Batteries

17 pages, 4693 KB

Open AccessArticle

From Waste to Cathode: A Comparative Evaluation of Sol–Gel and Co-Precipitation Routes for Closed-Loop Recycling of Lithium-Ion Battery Cathodes

by Alexandra Kosenko, Konstantin Pushnitsa, Pavel Novikov and Anatoliy A. Popovich

Batteries 2025, 11(12), 466; https://doi.org/10.3390/batteries11120466 - 18 Dec 2025

Viewed by 490

Abstract

The exponential growth of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems has amplified the urgent need for sustainable recycling strategies. Conventional pyrometallurgical and hydrometallurgical methods for LIB recycling are energy-intensive, chemically demanding, and fail to preserve the structural integrity of [...] Read more.

The exponential growth of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems has amplified the urgent need for sustainable recycling strategies. Conventional pyrometallurgical and hydrometallurgical methods for LIB recycling are energy-intensive, chemically demanding, and fail to preserve the structural integrity of cath-ode materials. Closed-loop recycling, in contrast, enables the recovery of layered oxides with minimal processing steps, reducing environmental footprint and supporting a circular economy. This study provides a systematic comparison of two regeneration approaches—sol–gel synthesis and hydroxide co-precipitation—for closed-loop recycling of layered NCM (LiNi_xCo_yMn_zO₂) cathode materials recovered from spent LIBs. Spent cells were mechani-cally processed and leached using malic acid to recover Ni, Co, Mn, which were subsequently used to synthesize NCM622 cathode powders. The regenerated materials were characterized using SEM/EDX, XRD, and electrochemical testing in CR2032 coin cells. Both methods successfully produced phase-pure layered oxides with the R-3m structure, with distinct differences in structural ordering and electrochemical behavior. The sol–gel-derived NCM622 displayed higher crystallinity and reduced cation mixing, evidenced by an I(003)/I(104) ratio of 1.896 compared to 1.720 for the co-precipitated sample, and delivered a high initial discharge capacity of 170 mAh/g at 0.1 C. However, it exhibited significant capacity fade, retaining only 60 mAh/g after 40 cycles. In contrast, the co-precipitation route produced hierarchical porous spherical agglomerates that offered superior cycling stability, maintaining ~150 mAh/g after 40 cycles with lower polarization (ΔE_p = 0.16 V). Both materials demonstrated electrochemical performance comparable to commercial NCM. Overall, hydroxide co-precipitation emerged as the most industrially viable method due to scalable processing, compositional robustness, and improved long-term stability of regenerated cathodes. This work highlights the critical influence of synthesis route selection in LIB closed-loop recycling and provides a technological framework for industrial recovery of high-value NCM cathode materials. Full article

(This article belongs to the Special Issue Second Life and Recycling: Perspectives for High-Performance Batteries)

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18 pages, 1484 KB

Open AccessArticle

Insights into Chemo-Mechanical Yielding and Eigenstrains in Lithium-Ion Battery Degradation

by Fatih Uzun

Batteries 2025, 11(12), 465; https://doi.org/10.3390/batteries11120465 - 18 Dec 2025

Viewed by 343

Abstract

In lithium-ion battery electrodes, repeated lithium insertion and extraction generate compositional gradients and volumetric changes that produce evolving stress fields and eigenstrains, accelerating mechanical degradation. While existing diffusion-induced stress models often capture only elastic behavior, they rarely provide a closed-form analytical treatment of [...] Read more.

In lithium-ion battery electrodes, repeated lithium insertion and extraction generate compositional gradients and volumetric changes that produce evolving stress fields and eigenstrains, accelerating mechanical degradation. While existing diffusion-induced stress models often capture only elastic behavior, they rarely provide a closed-form analytical treatment of irreversible deformation or its connection to cyclic degradation. In this work, a transparent analytical framework is developed for a planar electrode that explicitly couples lithium diffusion with elastic-plastic deformation, eigenstrain formation, and fracture-aware stress relaxation. The framework provides a means to quantitatively model the evolution of residual stress gradients, revealing the formation of a damaging tensile state at the electrode surface after delithiation and demonstrating how path-dependent irreversible deformation establishes a degradation memory. A parametric study is used to demonstrate the framework’s capability to clarify the influence of diffusivity and yield strength on residual stress development. This framework, which unifies diffusion, plasticity, and fracture in closed-form mechanical relations, provides new physical insight into the origins of chemo-mechanical degradation and offers a computationally efficient tool for guiding the design of durable next-generation electrode materials where chemo-mechanical strains are moderate. Full article

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24 pages, 9272 KB

Open AccessArticle

Cleaning and Cross-Contamination in Continuous Twin-Screw Extrusion of Battery Slurries

by Kevin Raczka, Furkan Öksüz, Nooshin Galahroudi, Emma Schiessl, Hermann Nirschl and Frank Rhein

Batteries 2025, 11(12), 464; https://doi.org/10.3390/batteries11120464 - 18 Dec 2025

Viewed by 339

Abstract

In the current industry standard of batch processing electrode slurry, manual cleaning processes pose significant challenges due to their labor intensive nature. The long-term objective is to expand the existing mixing process to create an intelligent, autonomous, and continuous slurry production process. This [...] Read more.

