Journal Description
Batteries
Batteries
is an international, peer-reviewed, open access journal on battery technology and materials published monthly online by MDPI. International Society for Porous Media (InterPore) is affiliated with Batteries and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Inspec, Ei Compendex, CAPlus / SciFinder, and other databases.
- Journal Rank: JCR - Q2 (Electrochemistry) / CiteScore - Q1 (Electrical and Electronic Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.5 days after submission; acceptance to publication is undertaken in 2.7 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Sections: published in 7 topical sections.
- Journal Cluster of Energy and Fuels: Energies, Batteries, Hydrogen, Biomass, Electricity, Wind, Fuels, Gases, Solar, ESA and Methane.
Impact Factor:
4.8 (2024);
5-Year Impact Factor:
5.2 (2024)
Latest Articles
Condition-Dependent Rate Capability of Laser-Structured Hard Carbon Anodes in Sodium-Based Batteries
Batteries 2025, 11(11), 403; https://doi.org/10.3390/batteries11110403 (registering DOI) - 1 Nov 2025
Abstract
Changing the topography of electrodes by ultrafast laser ablation has shown great potential in enhancing electrochemical performance in lithium-ion batteries. The generation of microstructured channels within the electrodes creates shorter pathways for lithium-ion diffusion and mitigates strain from volume expansion during electrochemical cycling.
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Changing the topography of electrodes by ultrafast laser ablation has shown great potential in enhancing electrochemical performance in lithium-ion batteries. The generation of microstructured channels within the electrodes creates shorter pathways for lithium-ion diffusion and mitigates strain from volume expansion during electrochemical cycling. The topography modification enables faster charging, improved rate capability, and the potential to combine high-power and high-energy properties. In this study, we present a preliminary exploration of this approach for sodium-ion battery technology, focusing on the impact of laser-generated channels on hard carbon electrodes in sodium-metal half-cells. The performance was analyzed by employing different conditions, including different electrolytes, separators, and electrodes with varying compaction degrees. To identify key factors contributing to rate capability improvements, we conducted a comparative analysis of laser-structured and unstructured electrodes using methods including scanning electron microscopy, laser-induced breakdown spectroscopy, and electrochemical cycling. Despite being based on a limited sample size, the data reveal promising trends and serve as a basis for further optimization. Our findings suggest that laser structuring can enhance rate capability, particularly under conditions of limited electrolyte wetting or increased electrode density. This highlights the potential of laser structuring to optimize electrode design for next-generation sodium-ion batteries and other post-lithium technologies.
Full article
(This article belongs to the Special Issue Batteries: 10th Anniversary)
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Open AccessReview
A Critical Review of Recent Inorganic Redox Flow Batteries Development from Laboratories to Industrial Applications
by
Chivukula Kalyan Sundar Krishna and Yansong Zhao
Batteries 2025, 11(11), 402; https://doi.org/10.3390/batteries11110402 (registering DOI) - 1 Nov 2025
Abstract
Redox flow batteries (RFBs) are an emerging class of large-scale energy storage devices, yet the commercial benchmark—vanadium redox flow batteries (VRFBs)—is highly constrained by a modest open-circuit potential (1.26 V) while posing an expensive and volatile material procurement costs. This review focuses on
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Redox flow batteries (RFBs) are an emerging class of large-scale energy storage devices, yet the commercial benchmark—vanadium redox flow batteries (VRFBs)—is highly constrained by a modest open-circuit potential (1.26 V) while posing an expensive and volatile material procurement costs. This review focuses on recent progress in diversifying redox-active species to overcome these limits, highlighting chemistries that increase overall cell voltage, energy density, and efficiency while maintaining long cycle life and safety. The study dwells deeper into manganese-based systems (e.g., Mn/Ti, Mn/V, Mn/S, M/Zn) that leverage Mn’s high positive potential while addressing Mn(III) disproportionation reactions; iron-based hybrids (Fe/Cr, Fe/Zn, Fe/Pb, Fe/V, Fe/S, Fe/Cd) that exploit the low cost, and its abundance, along with membrane and electrolyte strategies to prevent the potential issue involving crossover; cerium-anchored catholytes (Ce/Pb, V/Ce, Eu/Ce, Ce/S, Ce/Zn) that deliver high operational voltage by implementing an acid-base media, along with selective zeolite membranes; and halide systems (Zn–I, Zn–Br, Sn–Br, polysulfide–bromine/iodide) that combine fast redox kinetics and high solubility with advances such as carbon-coated membranes, bromine complexation, and ambipolar electrolytes. Across these various families of RFBs, the review highlights the modifications made to the flow-fields, membranes, and electrodes by utilizing a zero-gap serpentine flow field, sulfonated poly(ether ether ketone) (SPEEK) membranes, carbon-modified and zeolite separators, electrolyte additives to enhance the voltage (VE%), and thereby energy (EE%) efficiency, while reducing the overall system cost. These modifications to the existing RFB technology offer a promising alternative to traditional approaches, paving the way for improved performance and widespread adoption of RFB technology in large-scale grid-based energy storage solutions.
