their potential to serve as effective range extenders, thereby facilitating the transition to a cleaner, more sustainable transportation landscape. Full article
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15 pages, 3713 KiB  
Article
Analysis of Aging and Degradation in Lithium Batteries Using Distribution of Relaxation Time
by Muhammad Sohaib, Abdul Shakoor Akram and Woojin Choi
Batteries 2025, 11(1), 34; https://doi.org/10.3390/batteries11010034 - 19 Jan 2025
Viewed by 581
Abstract
In this paper, the deconvolution of Electrochemical Impedance Spectroscopy (EIS) data into the Distribution of Relaxation Times (DRTs) is employed to provide a detailed examination of degradation mechanisms in lithium-ion batteries. Using an nth RC model with Gaussian functions, this study achieves enhanced [...] Read more.
In this paper, the deconvolution of Electrochemical Impedance Spectroscopy (EIS) data into the Distribution of Relaxation Times (DRTs) is employed to provide a detailed examination of degradation mechanisms in lithium-ion batteries. Using an nth RC model with Gaussian functions, this study achieves enhanced separation of overlapping electrochemical processes where Gaussian functions yield smoother transitions and clearer peak identification than conventional piecewise linear functions. The advantages of employing Tikhonov Regularization (TR) with Gaussian functions over Maximum Entropy (ME) and FFT methods are highlighted as this approach provides superior noise resilience, unbiased analysis, and enhanced resolution of critical features. This approach is applied to LIB cell data to identify characteristic peaks of the DRT plot and evaluate their correlation with battery degradation. By observing how these peaks evolve through cycles of battery aging, insights into specific aging mechanisms and performance decline are obtained. This study combines experimental measurements with DRT peak analysis to characterize the impedance distribution within LIBs which enables accelerated detection of degradation pathways and enhances the predictive accuracy for battery life and reliability. This analysis contributes to a refined understanding of LIB degradation behavior, supporting the development of advanced battery management systems designed to improve safety, optimize battery performance, and extend the operational lifespan of LIBs for various applications. Full article
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34 pages, 4187 KiB  
Review
Recycling of Lithium Iron Phosphate (LiFePO4) Batteries from the End Product Quality Perspective
by Deise F. Barbosa de Mattos, Simon Duda and Martina Petranikova
Batteries 2025, 11(1), 33; https://doi.org/10.3390/batteries11010033 - 18 Jan 2025
Viewed by 850
Abstract
As efforts towards greener energy and mobility solutions are constantly increasing, so is the demand for lithium-ion batteries (LIBs). Their growing market implies an increasing generation of hazardous waste, which contains large amounts of electrolyte, which is often corrosive and flammable and releases [...] Read more.
As efforts towards greener energy and mobility solutions are constantly increasing, so is the demand for lithium-ion batteries (LIBs). Their growing market implies an increasing generation of hazardous waste, which contains large amounts of electrolyte, which is often corrosive and flammable and releases toxic gases, and critical raw materials that are indispensable to the renewable energy sector, such as lithium. Therefore, it is crucial that end-of-life LIBs be recycled in a viable way to avoid environmental pollution and to ensure the reuse of valuable materials that would otherwise be lost. Here, we present a critical review of recent developments in the field of LIB recycling with the LiFePO4 (LFP) chemistry, which is one of the fastest-growing fields, especially in the electromobility sector. Most of the recycling methods developed are not applied industrially due to issues such as complexity, cost, or low quality of the recycled product. This last issue is rarely discussed in the literature, which motivated the creation of this review article, with emphasis on the positive electrode recycling by the direct method and on the quality of the resynthesized LFP in terms of electrochemical performance. Full article
(This article belongs to the Section Battery Processing, Manufacturing and Recycling)
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42 pages, 6623 KiB  
Review
State of Charge and State of Health Estimation in Electric Vehicles: Challenges, Approaches and Future Directions
by Babatunde D. Soyoye, Indranil Bhattacharya, Mary Vinolisha Anthony Dhason and Trapa Banik
Batteries 2025, 11(1), 32; https://doi.org/10.3390/batteries11010032 - 17 Jan 2025
Viewed by 888
Abstract
This critical review paper delves into the complex and evolving landscape of the state of health (SOH) and state of charge (SOC) in electric vehicles (EVs), highlighting the pressing need for accurate battery management to enhance safety, efficiency, and longevity. With the global [...] Read more.
This critical review paper delves into the complex and evolving landscape of the state of health (SOH) and state of charge (SOC) in electric vehicles (EVs), highlighting the pressing need for accurate battery management to enhance safety, efficiency, and longevity. With the global shift towards EVs, understanding and improving battery performance has become crucial. The paper systematically explores various SOC estimation techniques, emphasizing their importance akin to that of a fuel gauge in traditional vehicles, and addresses the challenges in accurately determining SOC given the intricate electrochemical nature of batteries. It also discusses the imperative of SOH estimation, a less defined but critical parameter reflecting battery health and longevity. The review presents a comprehensive taxonomy of current SOC estimation methods in EVs, detailing the operation of each type and succinctly discussing the advantages and disadvantages of these methods. Furthermore, it scrutinizes the difficulties in applying different SOC techniques to battery packs, offering insights into the challenges posed by battery aging, temperature variations, and charge–discharge cycles. By examining an array of approaches—from traditional methods such as look-up tables and direct measurements to advanced model-based and data-driven techniques—the paper provides a holistic view of the current state and potential future of battery management systems (BMS) in EVs. It concludes with recommendations and future directions, aiming to bridge the gap for researchers, scientists, and automotive manufacturers in selecting optimal battery management and energy management strategies. Full article
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29 pages, 3624 KiB  
Review
Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies
by Mohamed Ahwiadi and Wilson Wang
Batteries 2025, 11(1), 31; https://doi.org/10.3390/batteries11010031 - 17 Jan 2025
Viewed by 714
Abstract
Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system [...] Read more.
Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system failures. To ensure the reliability and longevity of Li-ion batteries in applications, various methods have been proposed for battery health monitoring and remaining useful life (RUL) prediction. This paper provides a comprehensive review and analysis of the primary approaches employed for battery health monitoring and RUL estimation under the categories of model-based, data-driven, and hybrid methods. Generally speaking, model-based methods use physical or electrochemical models to simulate battery behaviour, which offers valuable insights into the principles that govern battery degradation. Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. Hybrid approaches integrate multiple methods to enhance predictive accuracy by combining the physical insights of model-based methods with the statistical and analytical strengths of data-driven techniques. This paper thoroughly evaluates these methodologies, focusing on recent advancements along with their respective strengths and limitations. By consolidating current findings and highlighting potential pathways for advancement, this review paper serves as a foundational resource for researchers and practitioners working to advance battery health monitoring and RUL prediction methods across both academic and industrial fields. Full article
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20 pages, 6706 KiB  
Article
On the Performance of Portable NiMH Batteries of General Use
by Diego F. Quintero Pulido, Catalin Felix Covrig and Matthias Bruchhausen
Batteries 2025, 11(1), 30; https://doi.org/10.3390/batteries11010030 - 16 Jan 2025
Viewed by 529
Abstract
NiMH batteries are the most used technology of rechargeable batteries sold directly to consumers. Herein, we study the performance of the most common sizes of portable NiMH batteries (AA, AAA, D, C, and 9V). The performance and durability parameters—capacity, charge retention, charge recovery, [...] Read more.
NiMH batteries are the most used technology of rechargeable batteries sold directly to consumers. Herein, we study the performance of the most common sizes of portable NiMH batteries (AA, AAA, D, C, and 9V). The performance and durability parameters—capacity, charge retention, charge recovery, and endurance in cycles—are measured for these types of batteries, according to the standard IEC 61951-2:2017 NiMH batteries. The purpose of this study is to create a basis for setting minimum performance requirements for the parameters in the European Regulation concerning batteries and waste batteries, EU 2023/1542, Annex III, Part B. Results show that the charging time of 16 h could be reduced to 8 h for verifying the rated capacity. The performance of commercial batteries with regard to charge retention, charge recovery, and endurance in cycles is often found to be 25–30% better than required in the relevant IEC standard. Furthermore, we present a short comparative analysis of an application test (IEC 60086-2:2021 “toy”) for portable NiMH batteries with primary batteries. Such data allow comparing the performance of portable NiMH batteries compared to primary batteries in the application test “toy”. Full article
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
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17 pages, 1349 KiB  
Article
Enhancing Mass Transport in Organic Redox Flow Batteries Through Electrode Obstacle Design
by Joseba Martínez-López, Unai Fernández-Gamiz, Eduardo Sánchez-Díez, Aitor Beloki-Arrondo and Íñigo Ortega-Fernández
Batteries 2025, 11(1), 29; https://doi.org/10.3390/batteries11010029 - 16 Jan 2025
Viewed by 498
Abstract
This study examines the impact of incorporating obstacles in the electrode structure of an organic redox flow battery with a flow-through configuration. Two configurations were compared: A control case without obstacles (Case 1) and a modified design with obstacles to enhance mass transport [...] Read more.
This study examines the impact of incorporating obstacles in the electrode structure of an organic redox flow battery with a flow-through configuration. Two configurations were compared: A control case without obstacles (Case 1) and a modified design with obstacles to enhance mass transport and uniformity (Case 2). While Case 1 exhibited marginally higher discharge voltages (average difference of 0.18%) due to reduced hydraulic resistance and lower Ohmic losses, Case 2 demonstrated significant improvements in concentration uniformity, particularly at low state-of-charge (SOC) levels. The obstacle design mitigated local depletion of active species, thereby enhancing limiting current density and improving minimum concentration values across the studied SOC range. However, the introduction of obstacles increased flow resistance and pressure drops, indicating a trade-off between electrochemical performance and pumping energy requirements. Notably, Case 2 performed better at lower flow rates, showcasing its potential to optimize efficiency under varying operating conditions. At higher flow rates, the advantages of Case 2 diminished but remained evident, with better concentration uniformity, higher minimum concentration values, and a 1% average increase in limiting current density. Future research should focus on optimizing obstacle geometry and positioning to further enhance performance. Full article
(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)
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14 pages, 5099 KiB  
Article
S, Se-Codoped Dual Carbon Coating and Se Substitution in Co-Alkoxide-Derived CoS2 Through SeS2 Triggered Selenization for High-Performance Sodium-Ion Batteries
by Kaiqin Li, Yuqi Kang, Chengjiang Deng, Yanfeng Wang, Haocun Ba, Qi An, Xiaoyan Han and Shaozhuan Huang
Batteries 2025, 11(1), 28; https://doi.org/10.3390/batteries11010028 - 15 Jan 2025
Viewed by 487
Abstract
The development of metal sulfides as anodes for sodium-ion batteries (SIBs) is significantly obstructed by the slow kinetics of the electrochemical reactions and the substantial volume changes on the cycling. Herein, we introduce a selenium-substituted cobalt disulfide embedded within a dual carbon–graphene framework [...] Read more.
The development of metal sulfides as anodes for sodium-ion batteries (SIBs) is significantly obstructed by the slow kinetics of the electrochemical reactions and the substantial volume changes on the cycling. Herein, we introduce a selenium-substituted cobalt disulfide embedded within a dual carbon–graphene framework (Se-CoS2/C@rGO) for high-performance SIBs. The Se-CoS2/C@rGO was prepared via a synchronous sulfurization/selenization strategy using Co-alkoxide as the precursor and SeS2 as the source of selenium and sulfur, during which the EG anions are converted in situ to a S, Se codoped carbon scaffold. The dual carbon–graphene matrix not only improves the electronic conductivity but also stabilizes the electrode material effectively. In addition, the Se substitution within the CoS2 lattice further improves the electrical conductivity and promotes the Na+ reaction kinetics. The enhanced intrinsic electronic/ionic conductivity and reinforced structural stability endow the Se-CoS2/C@rGO anode with a high reversible capacity (558.2 mAh g−1 at 0.2 A g−1), superior rate performance (351 mAh g−1 at 20 A g−1), and long cycle life (93.5% capacity retention after 2100 cycles at 1 A g−1). This work provides new insights into the development of stable and reversible anode materials through Se substitution and dual carbon encapsulation. Full article
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16 pages, 3404 KiB  
Article
Unravelling Lithium Interactions in Non-Flammable Gel Polymer Electrolytes: A Density Functional Theory and Molecular Dynamics Study
by Nasser AL-Hamdani, Paula V. Saravia, Javier Luque Di Salvo, Sergio A. Paz and Giorgio De Luca
Batteries 2025, 11(1), 27; https://doi.org/10.3390/batteries11010027 - 14 Jan 2025
Viewed by 576
Abstract
Lithium metal batteries (LiMBs) have emerged as extremely viable options for next-generation energy storage owing to their elevated energy density and improved theoretical specific capacity relative to traditional lithium batteries. However, safety concerns, such as the flammability of organic liquid electrolytes, have limited [...] Read more.