In the current industry standard of batch processing electrode slurry, manual cleaning processes pose significant challenges due to their labor intensive nature. The long-term objective is to expand the existing mixing process to create an intelligent, autonomous, and continuous slurry production process. This will result in a reduction in downtime and setup times, as well as an increase in the degree of automation. Additionally, the implementation of complex parameter selection in the mixing process is intended to make it manageable for variable recipes, ensuring efficient, resource-saving process control. This study aims to address this issue by investigating the continuous production of anode slurry and its subsequent cleaning in a laboratory extruder, with a focus on optimizing the cleaning conditions and analyzing the residual slurry. Several samples were taken during the cleaning of the process area and analyzed by UV-Vis spectroscopy, while also quantifying the residual slurry on the screw elements. The effectiveness of the cleaning was evaluated using Sinner’s Circle parameters, i.e., the effects of time, mechanical, chemical and thermal treatment on the effectiveness of the cleaning process are evaluated and discussed. Several detergents were tested, including deionized water, alcohol, and industrial detergents. Deionized water proved to be the most effective in terms of cleaning rate and residual slurry. In addition, higher screw speeds and flow rates improved cleaning efficiency. The effect of temperature was significant, with better cleaning rate results at higher temperatures. This indicates that mechanical and thermal factors play a critical role in improving cleaning kinetics. For a more in-depth knowledge of the resulting cell chemistry, successive cross-contamination of cathode materials in anode half-cells was examined. As a result, an indicator was identified in the first cycle that displays a voltage increase during delithiation with regard to electrochemical properties. Full article

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12 pages, 2912 KB

Open AccessArticle

KI-Assisted MnO₂ Electrocatalysis Enables Low-Charging Voltage, Long-Life Rechargeable Zinc–Air Batteries

by Francesco Biscaglia, Sabrina Di Masi, Marco Milanese, Claudio Mele, Giuseppe Gigli, Arturo De Risi and Luisa De Marco

Batteries 2025, 11(12), 463; https://doi.org/10.3390/batteries11120463 - 16 Dec 2025

Viewed by 536

Abstract

Rechargeable zinc–air batteries (ZABs) are promising candidates for sustainable energy storage owing to their high theoretical energy density, safety, and environmental compatibility. However, their practical application is hindered by sluggish oxygen evolution reaction (OER) kinetics and the high charging voltage required, which reduce [...] Read more.

Rechargeable zinc–air batteries (ZABs) are promising candidates for sustainable energy storage owing to their high theoretical energy density, safety, and environmental compatibility. However, their practical application is hindered by sluggish oxygen evolution reaction (OER) kinetics and the high charging voltage required, which reduce energy efficiency and accelerate electrode degradation. Here, we report for the first time the beneficial role of potassium iodide (KI) as a reaction modifier in ZABs employing manganese dioxide (MnO₂) as a bifunctional catalyst. MnO₂ not only exhibits remarkable electrocatalytic activity toward the oxygen reduction reaction (ORR) but also catalyzes the iodide oxidation reaction (IOR), which proceeds at significantly lower potentials than the OER. As a result, KI-modified MnO₂ ZABs achieve a remarkably low charging voltage of ≈1.8 V and an energy efficiency of 69.9% at 5 mA/cm². Although the IOR is not fully reversible in alkaline media and its effectiveness depends on the iodide concentration in the electrolyte—which may decrease upon repeated discharge–charge cycling—the suppression of electrode degradation enables stable operation for more than 200 charge–discharge cycles. These findings demonstrate the synergistic effect of KI and MnO₂ in enabling an efficient ORR/IOR pathway, providing a sustainable and cost-effective alternative to noble metal catalysts and opening new perspectives for the practical development of high-performance ZABs. Full article

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14 pages, 2719 KB

Open AccessArticle

In Situ Growth of Cross-Linked Ti₂Nb₁₀O₂₉ Nanoparticles on Inner/Outer Surfaces of Carbon Microtubes for High-Efficiency Lithium Storage

by Zhi Nie, Hualin Xiong, Changlong Du, Lei Yu, Lianrui Li, Gengping Wan and Guizhen Wang

Batteries 2025, 11(12), 462; https://doi.org/10.3390/batteries11120462 - 16 Dec 2025

Viewed by 223

Abstract

Improving electronic and ionic transport and the structural stability of electrode materials is essential for the development of advanced lithium-ion batteries. Despite its great potential as a high-power anode, Ti₂Nb₁₀O₂₉ (TNO) still underperforms due to its unsatisfactory electronic [...] Read more.