Full article
(This article belongs to the Special Issue Batteries: 10th Anniversary)
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Open AccessArticle
Two-Stage Organic Acid Leaching of Industrially Sourced LFP- and NMC-Containing Black Mass
by
Marc Simon Henderson, Chau Chun Beh, Elsayed A. Oraby and Jacques Eksteen
Batteries 2025, 11(11), 401; https://doi.org/10.3390/batteries11110401 (registering DOI) - 31 Oct 2025
Abstract
Over the next 5–10 years, the feedstock to lithium-ion battery recycling facilities will shift from Co- and Ni-rich chemistries to lower-value battery chemistries, such as lithium iron phosphate (LFP). Traditional recycling processes use toxic and corrosive inorganic acids for leaching, generating toxic waste
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Over the next 5–10 years, the feedstock to lithium-ion battery recycling facilities will shift from Co- and Ni-rich chemistries to lower-value battery chemistries, such as lithium iron phosphate (LFP). Traditional recycling processes use toxic and corrosive inorganic acids for leaching, generating toxic waste streams. The low-value feedstocks will be LFP-rich with contamination from lithium cobalt oxide (LCO) and lithium–nickel–manganese–cobalt oxide (NMC) battery chemistries. Overall, the lower-value feedstock coupled with the need to reduce environmentally damaging waste streams requires the development of robust, green leaching processes capable of selectively targeting the LFP and LCO/NMC battery chemistries. This research concluded that a first-stage oxalic acid leach could selectively extract Al, Li, and P from the industrially sourced LFP-rich black mass. When operating at the optimal conditions (0.5 M oxalic acid, 5% solids, pH 0.8, and an agitation speed of 600 rpm), >99% of the Li and P and >97% of the Al were selectively extracted after 2 h, while Mn, Fe, Cu, Ni, and Co extractions were kept relatively low, namely, at 19%, <3%, <1%, 0%, and 0%. This research also explored a second-stage leach to treat the first-stage leach residue using ascorbic acid, citric acid, and glycine. It was concluded that when leaching with glycine (30 g/L glycine, a temperature of 40 °C, an agitation speed of 600 rpm, and 2% solids at pH 9.6), that >97% of the Co, >77% of the Ni, and 41% of the Mn were extracted, while the co-extraction percentages of Cu, Fe, and Al were <27%, <4%, and <2%.
Full article
(This article belongs to the Special Issue Innovative Technologies for Spent Lithium-Ion Batteries Recycling and Recovery)
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Open AccessArticle
Layered Binder-Free C/Si Anodes for Li Ion Batteries
by
Rumen I. Tomov, Dmitry Yarmolich and Vasant Kumar
Batteries 2025, 11(11), 400; https://doi.org/10.3390/batteries11110400 - 30 Oct 2025
Abstract
Novel high-energy, binder-free, and solvent-free carbon–silicon layered composite anodes were manufactured using an industrially scalable Virtual Cathode Deposition (VCD) technique. The deposition process transforms commercial graphite target material into carbon polymorph (CALIB) layers, interposed with silicon layers deposited in situ from a silicon
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Novel high-energy, binder-free, and solvent-free carbon–silicon layered composite anodes were manufactured using an industrially scalable Virtual Cathode Deposition (VCD) technique. The deposition process transforms commercial graphite target material into carbon polymorph (CALIB) layers, interposed with silicon layers deposited in situ from a silicon source, thereby forming high-capacity anodes for Li ion batteries. Composite CALIB-C/Si4 anodes with a layered architecture exhibited a first-cycle specific capacity of over 1550 mAh g−1 at 0.1 A g−1 and retained a capacity of ~1080 mAh g−1 at a 1 A g−1 rate after 200 cycles. Detailed structural characterisation revealed a disordered carbon matrix encompassing nanosized sp2-bonded carbon clusters (average size ~20 nm), cross-linked by a network of sp3-bonded atomic sites, with predominant mesoporosity and high surface area. The silicon layers were found to consist of an amorphous Si matrix with embedded nanocrystalline components, emulating the growth mode of the CALIB buffer. The presence of the mesoporous carbon matrix accommodated the stress caused by the alloying/de-alloying of silicon nanolayers, thereby alleviating the pulverisation effect and preserving the structural integrity of the composite. The initial performance and capacity decay of the anodes were found to depend on the thickness of the CALIB-C buffer interlayers.
Full article
(This article belongs to the Special Issue Advanced Electrode Materials and Electrolytes for Next-Generation Rechargeable Batteries)
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Open AccessReview
Recent Advances in Electrolyte Engineering for Silicon Anodes
by
Chenduan Xie, Tianyang Hong, Xiaoqin Yi, Di Liu, Xianting Zhao, Yunlin Zhu and Xianhui Zhang
Batteries 2025, 11(11), 399; https://doi.org/10.3390/batteries11110399 - 29 Oct 2025
Abstract
Silicon (Si) anodes offer ultrahigh theoretical capacity (~4200 mAh g−1) for next-generation lithium-ion batteries but suffer from severe mechanical degradation due to repetitive volume expansion (>300%). Conventional electrode-centric strategies face scalability limitations, shifting focus to electrolyte engineering as a critical solution.