Lithium metal batteries (LiMBs) have emerged as extremely viable options for next-generation energy storage owing to their elevated energy density and improved theoretical specific capacity relative to traditional lithium batteries. However, safety concerns, such as the flammability of organic liquid electrolytes, have limited their extensive application. In the present study, we utilize molecular dynamics and Density Functional Theory based simulations to investigate the Li interactions in gel polymer electrolytes (GPEs), composed of a 3D cross-linked polymer matrix combined with two different non-flammable electrolytes: 1 M lithium hexafluorophosphate (LiPF6) in ethylene carbonate (EC)/dimethyl carbonate (DMC) and 1 M lithium bis(fluorosulfonyl)imide (LiFSI) in trimethyl phosphate (TMP) solvents. The findings derived from radial distribution functions, coordination numbers, and interaction energy calculations indicate that Li⁺ exhibits an affinity with solvent molecules and counter-anions over the functional groups on the polymer matrix, highlighting the preeminent influence of electrolyte components in Li⁺ solvation and transport. Furthermore, the second electrolyte demonstrated enhanced binding energies, implying greater ionic stability and conductivity relative to the first system. These findings offer insights into the Li+ transport mechanism at the molecular scale in the GPE by suggesting that lithium-ion transport does not occur by hopping between polymer functional groups but by diffusion into the solvent/counter anion system. The information provided in the work allows for the improvement of the design of electrolytes in LiMBs to augment both safety and efficiency. Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire)
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27 pages, 4856 KiB  
Article
A Study on the Differences in Optimized Inputs of Various Data-Driven Methods for Battery Capacity Prediction
by Kuo Xin, Fu Jia, Byoungik Choi and Geesoo Lee
Batteries 2025, 11(1), 26; https://doi.org/10.3390/batteries11010026 - 13 Jan 2025
Viewed by 468
Abstract
As lithium-ion batteries become increasingly popular worldwide, accurately determining their capacity is crucial for various devices that rely on them. Numerous data-driven methods have been applied to evaluate battery-related parameters. In the application of these methods, input features play a critical role. Most [...] Read more.
As lithium-ion batteries become increasingly popular worldwide, accurately determining their capacity is crucial for various devices that rely on them. Numerous data-driven methods have been applied to evaluate battery-related parameters. In the application of these methods, input features play a critical role. Most researchers often use the same input features to compare the performance of various neural network models. However, because most models are regarded as black-box models, different methods may show different dependencies on specific features given the inherent differences in their internal structures. And the corresponding optimal inputs of different neural network models should be different. Therefore, comparing the differences in optimized input features for different neural networks is essential. This paper extracts 11 types of lithium battery-related health features, and experiments are conducted on two traditional machine learning networks and three advanced deep learning networks in three aspects of input differences. The experiment aims to systematically evaluate how changes in health feature types, dimensions, and data volume affect the performance of different methods and find the optimal input for each method. The results demonstrate that each network has its own optimal input in the aspects of health feature types, dimensions, and data volume. Moreover, under the premise of obtaining more accurate prediction accuracy, different networks have different requirements for input data. Therefore, in the process of using different types of neural networks for battery capacity prediction, it is very important to determine the type, dimension, and number of input health features according to the structure, category, and actual application requirements of the network. Different inputs will lead to larger differences in results. The optimization degree of mean absolute error (MAE) can be improved by 10–50%, and other indicators can also be optimized to varying degrees. Therefore, it is very important to optimize the network in a targeted manner. Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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15 pages, 4641 KiB  
Article
Investigation of the Suitability of the DTV Method for the Online SoH Estimation of NMC Lithium-Ion Cells in Battery Management Systems
by Jan Neunzling, Philipp Hainke, Hanno Winter, David Henriques, Matthias Fleckenstein and Torsten Markus
Batteries 2025, 11(1), 25; https://doi.org/10.3390/batteries11010025 - 13 Jan 2025
Viewed by 544
Abstract
Investigating the temperature behavior of lithium-ion battery cells has become an important part of today’s research and development. The main reason for this is that the temperature profile of a battery cell changes during aging. By using Differential Thermal Voltammetry (DTV), new possibilities [...] Read more.
Investigating the temperature behavior of lithium-ion battery cells has become an important part of today’s research and development. The main reason for this is that the temperature profile of a battery cell changes during aging. By using Differential Thermal Voltammetry (DTV), new possibilities are opened up, especially since this diagnostic method is designed to work in operando by only requiring voltage and temperature readings. In this study, a batch of NMC-21700 cells were aged in calendar and cyclic manners. After a specified aging cycle was complete, a check-up measurement was performed. During this time, the cycler collected the electrical measuring values, while a negative temperature coefficient thermistor, which was located on the cell, was used to record the temperature fluctuations. The data were then evaluated by using the DTV analysis technique. By comparing the characteristic points of DTV, correlations between the changing curve characteristics and the capacity loss, and therefore the aging of the respective cell, were established. Based on these results, a simple model suitable for online State of Health (SoH) is derived and validated, showing an estimation accuracy of 1.1%. Full article
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
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24 pages, 10391 KiB  
Article
Research on the Thermal Runaway Behavior and Flammability Limits of Sodium-Ion and Lithium-Ion Batteries
by Changbao Qi, Hewu Wang, Minghai Li, Cheng Li, Yalun Li, Chao Shi, Ningning Wei, Yan Wang and Huipeng Zhang
Batteries 2025, 11(1), 24; https://doi.org/10.3390/batteries11010024 - 12 Jan 2025
Viewed by 842
Abstract
Batteries are widely used in energy storage systems (ESS), and thermal runaway in different types of batteries presents varying safety risks. Therefore, comparative research on the thermal runaway behaviors of various batteries is essential. This study investigates the thermal runaway characteristics of sodium-ion [...] Read more.