Improving electronic and ionic transport and the structural stability of electrode materials is essential for the development of advanced lithium-ion batteries. Despite its great potential as a high-power anode, Ti₂Nb₁₀O₂₉ (TNO) still underperforms due to its unsatisfactory electronic and ionic conductivity. Here, a TNO/carbon microtube (TNO@CMT) composite is constructed via an ethanol-assisted solvothermal process and controlled annealing. The hollow carbon framework derived from kapok fibers provides a lightweight conductive skeleton and abundant nucleation sites for uniform TNO growth. By tuning precursor concentration, the interfacial structure and loading are precisely regulated, optimizing electron/ion transport. The optimized TNO@CMT-2 exhibits uniformly dispersed TNO nanoparticles anchored on both inner and outer CMT surfaces, enabling rapid electron transfer, short Li⁺ diffusion paths, and high structural stability. Consequently, it delivers a reversible capacity of 314.9 mAh g⁻¹ at 0.5 C, retains 75.8% capacity after 1000 cycles at 10 C, and maintains 147.96 mAh g⁻¹ at 40 C. Furthermore, the Li⁺ diffusion coefficient of TNO/CMT-2 is 5.4 × 10⁻¹¹ cm² s⁻¹, which is nearly four times higher than that of pure TNO. This work presents a promising approach to designing multi-cation oxide/carbon heterostructures that synergistically enhance charge and ion transport, offering valuable insights for next-generation high-rate lithium-ion batteries. Full article

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18 pages, 3189 KB

Open AccessArticle

A Study on Thermal Performance Enhancement of Mini-Channel Cooling Plates with an Interconnected Design for Li-Ion Battery Cooling

by Armanto P. Simanjuntak, Joohan Bae, Benrico Fredi Simamora and Jae Young Lee

Batteries 2025, 11(12), 461; https://doi.org/10.3390/batteries11120461 - 15 Dec 2025

Viewed by 467

Abstract

The increasing adoption of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and renewable energy systems has heightened the demand for efficient Battery Thermal Management Systems (BTMS). Effective thermal regulation is critical to prevent performance degradation, extend battery lifespan, and mitigate safety risks such [...] Read more.

The increasing adoption of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and renewable energy systems has heightened the demand for efficient Battery Thermal Management Systems (BTMS). Effective thermal regulation is critical to prevent performance degradation, extend battery lifespan, and mitigate safety risks such as thermal runaway. Liquid cooling has become the dominant strategy in commercial EVs due to its superior thermal performance over air cooling. However, optimizing liquid cooling systems remains challenging due to the trade-off between heat transfer efficiency and pressure drop. Recent studies have explored various coolant selection, nanofluid enhancements, and complex channel geometries, an ideal balance remains difficult to achieve. While advanced methods such as topology optimization offer promising performance gains, they often introduce significant modeling and manufacturing complexity. In this study, we propose a practical alternative: an interconnected straight-channel cooling plate that introduces lateral passages to disrupt the thermal boundary layer and enhance mixing. Comparative analysis shows that the design improves temperature uniformity and reduces peak battery temperature, all while maintaining a moderate pressure drop. The proposed configuration offers a scalable and effective solution for next-generation BTMS, particularly in EV applications where thermal performance and manufacturability are both critical. Full article

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

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41 pages, 1635 KB

Open AccessReview

Photoresponsive TiO₂/Graphene Hybrid Electrodes for Dual-Function Supercapacitors with Integrated Environmental Sensing Capabilities

by María C. Cotto, José Ducongé, Francisco Díaz, Iro García, Carlos Neira, Carmen Morant and Francisco Márquez

Batteries 2025, 11(12), 460; https://doi.org/10.3390/batteries11120460 - 15 Dec 2025

Viewed by 497

Abstract

This review critically examines photoresponsive supercapacitors based on TiO₂/graphene hybrids, with a particular focus on their emerging dual role as energy-storage devices and environmental sensors. We first provide a concise overview of the electronic structure of TiO₂ and the key [...] Read more.