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Silicon (Si) anodes offer ultrahigh theoretical capacity (~4200 mAh g−1) for next-generation lithium-ion batteries but suffer from severe mechanical degradation due to repetitive volume expansion (>300%). Conventional electrode-centric strategies face scalability limitations, shifting focus to electrolyte engineering as a critical solution. This review synthesizes recent advances in liquid electrolyte design for stabilizing Si anodes, emphasizing three key pillars: (i) Lithium salts that enable anion-derived inorganic-rich solid electrolyte interphase (SEI) layers with high fracture toughness; (ii) Solvent systems including carbonates, ethers, and phosphonates, where fluorination and steric hindrance tailor SEI elasticity; (iii) Functional additives (F/B/Si-containing) that form mechanically compliant interphases and scavenge detrimental species. Innovative architectures—high-concentration electrolytes (HCEs), localized HCEs (LHCEs), and weakly solvating electrolytes—are critically assessed for their ability to decouple ion transport from volume strain. The perspective highlights the imperative of hybrid solid–liquid interfaces to enable commercially viable Si anodes.
Full article
(This article belongs to the Special Issue Advanced Electrolytes for Enhancing Performance in Lithium/Sodium-Ion Batteries)
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Open AccessArticle
Thermal Runaway Propagation in Pouch-Type Lithium-Ion Battery Modules: Effects of State of Charge and Initiation Location
by
So-Jin Kim, Yeong-Seok Yu, Chan-Seok Jeong, Sang-Bum Lee and Yong-Un Na
Batteries 2025, 11(11), 398; https://doi.org/10.3390/batteries11110398 - 28 Oct 2025
Abstract
The widespread adoption of lithium-ion batteries (LIBs) in electric vehicles (EVs) and energy-storage systems (ESSs) has raised growing concern about fire hazards caused by thermal runaway (TR). While many studies have examined cell-level TR mechanisms, investigations at the module level remain limited despite
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The widespread adoption of lithium-ion batteries (LIBs) in electric vehicles (EVs) and energy-storage systems (ESSs) has raised growing concern about fire hazards caused by thermal runaway (TR). While many studies have examined cell-level TR mechanisms, investigations at the module level remain limited despite their importance for safety design. In this study, TR propagation was experimentally analyzed in a 12-cell (2p6s) pouch-type LIB module with EV-grade cells. The state of charge (SOC) and initiation location were the main variables. TR was initiated by a surface-mounted Kapton heating film, with power increased stepwise from 63 W to 141 W at 5-min intervals. Temperature, voltage, and heat release rate (HRR) were continuously monitored. Results showed that higher SOC led to earlier TR onset, shorter vent-to-ignition delay, and stronger combustion with jet flames. Center initiation produced rapid bidirectional propagation with a peak heat release rate (PHRR) of 590 kW and a propagation time of 107 s, whereas edge initiation caused slower unidirectional spread with a PHRR of 105 kW and a propagation time of 338 s. These results demonstrate that both SOC and initiation location critically control TR severity and propagation, providing essential data for EV fire safety evaluation and module design.
Full article
(This article belongs to the Special Issue Advanced Battery Safety Technologies: From Materials to Systems)
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Open AccessArticle
Numerical Investigation into 18650 Li-Ion Battery Temperature Control Applying Immersion Cooling with FC-40 Dielectric Fluid
by
Sara El Afia, Rachid Hidki, Francisco Jurado and Antonio Cano-Ortega
Batteries 2025, 11(11), 397; https://doi.org/10.3390/batteries11110397 - 27 Oct 2025
Abstract
Nowadays, immersion cooling-based battery thermal management systems have demonstrated their effectiveness in controlling the temperature of lithium-ion batteries. While previous scientific research has primarily concentrated on traditional dielectric fluids such as mineral oil, the current research investigates the effectiveness of the dielectric fluid
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Nowadays, immersion cooling-based battery thermal management systems have demonstrated their effectiveness in controlling the temperature of lithium-ion batteries. While previous scientific research has primarily concentrated on traditional dielectric fluids such as mineral oil, the current research investigates the effectiveness of the dielectric fluid FC-40. A three-dimensional Computational Fluid Dynamics model of an eight-cell 18650 battery system was constructed using ANSYS Fluent 19.2 to examine the effect of cooling fluids (air, mineral oil, and FC-40), velocity of flow (0.01 m/s to 0.15 m/s), discharge rate (1C to 5C), and inlet/outlet size (2.5 mm to 3.5 mm) on thermal efficiency as well as pressure drop. The findings indicate that employing FC-40 as the dielectric fluid significantly reduces the peak cell temperature, with an absolute decrease of 2.80 °C compared to mineral oil and 15.10 °C compared to air. Furthermore, FC-40 achieves the highest uniformity with minimal hotspot. On the other hand, as the fluid velocity increases, the maximum temperature of the battery drops, reaching a minimum of 26 °C at a velocity of 0.15 m/s. Otherwise, at lower flow velocities, the pressure drop remains minimal, thereby reducing the pumping power consumption. Additionally, increasing the inlet and outlet diameter of the fluid directly improves cooling uniformity. Consequently, the temperature dropped by up to 4.3%. Finally, the findings demonstrate that elevated discharge rates contribute to increased heat dissipation but adversely affect the efficiency of the thermal management system. This study provides critical knowledge for the enhancement of battery thermal management systems based on immersion cooling using FC-40 as a dielectric.