Batteries are widely used in energy storage systems (ESS), and thermal runaway in different types of batteries presents varying safety risks. Therefore, comparative research on the thermal runaway behaviors of various batteries is essential. This study investigates the thermal runaway characteristics of sodium-ion batteries (NIBs), lithium iron phosphate batteries (LFP), and lithium-ion batteries with NCM523 and NCM622 cathodes. The experiments were conducted in a nitrogen-filled constant-volume sealed chamber. The results show that the critical surface temperatures at the time of thermal runaway are as follows: LFP (346 °C) > NIBs (292 °C) > NCM523 (290 °C) > NCM622 (281 °C), with LFP batteries exhibiting the highest thermal runaway critical temperature. NIBs have the lowest thermal runaway triggering energy (158 kJ), while LFP has the highest (592.8 kJ). During the thermal runaway of all four battery types, the primary gases produced include carbon dioxide, hydrogen, carbon monoxide, methane, ethylene, propylene, and ethane. For NCM622 and NCM523, carbon monoxide is the dominant combustible gas, with volume fractions of 35% and 29%, respectively. In contrast, hydrogen is the main flammable gas for LFP and NIBs, with volume fractions of 44% and 30%, respectively. Among these, NIBs have the lowest lower flammability limit (LFL), indicating the highest explosion risk. The thermal runaway characteristics of 50 Ah batteries provide valuable insights for battery selection and design in energy storage applications. Full article
(This article belongs to the Special Issue Thermal Safety of Lithium Ion Batteries—2nd Edition)
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25 pages, 6206 KiB  
Article
Comparative Study on Environmental Impact of Electric Vehicle Batteries from a Regional and Energy Perspective
by Ruiqi Feng, Wei Guo, Chenjie Zhang, Yuxuan Nie and Jiajing Li
Batteries 2025, 11(1), 23; https://doi.org/10.3390/batteries11010023 - 11 Jan 2025
Viewed by 1147
Abstract
Against the backdrop of the global goal of “carbon neutrality”, the advancement of electric vehicles (EVs) holds substantial importance for diminishing the reliance on fossil fuels, mitigating vehicular emissions, and fostering the transition of the automotive sector towards a sustainable, low-carbon paradigm. The [...] Read more.
Against the backdrop of the global goal of “carbon neutrality”, the advancement of electric vehicles (EVs) holds substantial importance for diminishing the reliance on fossil fuels, mitigating vehicular emissions, and fostering the transition of the automotive sector towards a sustainable, low-carbon paradigm. The wide application of electric vehicles not only reduces the dependence on non-renewable resources such as oil, but also concurrently effectuates a substantial reduction in carbon emissions within the transportation sector. In the realm of electric vehicles, ternary lithium batteries (NCM) and lithium iron phosphate batteries (LFP) are two widely used batteries. This study examines the resource utilization and environmental repercussions associated with the production of 1 kW ternary lithium batteries and lithium iron phosphate batteries, employing a life cycle assessment (LCA) framework. The importance of clean energy in reducing environmental pollution and global warming potential is revealed by introducing five different power generation types and the regional power generation structure in China into the power battery production process. The findings of the investigation indicate that lithium iron phosphate batteries exhibit pronounced superiority in terms of environmental sustainability, while ternary lithium batteries are more advantageous in terms of performance. The mitigation of environmental pollution associated with battery production can be significantly achieved by the holistic integration of clean energy sources and the systematic optimization of manufacturing processes. Specific interventions encompass enhancing the energy efficiency of the production process, incorporating renewable energy sources for power generation, and minimizing the utilization of hazardous materials. By implementing these strategies, the battery sector can advance towards a more environmentally benign and sustainable trajectory. Full article
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17 pages, 2437 KiB  
Article
A State-of-Health Estimation Method of a Lithium-Ion Power Battery for Swapping Stations Based on a Transformer Framework
by Yu Shi, Haicheng Xie, Xinhong Wang, Xiaoming Lu, Jing Wang, Xin Xu, Dingheng Wang and Siyan Chen
Batteries 2025, 11(1), 22; https://doi.org/10.3390/batteries11010022 - 11 Jan 2025
Viewed by 509
Abstract
Against the backdrop of automobile electrification, an increasing number of battery-swapping stations for electric vehicles have been launched to address the issue of slow battery charging under cold temperature conditions. However, due to the separation of the discharging and charging processes for lithium-ion [...] Read more.
Against the backdrop of automobile electrification, an increasing number of battery-swapping stations for electric vehicles have been launched to address the issue of slow battery charging under cold temperature conditions. However, due to the separation of the discharging and charging processes for lithium-ion batteries (LIBs) at swapping stations, and the circulation of batteries across different vehicles and stations, the operating data become fragmented, making it difficult to accurately identify the battery state-of-health (SOH). This study proposes a BiLSTM-Transformer framework that extracts the Constant Voltage Time (CVT) feature using only charging data, enabling the precise estimation of battery capacity degradation. Validation experiments conducted on battery samples under different operating temperatures showed that the model achieved a normalized RMSE of less than 1.6%. In ideal conditions, the normalized RMSE of the estimation reached as low as 0.11%. This model enables SOH estimation without relying on discharge data, contributing to the efficient and safe operation of battery swapping stations. Full article
(This article belongs to the Collection Advances in Battery Energy Storage and Applications)
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19 pages, 982 KiB  
Article
Adaptive Transfer Learning Strategy for Predicting Battery Aging in Electric Vehicles
by Daniela Galatro, Manav Shroff and Cristina H. Amon
Batteries 2025, 11(1), 21; https://doi.org/10.3390/batteries11010021 - 9 Jan 2025
Viewed by 562
Abstract
This work presents an adaptive transfer learning approach for predicting the aging of lithium-ion batteries (LiBs) in electric vehicles using capacity fade as the metric for the battery state of health. The proposed approach includes a similarity-based and adaptive strategy in which selected [...] Read more.