This review critically examines photoresponsive supercapacitors based on TiO₂/graphene hybrids, with a particular focus on their emerging dual role as energy-storage devices and environmental sensors. We first provide a concise overview of the electronic structure of TiO₂ and the key attributes of graphene and related nanocarbons that enable efficient charge separation, transport, and interfacial engineering. We then summarize and compare reported device architectures and electrode designs, highlighting how morphology, graphene integration strategies, and illumination conditions govern specific capacitance, cycling stability, rate capability, and light-induced enhancement in performance. Particular attention is given to the underlying mechanisms of photo-induced capacitance enhancement—including photocarrier generation, interfacial polarization, and photodoping—and to how these processes can be exploited to embed sensing functionality in working supercapacitors. We review representative studies in which TiO₂/graphene systems operate as capacitive sensors for humidity, gases, and volatile organic compounds, emphasizing quantitative figures of merit such as sensitivity, response/recovery times, and stability under repeated cycling. Finally, we outline current challenges in materials integration, device reliability, and benchmarking, and propose future research directions toward scalable, multifunctional TiO₂/graphene platforms for self-powered and environmentally aware electronics. This work is intended as a state-of-the-art summary and critical guide for researchers developing next-generation photoresponsive supercapacitors with integrated sensing capability. Full article

(This article belongs to the Special Issue High Energy Density Supercapacitors: Acquisition, Characterization, and Application: 2nd Edition)

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20 pages, 4665 KB

Open AccessArticle

Remaining Useful Life Prediction of Lithium Batteries Based on Transfer Learning and Particle Filter Fusion

by Liping Chen, Xiaolong Liang, Jiyu Ding, Kun Qiu and Hongli Ma

Batteries 2025, 11(12), 459; https://doi.org/10.3390/batteries11120459 - 13 Dec 2025

Viewed by 522

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for anticipating battery failure and enabling effective health management. However, existing RUL prediction methods often suffer from several limitations, including the need for large volumes of training data, significant differences across [...] Read more.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for anticipating battery failure and enabling effective health management. However, existing RUL prediction methods often suffer from several limitations, including the need for large volumes of training data, significant differences across datasets, and insufficient accuracy in long-term forecasting, which hinder their applicability in real world scenarios. To address these challenges, this paper proposes a hybrid model that integrates transfer learning (TL) and particle filtering (PF) with the Mogrifier LSTM (MLSTM) network. Specifically, the model first employs a transfer learning-based Mogrifier LSTM (TL-MLSTM) to perform long-term prediction of battery capacity, thereby enhancing the model’s generalization ability to accommodate RUL prediction under varying operating conditions. Subsequently, the capacity predictions generated by TL-MLSTM are used as observations in the PF algorithm, which iteratively updates the battery state parameters and refines the capacity predictions, thereby further improving accuracy. The proposed model is validated using publicly available datasets comprising multiple types of batteries under various operational conditions. Experimental results demonstrate that the model achieves an average RMSE of 0.0199, MAPE of 0.5803%, MAE of 0.0167 and APE of 11 cycles across multiple test groups. Compared with standalone models or purely data-driven approaches, the proposed method exhibits significant advantages in robustness and accuracy for long-term capacity degradation prediction. Full article

(This article belongs to the Special Issue Physics-Informed Artificial Intelligence for Battery Energy Storage Systems)

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14 pages, 1444 KB

Open AccessArticle

Li-Ion Battery Recycling via High-Intensity Milling Followed by Organic Acid Leaching for Preferential Lithium Extraction

by Brenda Segura-Bailón, Léa Rouquette, Nathália Vieceli, Karolina Bogusz, Cécile Moreau and Martina Petranikova

Batteries 2025, 11(12), 458; https://doi.org/10.3390/batteries11120458 - 12 Dec 2025

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Abstract

Nowadays, spent batteries are considered a secondary and potential resource to meet the growing demand for lithium, a critical element widely used in the manufacturing of electric vehicles. Therefore, this work presents a hydrometallurgical method for extracting lithium from Nickel–Manganese–Cobalt (NMC) batteries. Citric [...] Read more.

Nowadays, spent batteries are considered a secondary and potential resource to meet the growing demand for lithium, a critical element widely used in the manufacturing of electric vehicles. Therefore, this work presents a hydrometallurgical method for extracting lithium from Nickel–Manganese–Cobalt (NMC) batteries. Citric (

C_{6} H_{8} O_{7}

) and oxalic (

C_{2} H_{2} O_{4}

) acids were used as leaching agents, both of which are cataloged as environmentally friendly organic compounds. To comprehend the chemical interactions between citrate (

c i t

), oxalate (

o x

) and metallic ions, a thermodynamic analysis is presented. According to this analysis, both ions were effective in dissolving lithium; however, the experimental studies demonstrated that oxalate ensured a selective process and achieved complete lithium dissolution under the experimental conditions 1 M C₂H₂O₄, 50 g/L, 60 °C, and 60 min, with a mechanically treated sample (milling time 8 min at 1000 rpm). In this process, the other metals present in the sample, such as cobalt, nickel, and manganese, formed insoluble species with oxalate, allowing their recovery in subsequent stages. Therefore, this investigation provides a proficient methodology for battery recycling, emphasizing sustainable practices. Full article

(This article belongs to the Special Issue Selected Papers from Circular Materials Conference 2025)

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20 pages, 2340 KB

Open AccessArticle

Macro Economic and Ecological Aspects of Cell Production in Europe 2030

by Tim Wicke, Lukas Weymann, Christoph Neef and Jens Tübke

Batteries 2025, 11(12), 457; https://doi.org/10.3390/batteries11120457 - 12 Dec 2025

Viewed by 488

Abstract

Factory announcements for battery production are increasing in number as European demand for battery cells grows. Using a Monte Carlo simulation (108 projects as of October 2025) with risk factors for individual projects, the predicted theoretical production capacity for lithium-ion batteries in Europe [...] Read more.