Full article
(This article belongs to the Special Issue Thermal Safety of Lithium Ion Batteries—2nd Edition)
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Open AccessArticle
Research on Aging Evolution and Safety Characteristics of Lithium-Ion Batteries Cycling at Low Temperature
by
Ruiheng Wang and Bing Xue
Batteries 2025, 11(11), 396; https://doi.org/10.3390/batteries11110396 - 27 Oct 2025
Abstract
Complex operating conditions, such as low temperature, can affect the degradation and safety stability of lithium-ion batteries (LIBs). This paper conducts research on the aging evolution and safety characteristics of LIBs under low-temperature conditions (−20 °C), to reveal the change laws of battery
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Complex operating conditions, such as low temperature, can affect the degradation and safety stability of lithium-ion batteries (LIBs). This paper conducts research on the aging evolution and safety characteristics of LIBs under low-temperature conditions (−20 °C), to reveal the change laws of battery degradation and the trends of thermal parameters of aging LIBs. Cycling and charging/discharging experiments under low temperatures were conducted to collect realistic battery data. Various factors such as temperature, cycle number, charging/discharging rate, and depth of discharge/charge (DOD/DOC) are taken into consideration to test the battery cycling and thermal performance. With collected experimental results, basic electrical states of LIBs such as open-circuit voltage (OCV), internal resistance, and capacity are presented. Then, the capacity loss and internal resistance growth are also described and analyzed under various charge/discharge rates and DODs/DOCs. The experimental results show that low temperatures cause an almost 30% increase in polarization resistance, with nonlinear changes in total internal resistance. Moreover, the battery capacity and internal resistance also have extreme points with different charge/discharge rates under −20 °C, which may demonstrate that the charge/discharge rates of LIBs can be optimized under low temperature. Thermal runaway (TR) experiments were also conducted, and the self-heating rate and other indices are presented to show that an aging battery under low temperature still holds large energy to develop TR. The aging trends of LIBs under low temperatures are summarized, and battery safety is clarified to provide a reference for battery lifetime and safety management under low-temperature conditions.
Full article
(This article belongs to the Special Issue Physics-Informed Artificial Intelligence for Battery Energy Storage Systems)
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Open AccessReview
Control Algorithms for Ultracapacitors Integrated in Hybrid Energy Storage Systems of Electric Vehicles’ Powertrains: A Mini Review
by
Florin Mariasiu
Batteries 2025, 11(11), 395; https://doi.org/10.3390/batteries11110395 - 26 Oct 2025
Abstract
The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends
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The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends on the implementation of sophisticated control algorithms. From fundamental rule-based systems to advanced predictive and intelligent control strategies, the evolution and integration of these algorithms are driven by the need to efficiently manage the power flow, optimize energy utilization and ensure the long-term reliability of hybrid energy storage systems. This study briefly presents (in the form of a mini review) the research in this field and the development directions and application of state-of-the-art control algorithms, also highlighting the needs, challenges and future development directions. Based on the analysis made, it is found that from the point of view of performance vs. ease of implementation and computational resource requirements, fuzzy algorithms are the most suitable for HESS control in the case of common applications. However, when the performance requirements of HESSs relate to special and high-tech applications, HESS control will be achieved by using convolutional neural networks. As electric vehicles continue to evolve, the development of more intelligent, adaptive and robust control algorithms will be essential for achieving the full potential of integrating ultracapacitors into electric mobility.
Full article
(This article belongs to the Special Issue Advances in Hybrid Supercapacitors: Materials, Devices, Models, Systems, and Applications)
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Optimal DC Fast-Charging Strategies for Battery Electric Vehicles During Long-Distance Trips
by
David Clar-Garcia, Miguel Fabra-Rodriguez, Hector Campello-Vicente and Emilio Velasco-Sanchez
Batteries 2025, 11(11), 394; https://doi.org/10.3390/batteries11110394 - 24 Oct 2025
Abstract
The rapid adoption of electric vehicles (BEVs) has increased the need to understand how fast-charging strategies influence long-distance travel times under real-world conditions. While most manufacturers specify maximum charging power and standardized driving ranges, these figures often fail to reflect actual highway operation,
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The rapid adoption of electric vehicles (BEVs) has increased the need to understand how fast-charging strategies influence long-distance travel times under real-world conditions. While most manufacturers specify maximum charging power and standardized driving ranges, these figures often fail to reflect actual highway operation, particularly in adverse weather. This study addresses this gap by analyzing the fast-charging behaviour, net battery capacity and highway energy consumption of 62 EVs from different market segments. Charging power curves were obtained experimentally at high-power DC stations, with data recorded through both the charging infrastructure and the vehicles’ battery management systems. Tests were conducted, under optimal conditions, between 10% and 90% state of charge (SoC), with additional sessions performed under both cold and preconditioned battery conditions to show thermal effects on the batteries’ fast-charging capabilities. Real-world highway consumption values were applied to simulate 1000 km journeys at 120 km/h under cold (−10 °C, cabin heating) and mild (23 °C, no AC) weather scenarios. An optimization model was developed to minimize total trip time by adjusting the number and duration of charging stops, including a 5 min detour for each charging session. Results show that the optimal charging cutoff point consistently emerges around 59% SoC, with a typical deviation of 10, regardless of ambient temperature. Charging beyond 70% SoC is generally inefficient unless dictated by charging station availability. The optimal strategy involves increasing the number of shorter stops—typically every 2–3 h of driving—thereby reducing total trip.