This work presents an adaptive transfer learning approach for predicting the aging of lithium-ion batteries (LiBs) in electric vehicles using capacity fade as the metric for the battery state of health. The proposed approach includes a similarity-based and adaptive strategy in which selected data from an original dataset are transferred to a clean dataset based on the combined/weighted similarity contribution of feature and stress factor similarities and times series similarities. Transfer learning (TL) is then performed by pre-training a model with clean data, with frozen weights and biases to the hidden layer. At the same time, weights and biases toward the output node are recalculated with the target data. The error reduction lies between −0.4% and −8.3% for 20 computational experiments, attesting to the effectiveness and robustness of our adaptive TL approach. Considerations for data structure and representation learning are presented, as well as a workflow to enhance the application of transfer learning for predicting aging in LiBs. Full article
(This article belongs to the Collection Feature Papers in Batteries)
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40 pages, 6548 KiB  
Review
Cell Architecture Design for Fast-Charging Lithium-Ion Batteries in Electric Vehicles
by Firoozeh Yeganehdoust, Anil Kumar Madikere Raghunatha Reddy and Karim Zaghib
Batteries 2025, 11(1), 20; https://doi.org/10.3390/batteries11010020 - 8 Jan 2025
Viewed by 1548
Abstract
This paper reviews the growing demand for and importance of fast and ultra-fast charging in lithium-ion batteries (LIBs) for electric vehicles (EVs). Fast charging is critical to improving EV performance and is crucial in reducing range concerns to make EVs more attractive to [...] Read more.
This paper reviews the growing demand for and importance of fast and ultra-fast charging in lithium-ion batteries (LIBs) for electric vehicles (EVs). Fast charging is critical to improving EV performance and is crucial in reducing range concerns to make EVs more attractive to consumers. We focused on the design aspects of fast- and ultra-fast-charging LIBs at different levels, from internal cell architecture, through cell design, to complete system integration within the vehicle chassis. This paper explores battery internal cell architecture, including how the design of electrodes, electrolytes, and other factors may impact battery performance. Then, we provide a detailed review of different cell format characteristics in cylindrical, prismatic, pouch, and blade shapes. Recent trends, technological advancements in tab design and placement, and shape factors are discussed with a focus on reducing ion transport resistance and enhancing energy density. In addition to cell-level modifications, pack and chassis design must be implemented across aspects such as safety, mechanical integrity, and thermal management. Considering the requirements and challenges of high-power charging systems, we examined how modules, packs, and the vehicle chassis should be adapted to provide fast and ultra-fast charging. In this way, we explored the potential of fast and ultra-fast charging by investigating the required modification of individual cells up to their integration into the EV system through pack and chassis design. Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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16 pages, 5055 KiB  
Article
A Millimeter-Resolution Operando Thermal Image of Prismatic Li-Ion Batteries Using a Distributed Optical Fiber Sensor
by Zhen Guo, Mina Abedi Varnosfaderani, Calum Briggs, Erdogan Guk and James Marco
Batteries 2025, 11(1), 19; https://doi.org/10.3390/batteries11010019 - 8 Jan 2025
Viewed by 531
Abstract
With the demand for energy gravimetric and volumetric density in electrical vehicles, lithium-ion batteries are undergoing a trend toward larger formats, along with maximized cell-to-pack efficiency. Current battery thermal management systems and battery modeling, relying on point measurement (thermocouples/thermistors), face challenges in providing [...] Read more.
With the demand for energy gravimetric and volumetric density in electrical vehicles, lithium-ion batteries are undergoing a trend toward larger formats, along with maximized cell-to-pack efficiency. Current battery thermal management systems and battery modeling, relying on point measurement (thermocouples/thermistors), face challenges in providing comprehensive characterization for larger batteries and extensive monitoring across the pack. Here, we proposed a novel Rayleigh-scattering-based distributed optical fiber sensor to deliver thermal images of a large prismatic cell. Using an optical fiber of 1 mm diameter wrapped around the cell, the optical sensor delivered over 400 unique measurement locations at 3 mm spatial resolution. During a 1.0 C charge, the optical-measured maximum temperature difference was 8.2 °C, while point-like thermocouples, located at the cell front surface and rear surface center, only had a 0.8 °C maximum temperature difference. Moreover, the all-surface-covered optical sensor identified hotspot generation around the vicinity of the tabs, highlighting the essential role of tabs. The maximum temperature on the negative current tab reached 113.9 °C during a 1.5 C discharge, while the hottest spot on the cell surface was only 52.1 °C. This was further validated by the operando thermal image in both the time domain and the spatial domain, facilitating a detailed analysis of the thermal-behavior-like heat generation on the current tabs, transmission through the surface, and dissipation to the cell bottom. Full article
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23 pages, 5299 KiB  
Article
Numerical Simulation of Impact of Different Redox Couples on Flow Characteristics and Electrochemical Performance of Deep Eutectic Solvent Electrolyte Flow Batteries
by Zhiyuan Xiao, Ruiping Zhang, Mengyue Lu, Qiang Ma, Zhuo Li, Huaneng Su, Huanhuan Li and Qian Xu
Batteries 2025, 11(1), 18; https://doi.org/10.3390/batteries11010018 - 7 Jan 2025
Viewed by 507
Abstract
A comprehensive, three-dimensional, macro-scale model was developed to simulate non-aqueous deep eutectic solvent (DES) electrolyte flow batteries. The model’s feasibility was validated by comparing the simulated polarization data with the experimental results. Utilizing this model, the work reported here compared the flow characteristics [...] Read more.