Factory announcements for battery production are increasing in number as European demand for battery cells grows. Using a Monte Carlo simulation (108 projects as of October 2025) with risk factors for individual projects, the predicted theoretical production capacity for lithium-ion batteries in Europe will rise to 1.1–1.5 TWh, enabling a real production output of 0.8–1.0 TWh by 2030. Our analysis suggests necessary cumulative investments in battery cell gigafactories of 36–139 billion euros by 2030. The industrial output of LIB cells in 2030 will have a value of 35–99 billion euros, of which the market size of battery production is around 6–17 billion euros. Furthermore, 43,000–174,000 direct jobs could be created, with the strongest impacts seen in Eastern Europe by the end of the decade. The raw material demand generated by this industry rises steeply: lithium will rise from 14 kt in 2025 to 47–133 kt, and nickel from 83 kt to 226–640 kt by 2030, implying continued import dependencies. The energy demand of European cell production will be 8.4–19.9 TWh in 2030. Furthermore, CO₂ emissions of cell production will be 1.6 to 3.7 Mt CO₂-eq in 2030. The volume of production scrap is estimated at 160–398 kt in 2030, creating near-term demand for recycling capacities. Full article

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

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13 pages, 3460 KB

Open AccessArticle

First-Principles Calculation Study on the Interfacial Stability Between Zr and F Co-Doped Li₆PS₅Cl and Lithium Metal Anode

by Junbo Zhang, Hailong Zhang, Binbin Chen, Yinlian Ji, Caixia Qian, Jue Wang, Yu Wang, Tiantian Bao, Peipei Chen and Jie Mei

Batteries 2025, 11(12), 456; https://doi.org/10.3390/batteries11120456 - 11 Dec 2025

Viewed by 413

Abstract

Li-Argyrodite-type Li₆PS₅Cl solid electrolyte is one of the most extensively investigated and promising materials in the field of all-solid-state batteries. However, its interfacial stability against lithium metal anodes remains challenging. Herein, first-principles calculations were employed to probe the effects [...] Read more.

Li-Argyrodite-type Li₆PS₅Cl solid electrolyte is one of the most extensively investigated and promising materials in the field of all-solid-state batteries. However, its interfacial stability against lithium metal anodes remains challenging. Herein, first-principles calculations were employed to probe the effects of Zr and F co-doping on the interfacial structural characteristics of Li₆P_0.9Zr_0.1S_4.9F_0.1Cl solid electrolytes in contact with lithium metal at the atomic scale. Systematic investigations were conducted on interfacial structural stability, electronic structure, lithium-ion transport properties, and stress–strain properties. Theoretical results demonstrate that the formation energy of sulfur on the lithium metal side in the Zr and F co-doped interface is significantly increased, which stems from the strong bonding interactions of Zr–S and P-F bonds. This effectively suppresses sulfur diffusion toward the lithium metal anode, thereby enhancing the interfacial structural stability. Moreover, Zr and F co-doping simultaneously improves both the lithium-ion migration capability and mechanical stress–strain properties at the interface. The maximum strain at the Li/Li₆PS₅Cl interface increases substantially from 6% to 12% with the implementation of Zr/F co-doping. The Li⁺ migration barrier at the interface exhibits a reduction of 36%. The insights from this study can serve as a design guideline for engineering high-performance solid electrolytes for all-solid-state batteries. Full article

(This article belongs to the Special Issue Design and Optimization of Critical Materials for Lithium or Sodium Batteries)

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3 pages, 131 KB

Open AccessEditorial

Advances in Lithium-Ion Battery Safety and Fire Prevention

by Zhi Wang

Batteries 2025, 11(12), 455; https://doi.org/10.3390/batteries11120455 - 10 Dec 2025

Viewed by 570

Abstract

With the rapid development of new energy technologies, lithium-ion batteries (LIBs) have become one of the core technologies in modern energy storage and electric mobility [...] Full article

(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire)

19 pages, 3215 KB

Open AccessArticle

Thick LiMn₂O₄ Electrode with Polymer Electrolyte for Electrochemical Extraction of Lithium from Brines

by Daiwei Yao, Jing Qin, Hongtan Liu, Mert Akin and Xiangyang Zhou

Batteries 2025, 11(12), 454; https://doi.org/10.3390/batteries11120454 - 10 Dec 2025

Viewed by 321

Abstract

Thick (900–1500 µm), crack-free lithium manganese oxide (LMO) electrodes with a polyvinylidene fluoride (PVDF)-based polymer electrolyte were prepared using an innovated slurry casting method. The selectivity and intercalation capacity of the thick electrodes of 900–1500 μm were evaluated in aqueous chloride solutions containing [...] Read more.