Full article
(This article belongs to the Special Issue Advances in Battery Modeling: Models, Charging Strategies, Performance Estimations and Thermal Management)
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Comparative Analysis of ML and DL Models for Data-Driven SOH Estimation of LIBs Under Diverse Temperature and Load Conditions
by
Seyed Saeed Madani, Marie Hébert, Loïc Boulon, Alexandre Lupien-Bédard and François Allard
Batteries 2025, 11(11), 393; https://doi.org/10.3390/batteries11110393 - 24 Oct 2025
Abstract
Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogeneous real-world cells remains limited, often confounded by data leakage and inconsistent validation. Here,
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Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogeneous real-world cells remains limited, often confounded by data leakage and inconsistent validation. Here, we establish a leakage-averse, cross-battery evaluation framework encompassing 32 commercial LIBs (B5–B56) spanning diverse cycling histories and temperatures (≈4 °C, 24 °C, 43 °C). Models ranging from classical regressors to ensemble trees and deep sequence architectures were assessed under blocked 5-fold GroupKFold splits using RMSE, MAE, R2 with confidence intervals, and inference latency. The results reveal distinct stratification among model families. Sequence-based architectures—CNN–LSTM, GRU, and LSTM—consistently achieved the highest accuracy (mean RMSE ≈ 0.006; per-cell R2 up to 0.996), demonstrating strong generalization across regimes. Gradient-boosted ensembles such as LightGBM and CatBoost delivered competitive mid-tier accuracy (RMSE ≈ 0.012–0.015) yet unrivaled computational efficiency (≈0.001–0.003 ms), confirming their suitability for embedded applications. Transformer-based hybrids underperformed, while approximately one-third of cells exhibited elevated errors linked to noise or regime shifts, underscoring the necessity of rigorous evaluation design. Collectively, these findings establish clear deployment guidelines: CNN–LSTM and GRU are recommended where robustness and accuracy are paramount (cloud and edge analytics), while LightGBM and CatBoost offer optimal latency–efficiency trade-offs for embedded controllers. Beyond model choice, the study highlights data curation and leakage-averse validation as critical enablers for transferable and reliable SOH estimation. This benchmarking framework provides a robust foundation for future integration of ML models into real-world battery management systems.
Full article
(This article belongs to the Special Issue Recent Advances in Numerical Modeling and Experimental Validation of Batteries)
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Open AccessArticle
Modelling of Battery Energy Storage Systems Under Real-World Applications and Conditions
by
Achim Kampker, Benedikt Späth, Xiaoxuan Song and Datao Wang
Batteries 2025, 11(11), 392; https://doi.org/10.3390/batteries11110392 - 24 Oct 2025
Abstract
Understanding the degradation behavior of lithium-ion batteries under realistic application conditions is critical for the design and operation of Battery Energy Storage Systems (BESS). This research presents a modular, cell-level simulation framework that integrates electrical, thermal, and aging models to evaluate system performance
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Understanding the degradation behavior of lithium-ion batteries under realistic application conditions is critical for the design and operation of Battery Energy Storage Systems (BESS). This research presents a modular, cell-level simulation framework that integrates electrical, thermal, and aging models to evaluate system performance in representative utility and residential scenarios. The framework is implemented using Python and allows time-series simulations to be performed under different state of charge (SOC), depth of discharge (DOD), C-rate, and ambient temperature conditions. Simulation results reveal that high-SOC windows, deep cycling, and elevated temperatures significantly accelerate capacity fade, with distinct aging behavior observed between residential and utility profiles. In particular, frequency modulation and deep-cycle self-consumption use cases impose more severe aging stress compared to microgrid or medium-cycle conditions. The study provides interpretable degradation metrics and visualizations, enabling targeted aging analysis under different load conditions. The results highlight the importance of thermal effects and cell-level stress variability, offering insights for lifetime-aware BESS control strategies. This framework serves as a practical tool to support the aging-resilient design and operation of grid-connected storage systems.