A comprehensive, three-dimensional, macro-scale model was developed to simulate non-aqueous deep eutectic solvent (DES) electrolyte flow batteries. The model’s feasibility was validated by comparing the simulated polarization data with the experimental results. Utilizing this model, the work reported here compared the flow characteristics and electrochemical properties of electrolytes with different redox couples within the porous electrodes of the batteries. Despite variations in the active materials, the distribution of the electrolyte flow rate showed uniformity due to consistent electrode and flow channel designs, indicating that the structural design of electrodes and channels has a more significant impact on electrolyte flow than the physicochemical properties of the electrolytes themselves. This study also highlighted that TEMPO and Quinoxaline DES electrolytes exhibited less flow resistance and more uniform concentration distributions, which helped reduce overpotentials and enhance battery energy efficiency. Furthermore, this research identified that the highest average overpotentials occurred near the membrane for all the redox couples, demonstrating that electrochemical reactions in DES electrolyte flow batteries primarily occur in the region close to the membrane. This finding underscores the importance of optimizing active redox ions transport in electrolytes to enhance electrochemical reactions in the proximal membrane region, which is crucial for improving flow battery performance. Full article
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30 pages, 388 KiB  
Review
Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review
by Daniel H. de la Iglesia, Carlos Chinchilla Corbacho, Jorge Zakour Dib, Vidal Alonso-Secades and Alfonso J. López Rivero
Batteries 2025, 11(1), 17; https://doi.org/10.3390/batteries11010017 - 3 Jan 2025
Viewed by 757
Abstract
This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. Conducted in accordance with PRISMA guidelines and using a novel adaptation of the Downs [...] Read more.
This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. Conducted in accordance with PRISMA guidelines and using a novel adaptation of the Downs and Black (D&B) scale, this study evaluates 89 research papers and provides insights into the evolving landscape of RUL estimation. Our analysis reveals an evolving landscape of methodological approaches, with different techniques showing distinct capabilities in capturing complex degradation patterns in EV batteries. While recent years have seen increased adoption of DL methods, the effectiveness of different approaches varies significantly based on application context and data characteristics. However, we also uncover critical challenges, including a lack of standardized evaluation metrics, prevalent overfitting problems, and limited dataset sizes, that hinder the field’s progress. To address these, we propose a comprehensive set of evaluation metrics and emphasize the need for larger and more diverse datasets. The review introduces an innovative clustering approach that provides a nuanced understanding of research trends and methodological gaps. In addition, we discuss the ethical implications of DL in RUL estimation, addressing concerns about privacy and algorithmic bias. By synthesizing current knowledge, identifying key research directions, and suggesting methodological improvements, this review serves as a central guide for researchers and practitioners in the rapidly evolving field of EV battery management. It not only contributes to the advancement of RUL estimation techniques but also sets a new standard for conducting systematic reviews in technology-driven fields, paving the way for more sustainable and efficient EV technologies. Full article
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13 pages, 4352 KiB  
Article
Magnesium Alginate as an Electrolyte for Magnesium Batteries
by Markus C. Kwakernaak, Lindah K. Kiriinya, Walter J. Legerstee, Winok M. J. Berghmans, Caspar G. T. Hofman and Erik M. Kelder
Batteries 2025, 11(1), 16; https://doi.org/10.3390/batteries11010016 - 3 Jan 2025
Viewed by 440
Abstract
We present magnesium alginate as an aqueous polymer electrolyte for use in magnesium batteries. Alginates are polysaccharides extracted from algae, which form hydrogel materials upon interaction with divalent and trivalent cations. They are renewable, non-toxic, biocompatible materials that are widely used in the [...] Read more.
We present magnesium alginate as an aqueous polymer electrolyte for use in magnesium batteries. Alginates are polysaccharides extracted from algae, which form hydrogel materials upon interaction with divalent and trivalent cations. They are renewable, non-toxic, biocompatible materials that are widely used in the food and pharmaceutical industries. Mg2+ is weakly bound to an alginate polymer, which results in a hydrogel-like material that contains mobile magnesium ions. We propose that this is the ideal situation for an electrolyte that behaves in a similar way as a ‘water-in-salt’ system. Magnesium alginate was successfully synthesized and characterized by FTIR, XRD, and PDF. Ionic conductivity was measured with EIS measurements; a 2 wt% magnesium electrolyte shows a conductivity of 1.8 mS/cm. During conductivity experiments, we noticed the formation of a black layer on magnesium electrodes, which can improve the ionic conductivity between the electrodes. We carefully characterized this layer with XPS and saw that it mainly consists of alginate derivatives. Full article
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13 pages, 890 KiB  
Article
A Reduced-Order Model of Lithium–Sulfur Battery Discharge
by Noushin Haddad and Hosam K. Fathy
Batteries 2025, 11(1), 15; https://doi.org/10.3390/batteries11010015 - 2 Jan 2025
Viewed by 495
Abstract
This paper examines the problem of modeling lithium–sulfur (Li-S) battery discharge dynamics. The importance of this problem stems from the attractive specific energy levels achievable by Li-S batteries, which can be particularly appealing for applications such as aviation electrification. Previous research presents different [...] Read more.