Thick (900–1500 µm), crack-free lithium manganese oxide (LMO) electrodes with a polyvinylidene fluoride (PVDF)-based polymer electrolyte were prepared using an innovated slurry casting method. The selectivity and intercalation capacity of the thick electrodes of 900–1500 μm were evaluated in aqueous chloride solutions containing main cations in synthetic Salar de Atacama brine using cyclic voltammetry (CV) measurements. The CV data indicated that a high Li⁺ selectivity of Li/Na = 152.7 could be achieved under potentiostatic conditions. With the thickest electrode, while the mass specific intercalation capacity was 6.234 mg per gram of LMO, the area specific capacity was increased by 3–11 folds compared to that for conventional thin electrodes to 0.282 mg per square centimeter. In addition, 82% of capacity was retained over 30 intercalation/dis-intercalation cycles. XRD and electrochemical analyses revealed that both Faradaic diffusion-controlled or battery-like intercalation and Faradaic non-diffusion controlled or pseudocapacitive intercalation contributed to the capacity and selectivity. This work demonstrates a practical technology for thick electrode fabrication that promises to result in a significant reduction in manufacturing and operational costs for lithium extraction from brines. Full article

(This article belongs to the Special Issue Solid Polymer Electrolytes for Lithium Batteries and Beyond)

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19 pages, 4401 KB

Open AccessArticle

Research and Structural Optimization of Lithium Battery Heat Dissipation Based on Leaf Vein Channels

by Haiyan Dai, Changyu Li and Jixiang Zhou

Batteries 2025, 11(12), 453; https://doi.org/10.3390/batteries11120453 - 10 Dec 2025

Viewed by 355

Abstract

The operating temperature of lithium batteries directly affects their charge–discharge performance. This study is based on the LF50K prismatic power battery. The battery’s thermal model and the computational fluid dynamics (CFD) control equation were established. After completing the model verification, a thermal management [...] Read more.

The operating temperature of lithium batteries directly affects their charge–discharge performance. This study is based on the LF50K prismatic power battery. The battery’s thermal model and the computational fluid dynamics (CFD) control equation were established. After completing the model verification, a thermal management system with a bionic leaf vein flow channel was designed. The study focused on investigating the effects of varied flow passage configurations, inlet–outlet flow channel angles, flow channel widths, flow rates, leaf vein angles, and inlet–outlet positions on the cooling effect of the lithium battery module. The results show that, as the inlet–outlet angle and width of the bionic leaf vein fluid flow channel increase, the battery cooling effect deteriorates; the increase in the angle and flow channel width has an adverse impact on battery heat dissipation. The significant reduction in the battery’s maximum temperature observed with an elevated fluid flow rate underscores the positive contribution of flow rate to the cooling process. The effect of the leaf vein angle on the cooling of lithium batteries shows a fluctuating trend: when the angle rises from 30° to 45°, the battery’s peak temperature shows a slow upward tendency; conversely, with the angle further increasing from 45° to 80°, the maximum temperature shows a gradual downward tendency. Specifically, at an angle of 45°, Battery No. 5 hits a maximum temperature of 306.58 K (around 33.43 °C), with the maximum temperature difference also reaching 6.38 K. After optimizing the structural parameters, when operating under the maximum ambient temperature conditions in 2024, the maximum temperature of the battery module decreased by 7 K, and the temperature difference decreased by 5.47 K, enabling the battery to achieve optimal operating efficiency. This study lays a foundation for a further optimization of the thermal management system for lithium-ion batteries in subsequent research. Full article

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25 pages, 4711 KB

Open AccessArticle

Hybrid Deep Learning Approach for Fractional-Order Model Parameter Identification of Lithium-Ion Batteries

by Maharani Putri, Dat Nguyen Khanh, Kun-Che Ho, Shun-Chung Wang and Yi-Hua Liu

Batteries 2025, 11(12), 452; https://doi.org/10.3390/batteries11120452 - 9 Dec 2025

Viewed by 424

Abstract

Fractional-order models (FOMs) have been recognized as superior tools for capturing the complex electrochemical dynamics of lithium-ion batteries, outperforming integer-order models in accuracy, robustness, and adaptability. Parameter identification (PI) is essential for FOMs, as its accuracy directly affects the model’s ability to predict [...] Read more.