Full article
(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)
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Open AccessArticle
Expansion Pressure as a Probe for Mechanical Degradation in LiFePO4 Prismatic Batteries
by
Shuaibang Liu, Xue Li, Jinhan Li, Jintao Shi, Xingcun Fan, Zifeng Cong, Xiaolong Feng, Haoteng Li, Wenwei Wang, Jiuchun Jiang and Xiao-Guang Yang
Batteries 2025, 11(11), 391; https://doi.org/10.3390/batteries11110391 - 23 Oct 2025
Abstract
Battery mechanical properties degrade progressively with aging, manifesting as expansion pressure in module-constrained cells. Here, an in situ pressure operating system was developed to replicate the mechanical environment of lithium iron phosphate (LFP) prismatic batteries, enabling long-term monitoring under different loads and temperatures.
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Battery mechanical properties degrade progressively with aging, manifesting as expansion pressure in module-constrained cells. Here, an in situ pressure operating system was developed to replicate the mechanical environment of lithium iron phosphate (LFP) prismatic batteries, enabling long-term monitoring under different loads and temperatures. Coupled with quasi-static compression tests on internal components, stress–strain curves and elasticity moduli were obtained to link microscopic behavior with macroscopic pressure response. Results show that irreversible pressure growth is jointly governed by state of health (SOH) and load: under low-load conditions, irreversible pressure increases nonlinearly with SOH, whereas higher loads yield more linear trends. A multilevel physical model encompassing electrodes, cells, and modules was proposed to explain these behaviors. This model takes into account the influence of external pressure on the modulus of the battery, and indicates that SOH and load influence reversible pressure curves through their effect on modulus. A theoretical method was derived to calculate in-module modulus, confirming its linear correlation with the fluctuation amplitude of reversible pressure. Differential pressure-capacity analysis further demonstrated that characteristic changes in expansion pressure reflect modulus evolution, and deviations from this relationship reveal degradation pathways such as gas generation, solid electrolyte interphase (SEI) growth, or lithium plating. This study establishes pressure signals as mechanistic indicators of modulus evolution and provides a framework for diagnosing mechanical degradation in batteries.
Full article
(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)
Open AccessArticle
Propane Ignition Characteristics in a Pt-Catalyzed Microreactor for SOFC Preheating: A Numerical Study of Catalyst Activity Effects
by
Zhulong Wang, Zhen Wang, Zhifang Miao, Lili Ma, Weiqiang Xu, Zunmin Li, Zhiyuan Yang and Guohe Jiang
Batteries 2025, 11(11), 390; https://doi.org/10.3390/batteries11110390 - 23 Oct 2025
Abstract
Leveraging catalytic microreactors as compact yet powerful thermal sources represents a promising approach to enable rapid and reliable startup of small-scale solid oxide fuel cell (SOFC) systems. In the present study, the homogeneous–heterogeneous (HH) combustion behavior of a propane/air mixture in a Pt-catalyzed
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Leveraging catalytic microreactors as compact yet powerful thermal sources represents a promising approach to enable rapid and reliable startup of small-scale solid oxide fuel cell (SOFC) systems. In the present study, the homogeneous–heterogeneous (HH) combustion behavior of a propane/air mixture in a Pt-catalyzed microreactor is investigated using two-dimensional computational fluid dynamic (CFD) simulations. The catalytic reaction kinetics model is integrated into the general module of ANSYSY Fluent via a user-defined function (UDF) interface. By varying the surface area factor, the ignition characteristics of the propane/air mixture under different catalyst activities are systematically explored. Numerical results reveal that the relative catalyst activity range of 0–2 represents a sensitive region for propane/air ignition characteristics, characterized by a 541 K decrease in ignition temperature and a 50% reduction in ignition delay time. Nevertheless, further increases in relative catalyst activity from 2 to 10, yield a much smaller reduction—64 K in ignition temperature and 6.7 s in ignition delay time—indicating a weakly responsive regime. The relative contribution of the heterogeneous reaction (HTR) to the total heat release decreases with higher feed temperatures but increases with enhanced catalyst activity. Regarding the temporal evolution of HTR contribution, the initiation of homogeneous ignition undermines the dominance of HTR contribution. Irrespective of catalytic activity levels, the relative contributions of the two reaction pathways subsequently undergo dynamic redistribution and ultimately stabilize, reaching an equilibrium state within approximately 10 s. These findings provide critical insights into the role of catalyst activity in propane/air mixture ignition and the interplay between homogeneous and heterogeneous reactions in microscale combustion systems.