This paper examines the problem of modeling lithium–sulfur (Li-S) battery discharge dynamics. The importance of this problem stems from the attractive specific energy levels achievable by Li-S batteries, which can be particularly appealing for applications such as aviation electrification. Previous research presents different Li-S battery models, including “zero-dimensional” models that neglect diffusion while using the laws of electrochemistry to represent reduction–oxidation (redox) rates. Zero-dimensional models typically succeed in capturing key features of Li-S battery discharge, including the high plateau, low plateau, and dip point visible in the discharge curves of certain Li-S battery chemistries. However, these models’ use of one state variable to represent the mass of each active species tends to furnish high-order models, with many state variables. This increases the computational complexity of model-based estimation and optimal control. The main contribution of this paper is to develop low-order state-space model of Li-S battery discharge. Specifically, the paper starts with a seventh-order zero-dimensional model of Li-S discharge dynamics, analyzes its discharge behavior, constructs phenomenological second- and third-order models capable of replicating this behavior, and parameterizes these models. The proposed models succeed in capturing battery discharge behavior accurately over a wide range of discharge rates. To the best of our knowledge, these are two of the simplest published models capable of doing so. Full article
(This article belongs to the Special Issue Energy-Dense Metal–Sulfur Batteries)
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26 pages, 7805 KiB  
Review
Acoustic Emission Technique for Battery Health Monitoring: Comprehensive Literature Review
by Eliška Sedláčková, Anna Pražanová, Zbyněk Plachý, Nikola Klusoňová, Vaclav Knap and Karel Dušek
Batteries 2025, 11(1), 14; https://doi.org/10.3390/batteries11010014 - 1 Jan 2025
Viewed by 836
Abstract
The rapid adoption of electric vehicles (EVs) has increased the demand for efficient methods to assess the state of health (SoH) of lithium-ion batteries (LIBs). Accurate and prompt evaluations are essential for safety, battery life extension, and performance optimization. While traditional techniques such [...] Read more.
The rapid adoption of electric vehicles (EVs) has increased the demand for efficient methods to assess the state of health (SoH) of lithium-ion batteries (LIBs). Accurate and prompt evaluations are essential for safety, battery life extension, and performance optimization. While traditional techniques such as electrochemical impedance spectroscopy (EIS) are commonly used to monitor battery degradation, acoustic emission (AE) analysis is emerging as a promising complementary method. AE’s sensitivity to mechanical changes within the battery structure offers significant advantages, including speed and non-destructive assessment, enabling evaluations without disassembly. This capability is particularly beneficial for diagnosing second-life batteries and streamlining decision-making regarding the management of used batteries. Moreover, AE enhances diagnostics by facilitating early detection of potential issues, optimizing maintenance, and improving the reliability and longevity of battery systems. Importantly, AE is a non-destructive technique and belongs to the passive method category, as it does not introduce any external energy into the system but instead detects naturally occurring acoustic signals during the battery’s operation. Integrating AE with other analytical techniques can create a comprehensive tool for continuous battery condition monitoring and predictive maintenance, which is crucial in applications where battery reliability is vital, such as in EVs and energy storage systems. This review not only examines the potential of AE techniques in battery health monitoring but also underscores the need for further research and adoption of these techniques, encouraging the academic community and industry professionals to explore and implement these methods. Full article
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15 pages, 3429 KiB  
Article
Classification of the Crystal Structures of Orthosilicate Cathode Materials for Li-Ion Batteries by Artificial Neural Networks
by Mookala Premasudha, Bhumi Reddy Srinivasulu Reddy, Kwon-Koo Cho, Ahn Hyo-Jun, Jae-Kyung Sung and Nagireddy Gari Subba Reddy
Batteries 2025, 11(1), 13; https://doi.org/10.3390/batteries11010013 - 31 Dec 2024
Viewed by 540
Abstract
The crystal structures of orthosilicate cathode materials play a critical role in determining the physical and chemical properties of Li-ion batteries. Accurate predictions of these crystal structures are essential for estimating key properties of cathode materials in battery applications. In this study, we [...] Read more.
The crystal structures of orthosilicate cathode materials play a critical role in determining the physical and chemical properties of Li-ion batteries. Accurate predictions of these crystal structures are essential for estimating key properties of cathode materials in battery applications. In this study, we utilized crystal structure data from density functional theory (DFT) calculations, sourced from the Materials Project, to predict monoclinic and orthorhombic crystal systems in orthosilicate-based cathode-based materials with Li–Si–(Fe, Mn, Co)–O compositions. An artificial neural network (ANN) model with a 6-22-22-22-1 architecture was trained on 85% of the data and tested on the remaining 15%, achieving an impressive accuracy of 97.3%. The model demonstrated strong predictive capability, with only seven misclassifications from 267 datasets, highlighting its robustness and reliability in predicting the crystal structure of orthosilicate cathodes. To enhance interpretability and model reliability, we employed the Index of Relative Importance (IRI) to identify critical features influencing predictions. Additionally, a user-friendly graphical user interface was also developed to facilitate rapid predictions, enabling researchers to explore structural configurations efficiently and accelerating advancements in battery materials research. Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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21 pages, 13217 KiB  
Article
Safety and Reliability Analysis of Reconfigurable Battery Energy Storage System
by Helin Xu, Lin Cheng, Daniyaer Paizulamu and Haoyu Zheng
Batteries 2025, 11(1), 12; https://doi.org/10.3390/batteries11010012 - 30 Dec 2024
Viewed by 503
Abstract
Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) and energy storage systems (ESSs) because of their high energy density, low self-discharge rate, good cycling performance, and environmental friendliness. Nevertheless, with the extensive utilization of LIBs, incidents of fires and explosions resulting [...] Read more.
Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) and energy storage systems (ESSs) because of their high energy density, low self-discharge rate, good cycling performance, and environmental friendliness. Nevertheless, with the extensive utilization of LIBs, incidents of fires and explosions resulting from thermal runaway (TR) have become increasingly prevalent. The resolution of safety concerns associated with LIBs and the reduction in operational risks have become pivotal to the operation and control of ESSs. This paper proposes a model for the TR process of LIBs. By simplifying the modeling of TR reactions, it is possible to calculate the starting temperature of the battery self-heating reaction. Subsequently, this paper puts forth an operational reliability evaluation algorithm for a reconfigurable battery energy storage system (BESS). Finally, this paper develops a control algorithm for reliability improvement, with the objective of ensuring safe and stable control of the ESS. Full article
(This article belongs to the Special Issue High-Safety Lithium-Ion Batteries: Basics, Progress and Challenges)
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17 pages, 7542 KiB  
Article
Electrochemical Impedance Spectroscopy-Based Characterization and Modeling of Lithium-Ion Batteries Based on Frequency Selection
by Yuechan Xiao, Xinrong Huang, Jinhao Meng, Yipu Zhang, Vaclav Knap and Daniel-Ioan Stroe
Batteries 2025, 11(1), 11; https://doi.org/10.3390/batteries11010011 - 29 Dec 2024
Viewed by 701
Abstract
Lithium-ion batteries are commonly employed in electric vehicles due to their efficient energy storage and conversion capabilities. Nevertheless, to ensure reliable and cost-effective operation, their internal states must be continuously monitored. Electrochemical impedance spectroscopy (EIS) is an effective tool for assessing the battery’s [...] Read more.