Fractional-order models (FOMs) have been recognized as superior tools for capturing the complex electrochemical dynamics of lithium-ion batteries, outperforming integer-order models in accuracy, robustness, and adaptability. Parameter identification (PI) is essential for FOMs, as its accuracy directly affects the model’s ability to predict battery behavior and estimate critical states such as state of charge (SOC) and state of health (SOH). In this study, a hybrid deep learning approach has been introduced for FOM PI, representing the first application of deep learning in this domain. A simulation platform was developed to generate datasets using Sobol and Monte Carlo sampling methods. Five deep learning models were constructed: long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (1DCNN), and hybrid models combining 1DCNN with LSTM and GRU. Hyperparameters were optimized using Optuna, and enhancements such as Huber loss for robustness to outliers, stochastic weight averaging, and exponential moving average for training stability were incorporated. The primary contribution lies in the hybrid architectures, which integrate convolutional feature extraction with recurrent temporal modeling, outperforming standalone models. On a test set of 1000 samples, the improved 1DCNN + GRU model achieved an overall root mean square error (RMSE) of 0.2223 and a mean absolute percentage error (MAPE) of 0.27%, representing nearly a 50% reduction in RMSE compared to its baseline. This performance surpasses that of the improved LSTM model, which yielded a MAPE of 9.50%, as evidenced by tighter scatter plot alignments along the diagonal and reduced error dispersion in box plots. Terminal voltage prediction was validated with an average RMSE of 0.002059 and mean absolute error (MAE) of 0.001387, demonstrating high-fidelity dynamic reconstruction. By advancing data-driven PI, this framework is well-positioned to enable real-time integration into battery management systems. Full article

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

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26 pages, 761 KB

Open AccessArticle

In Situ Estimation of Li-Ion Battery State of Health Using On-Board Electrical Measurements for Electromobility Applications

by Jorge E. García Bustos, Benjamín Brito Schiele, Leonardo Baldo, Bruno Masserano, Francisco Jaramillo-Montoya, Diego Troncoso-Kurtovic, Marcos E. Orchard, Aramis Perez and Jorge F. Silva

Batteries 2025, 11(12), 451; https://doi.org/10.3390/batteries11120451 - 9 Dec 2025

Viewed by 444

Abstract

The well-balanced combination of high energy density and competitive cycle performance has established lithium-ion batteries as the technology of choice for Electric Vehicles (EVs) energy storage. Nevertheless, battery degradation continues to pose challenges to EV range, safety, and long-term reliability, making accurate estimation [...] Read more.

The well-balanced combination of high energy density and competitive cycle performance has established lithium-ion batteries as the technology of choice for Electric Vehicles (EVs) energy storage. Nevertheless, battery degradation continues to pose challenges to EV range, safety, and long-term reliability, making accurate estimation of their State of Health (SoH) crucial for efficient battery management, safety, and improved longevity. This paper addresses a compelling research question surrounding the possibility of developing a real-time, non-invasive, and efficient methodology for estimating lithium-ion battery SoH without battery removal, relying solely on voltage and current data. Our approach integrates the fitting abilities of Maximum Likelihood Estimation (MLE) with the dynamic uncertainty propagation of Bayesian Filtering to provide accurate and robust online SoH estimation. By reconstructing the open-circuit voltage curve from real-time data, the MLE estimates battery capacity during discharge cycles, while Bayesian Filtering refines these estimates, accounting for uncertainties and variations. The methodology is validated using an available dataset from Stanford University, demonstrating its effectiveness in tracking battery degradation under driving profiles. The results indicate that the approach can reliably estimate battery SoH with mean absolute errors below 1%, confirming its suitability for scalable EV applications. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends: 2nd Edition)

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54 pages, 8634 KB

Open AccessReview

Comparative Analysis of Cell Design: Form Factor and Electrode Architectures in Advanced Lithium-Ion Batteries

by Khaled Mekdour, Anil Kumar Madikere Raghunatha Reddy, Jeremy I. G. Dawkins, Thiago M. Guimaraes Selva and Karim Zaghib

Batteries 2025, 11(12), 450; https://doi.org/10.3390/batteries11120450 - 9 Dec 2025

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Abstract

This review investigates how cell form factors (cylindrical, prismatic, and pouch) and electrode architecture (jelly-roll, stacked, and blade) influence the performance, safety, and manufacturability of lithium-ion batteries (LIBs) across the main commercial chemistries LiFePO₄ (LFP), Li (NiMnCo)O₂ (NMC), LiNiCoAlO₂ (NCA), [...] Read more.