Full article
(This article belongs to the Special Issue Challenges, Progress, and Outlook of High-Performance Fuel Cells)
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Open AccessArticle
Electrochemical Impedance Spectroscopy Accuracy and Repeatability Analysis of 10 kWh Automotive Battery Module
by
Manuel Kasper, Arnd Leike, Nawfal Al-Zubaidi R-Smith, Aikaterini Papachristou and Ferry Kienberger
Batteries 2025, 11(11), 389; https://doi.org/10.3390/batteries11110389 - 23 Oct 2025
Abstract
Electrochemical Impedance Spectroscopy (EIS) measurements are highly sensitive to the fixturing, temperature, and state of charge (SoC) of batteries. For 10 kWh automotive battery modules, we show that variations in SoC and temperature introduce significant errors at low-to-medium frequencies (<100 Hz), while improper
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Electrochemical Impedance Spectroscopy (EIS) measurements are highly sensitive to the fixturing, temperature, and state of charge (SoC) of batteries. For 10 kWh automotive battery modules, we show that variations in SoC and temperature introduce significant errors at low-to-medium frequencies (<100 Hz), while improper fixture wiring affects mainly higher-frequency accuracy, with errors up to 100% in the imaginary part at 1 kHz. In addition, we study repeatability across various tester-module configurations. EIS results remain highly consistent (±100 µΩ) across three different modules. Comparing the same module across two different testers, deviations are even lower (±30 µΩ up to 1 kHz). The EIS evolution is studied with respect to the cycle numbers, where a strong correlation of low-frequency impedance features is demonstrated. A new combined quotient feature is introduced and suggested as a reliable and efficient state of health (SoH) indicator, solely based on a model-free and phenomenological approach. The study demonstrates the potential of EIS as a powerful tool for battery module characterization, provided that its requirements and limitations are carefully addressed through well-defined experimental setups. Accurate and repeatable EIS measurements are particularly important for obtaining accurate electrochemical insights, especially in the low-to-mid frequency domain, where impedance variations are most sensitive to battery states and ageing effects.
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(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
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Open AccessArticle
Tailoring Particle Size and Morphology to Enhance Performance and Safety of Glass-Based Battery Separators
by
Philipp Rank, Sebastian Müllner, Thorsten Gerdes and Christina Roth
Batteries 2025, 11(11), 388; https://doi.org/10.3390/batteries11110388 - 22 Oct 2025
Abstract
The thermal characteristics and surface properties of battery separators are commonly modified by the incorporation of inorganic particles into a polymeric matrix material. At present, the particles employed are predominantly of an arbitrary shape. Herein, we demonstrate significantly improved battery safety features using
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The thermal characteristics and surface properties of battery separators are commonly modified by the incorporation of inorganic particles into a polymeric matrix material. At present, the particles employed are predominantly of an arbitrary shape. Herein, we demonstrate significantly improved battery safety features using a glass-based separator consisting of platelet-shaped particles. Glass is selected due to its temperature stability and the freedom of design that it offers when particles are formed directly from the melt. The influence of the particles’ aspect ratio and layer stacking on the electrochemical properties was analyzed, and a parametric study of glass particle layers as function of edge length and thickness was conducted. Particles with an excessively high aspect ratio impede the Li+ diffusion pathway, thereby negatively affecting the performance and stability of the battery cell. Conversely, if the aspect ratio is insufficient, a deterioration in cell performance can be observed, particularly at elevated C-rates, due to the high specific surface area of the particles. Hence, the utilization of particles with a moderate aspect ratio of about 10 and a thickness of around 1 µm is proposed to ensure optimum performance.
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(This article belongs to the Section Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others)
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Open AccessArticle
Fin-Embedded PCM Tubes in BTMS: Heat Transfer Augmentation and Mass Minimization via Multi-Objective Surrogate Optimization
by
Bo Zhu, Yi Zhang and Zhengfeng Yan
Batteries 2025, 11(10), 387; https://doi.org/10.3390/batteries11100387 - 21 Oct 2025
Abstract
The rapid proliferation of electric vehicles (EVs) demands lightweight yet efficient battery thermal management systems (BTMS). The fin-embedded phase-change material energy storage tube (PCM-EST) offers significant potential due to its high thermal energy density and passive operation, but conventional designs face a critical
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The rapid proliferation of electric vehicles (EVs) demands lightweight yet efficient battery thermal management systems (BTMS). The fin-embedded phase-change material energy storage tube (PCM-EST) offers significant potential due to its high thermal energy density and passive operation, but conventional designs face a critical trade-off: enhancing heat transfer typically increases mass, conflicting with EV lightweight requirements. To resolve this conflict, this study proposes a multi-objective surrogate optimization framework integrating computational fluid dynamics (CFD) and Kriging modeling. Fin geometric parameters—number, height, and tube length—were rigorously analyzed via ANSYS (2020 R1) Fluent simulations to quantify their coupled effects on PCM melting/solidification dynamics and structural mass. The results reveal that fin configurations dominate both thermal behavior and weight. An enhanced multi-objective particle swarm optimization (MOPSO) algorithm was then deployed to simultaneously maximize heat transfer and minimize mass, generating a Pareto-optimal solution. The optimized design achieves 8.7% enhancement in heat exchange capability and 0.732 kg mass reduction—outperforming conventional single-parameter designs by 37% in weight savings. This work establishes a systematic methodology for synergistic thermal-structural optimization, advancing high-performance BTMS for sustainable EVs.