Lithium-ion batteries are commonly employed in electric vehicles due to their efficient energy storage and conversion capabilities. Nevertheless, to ensure reliable and cost-effective operation, their internal states must be continuously monitored. Electrochemical impedance spectroscopy (EIS) is an effective tool for assessing the battery’s state. Different frequency ranges of EIS correspond to various electrochemical reaction processes. In this study, EIS measurements were conducted at seven temperatures, ranging from −20 °C to 10 °C, and across 21 states of charge (SOCs), spanning from 0% to 100%. A regression model was utilized to examine the unidirectional factorial characteristic impedance relative to temperature and SOC. An analysis of variance (ANOVA) table was created with temperature and SOC as independent variables and the impedance value as the dependent variable. These models accurately capture the behavior of lithium-ion batteries under different conditions. Based on this research, the battery electrochemical processes are better understood. This paper establishes a mathematical expression for a temperature–SOC-based impedance model at specific frequencies, i.e., 1 Hz, 20 Hz, and 3100 Hz. When comparing the models at these three frequencies, it was found that the model fitting accuracy is highest at 20 Hz, making it applicable across a wide range of temperatures and SOCs. Consequently, the accuracy of the impedance model can be enhanced at a specific frequency, simplifying the impedance model and facilitating the development of advanced battery state estimation methods. Full article
(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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12 pages, 3498 KiB  
Article
An Integrated Na2S−Electrocatalyst Nanostructured Cathode for Sodium–Sulfur Batteries at Room Temperature
by Sichang Ma, Yueming Zhu, Yadong Yang, Dongyang Li, Wendong Tan, Ling Gao, Wanwei Zhao, Wenbo Liu, Wenyu Liang and Rui Xu
Batteries 2025, 11(1), 9; https://doi.org/10.3390/batteries11010009 - 27 Dec 2024
Viewed by 541
Abstract
Room-temperature sodium–sulfur (RT Na–S) batteries offer a superior, high-energy-density solution for rechargeable batteries using earth-abundant materials. However, conventional RT Na–S batteries typically use sulfur as the cathode, which suffers from severe volume expansion and requires pairing with a sodium metal anode, raising significant [...] Read more.
Room-temperature sodium–sulfur (RT Na–S) batteries offer a superior, high-energy-density solution for rechargeable batteries using earth-abundant materials. However, conventional RT Na–S batteries typically use sulfur as the cathode, which suffers from severe volume expansion and requires pairing with a sodium metal anode, raising significant safety concerns. Utilizing Na2S as the cathode material addresses these issues, yet challenges such as Na2S’s low conductivity as well as the shuttle effect of polysulfide still hinder RT Na–S battery development. Herein, we present a simple and cost-effective method to fabricate a Na2S–Na6CoS4/Co@C cathode, wherein Na2S nanoparticles are embedded in a conductive carbon matrix and coupled with dual catalysts, Na6CoS4 and Co, generated via the in situ carbothermal reduction of Na2SO4 and CoSO4. This approach creates a three-dimensional porous composite cathode structure that facilitates electrolyte infiltration and forms a continuous conductive network for efficient electron transport. The in situ formed Na6CoS4/Co electrocatalysts, tightly integrated with Na2S, exhibit strong catalytic activity and robust physicochemical stabilization, thereby accelerating redox kinetics and mitigating the polysulfide shuttle effect. As a result, the Na2S–Na6CoS4/Co@C cathode achieves superior capacity retention, demonstrating a discharge capacity of 346 mAh g−1 after 100 cycles. This work highlights an effective strategy for enhancing Na2S cathodes with embedded catalysts, leading to enhanced reaction kinetics and superior cycling stability. Full article
(This article belongs to the Special Issue Energy-Dense Metal–Sulfur Batteries)
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13 pages, 2955 KiB  
Article
Modeling of a Non-Aqueous Redox Flow Battery for Performance and Capacity Fade Analysis
by Mirko D’Adamo, Nicolas Daub, Lluis Trilla, Jose A. Saez-Zamora and Juan Manuel Paz-Garcia
Batteries 2025, 11(1), 8; https://doi.org/10.3390/batteries11010008 - 27 Dec 2024
Viewed by 670
Abstract
This study presents a prototype non-aqueous redox flow battery that advances the capabilities of conventional systems by achieving a wide operational voltage range, high efficiency, and prolonged cycle life. Leveraging the redox pair 10-[2-(2-methoxy ethoxy)ethyl]-10H-phenothiazine and 2-ethylterephthalonitrile, the system delivers a discharge cell [...] Read more.
This study presents a prototype non-aqueous redox flow battery that advances the capabilities of conventional systems by achieving a wide operational voltage range, high efficiency, and prolonged cycle life. Leveraging the redox pair 10-[2-(2-methoxy ethoxy)ethyl]-10H-phenothiazine and 2-ethylterephthalonitrile, the system delivers a discharge cell voltage ranging from approximately 2.25 V to 1.9 V. To address the economic challenges associated with non-aqueous redox flow batteries, this work explores a cost-efficient design using a symmetric cell architecture and a low-cost, porous separator. To evaluate the feasibility and scalability of this approach, a 2D time-transient reactive transport model is developed, integrating Nernst–Planck electroneutrality principles and porous electrode kinetics. The model is optimized and validated against experimental charge/discharge cycles, accurately predicting voltage behavior. Additionally, the study provides crucial insights into the crossover phenomenon, elucidating the transport dynamics and spatial distribution of active species within the cell. This comprehensive framework establishes a robust foundation for future efforts to scale and optimize non-aqueous redox flow batteries for large-scale energy storage applications, bringing them closer to commercial viability. Full article
(This article belongs to the Section Battery Modelling, Simulation, Management and Application)
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