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(This article belongs to the Special Issue Advanced Battery Safety Technologies: From Materials to Systems)
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Open AccessArticle
Modelling of Thermal Runaway Propagation in Li-Ion Battery Cells Considering Variations in Thermal Property Measurements
by
Hayato Kitagawa, Yoichi Takagishi, Masato Nishiuchi, Koichi Saeki, Ryohei Baba and Tatsuya Yamaue
Batteries 2025, 11(10), 386; https://doi.org/10.3390/batteries11100386 - 21 Oct 2025
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Physics-based simulations of lithium-ion battery thermal runaway (TR) and thermal propagation (TP) enable the assessment of diverse temperature behaviors among individual cells. These behaviors are primarily driven by variations in thermal properties and the amount of heat released during thermal decomposition. However, given
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Physics-based simulations of lithium-ion battery thermal runaway (TR) and thermal propagation (TP) enable the assessment of diverse temperature behaviors among individual cells. These behaviors are primarily driven by variations in thermal properties and the amount of heat released during thermal decomposition. However, given the inherent variability in thermal property measurements, the specific values adopted can lead to substantial differences in predicted temperature behavior. In this study, we developed a 1-dimensional TP model for an array of three prismatic lithium-ion battery cells, in consideration of the uncertainty of key thermal parameters including specific heat, thermal conductivity, activation energy, and the latent heat of the thermal decomposition reaction. The validity of the model and the identification of calibration parameters are ensured through comparison with experimentally measured temperatures. We evaluated the influence of these parameter variations on the temperature and thermal runaway behavior of each cell. Our findings indicate that the variation in thermal runaway timing increases with distance from the trigger cell, and the probability of thermal runaway in the end cell was significantly higher than in the center cell. A sensitivity analysis using a surrogate model revealed that cell temperature is more sensitive to variations in thermal conductivity and latent heat than to variations in specific heat and activation energy.
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Remaining Useful Life Estimation of Lithium-Ion Batteries Using Alpha Evolutionary Algorithm-Optimized Deep Learning
by
Fei Li, Danfeng Yang, Jinghan Li, Shuzhen Wang, Chao Wu, Mingwei Li, Chuanfeng Li, Pengcheng Han and Huafei Qian
Batteries 2025, 11(10), 385; https://doi.org/10.3390/batteries11100385 - 20 Oct 2025
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The precise prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance for improving energy management efficiency and extending battery lifespan, and it is widely applied in the fields of new energy and electric vehicles. However, accurate RUL prediction
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The precise prediction of the remaining useful life (RUL) of lithium-ion batteries is of great significance for improving energy management efficiency and extending battery lifespan, and it is widely applied in the fields of new energy and electric vehicles. However, accurate RUL prediction still faces significant challenges. Although various methods based on deep learning have been proposed, the performance of their neural networks is strongly correlated with the hyperparameters. To overcome this limitation, this study proposes an innovative approach that combines the Alpha evolutionary (AE) algorithm with a deep learning model. Specifically, this hybrid deep learning architecture consists of convolutional neural network (CNN), time convolutional network (TCN), bidirectional long short-term memory (BiLSTM) and multi-scale attention mechanism, which extracts the spatial features, long-term temporal dependencies, and key degradation information of battery data, respectively. To optimize the model performance, the AE algorithm is introduced to automatically optimize the hyperparameters of the hybrid model, including the number and size of convolutional kernels in CNN, the dilation rate in TCN, the number of units in BiLSTM, and the parameters of the fusion layer in the attention mechanism. Experimental results demonstrate that our method significantly enhances prediction accuracy and model robustness compared to conventional deep learning techniques. This approach not only improves the accuracy and robustness of battery RUL prediction but also provides new ideas for solving the parameter tuning problem of neural networks.
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Open AccessArticle
A Hybrid CNN–LSTM–Attention Mechanism Model for Anomaly Detection in Lithium-Ion Batteries of Electric Bicycles
by
Zhaoyang Sun, Weiming Ye, Yuxin Mao and Yuan Sui
Batteries 2025, 11(10), 384; https://doi.org/10.3390/batteries11100384 - 20 Oct 2025
Abstract
To improve the accuracy and stability of anomaly detection in lithium-ion batteries for electric bicycles, in this study, we propose a hybrid deep learning model that integrates a convolutional neural network (CNN), long short-term memory (LSTM) network, and attention mechanism to extract local
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To improve the accuracy and stability of anomaly detection in lithium-ion batteries for electric bicycles, in this study, we propose a hybrid deep learning model that integrates a convolutional neural network (CNN), long short-term memory (LSTM) network, and attention mechanism to extract local temporal features, capture long-term dependencies, and adaptively focus on key time segments around anomaly occurrences, respectively, thereby achieving a balance between local and global feature modeling. In terms of data preprocessing, separate feature sets are constructed for charging and discharging conditions, and sliding windows combined with min–max normalization are applied to generate model inputs. The model was trained and validated on large-scale real-world battery operation data. The experimental results demonstrate that the proposed method achieves high detection accuracy and robustness in terms of reconstruction error distribution, alarm rate stability, and Top-K anomaly consistency. The method can effectively identify various types of abnormal operating conditions in unlabeled datasets based on unsupervised learning. This study provides a transferable deep learning solution for enhancing the safety monitoring of electric bicycle batteries.
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(This article belongs to the Special Issue State-of-Health Estimation of Batteries)
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