This review investigates how cell form factors (cylindrical, prismatic, and pouch) and electrode architecture (jelly-roll, stacked, and blade) influence the performance, safety, and manufacturability of lithium-ion batteries (LIBs) across the main commercial chemistries LiFePO₄ (LFP), Li (NiMnCo)O₂ (NMC), LiNiCoAlO₂ (NCA), and LiCoO₂ (LCO). Literature, OEM datasheets, and teardown analyses published between 2015 and 2025 were examined to map the interdependence among geometry, electrode design, and electrochemical behavior. The comparison shows trade-offs among gravimetric and volumetric energy density, thermal runaway tolerance, cycle lifespan, and cell-to-pack integration efficiency. LFP, despite its lower nominal voltage, offers superior thermal stability and a longer cycle life, making it suitable for both prismatic and blade configurations in EVs and stationary storage applications. NMC and NCA chemistries achieve higher specific energy and power by using jelly-roll architectures that are best suited for tabless or multi-tab current collection, enhancing uniform current distribution and manufacturability. Pouch cells provide high energy-to-weight ratios and flexible packaging for compact modules, though they require precise mechanical compression. LCO remains confined to small electronics owing to safety and cost limitations. Although LFP’s safety and affordability make it dominant in cost-sensitive applications, its low voltage and energy density limit broader adoption. LiMnFePO₄ (LMFP) cathodes offer a pathway to enhance voltage and energy while retaining cycle life and cost efficiency; however, their optimization across various form factors and electrode architecture remains underexplored. This study establishes an application-driven framework linking form factors and electrode design to guide the design and optimization of next-generation lithium-ion battery systems. Full article

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

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23 pages, 9482 KB

Open AccessArticle

A Hybrid End-to-End Dual Path Convolutional Residual LSTM Model for Battery SOH Estimation

by Azadeh Gholaminejad, Arta Mohammad-Alikhani and Babak Nahid-Mobarakeh

Batteries 2025, 11(12), 449; https://doi.org/10.3390/batteries11120449 - 6 Dec 2025

Viewed by 443

Abstract

Accurate estimation of battery state of health is essential for ensuring safety, supporting fault diagnosis, and optimizing the lifetime of electric vehicles. This study proposes a compact dual-path architecture that combines Convolutional Neural Networks with Convolutional Long Short-Term Memory (ConvLSTM) units to jointly [...] Read more.

Accurate estimation of battery state of health is essential for ensuring safety, supporting fault diagnosis, and optimizing the lifetime of electric vehicles. This study proposes a compact dual-path architecture that combines Convolutional Neural Networks with Convolutional Long Short-Term Memory (ConvLSTM) units to jointly extract spatial and temporal degradation features from charge-cycle voltage and current measurements. Residual and inter-path connections enhance gradient flow and feature fusion, while a three-channel preprocessing strategy aligns cycle lengths and isolates padded regions, improving learning stability. Operating end-to-end, the model eliminates the need for handcrafted features and does not rely on discharge data or temperature measurements, enabling practical deployment in minimally instrumented environments. The model is evaluated on the NASA battery aging dataset under two scenarios: Same-Battery Evaluation and Leave-One-Battery-Out Cross-Battery Generalization. It achieves average RMSE values of 1.26% and 2.14%, converging within 816 and 395 epochs, respectively. An ablation study demonstrates that the dual-path design, ConvLSTM units, residual shortcuts, inter-path exchange, and preprocessing pipeline each contribute to accuracy, stability, and reduced training cost. With only 4913 parameters, the architecture remains robust to variations in initial capacity, cutoff voltage, and degradation behavior. Edge deployment on an NVIDIA Jetson AGX Orin confirms real-time feasibility, achieving 2.24 ms latency, 8.24 MB memory usage, and 12.9 W active power, supporting use in resource-constrained battery management systems. Full article

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15 pages, 2343 KB

Open AccessArticle

State of Power Prediction for Battery Packs Based on Particle Swarm Optimization SVM

by Hangang Yan, Qingbin Wang, Yun Yang, Xianzhong Zhao, Zudi Huang, Yuxi Wang, Shi Liu, Bin Yi, Gancai Huang and Jianfeng Yang

Batteries 2025, 11(12), 448; https://doi.org/10.3390/batteries11120448 - 5 Dec 2025

Viewed by 393

Abstract

Lithium-ion batteries are widely used in energy storage systems and consumer electronics; however, long-term usage leads to capacity degradation, which affects system efficiency and safety. Existing studies have largely focused on individual cells and their SOH and SOC metrics, with less attention paid [...] Read more.

Lithium-ion batteries are widely used in energy storage systems and consumer electronics; however, long-term usage leads to capacity degradation, which affects system efficiency and safety. Existing studies have largely focused on individual cells and their SOH and SOC metrics, with less attention paid to larger battery packs, while the SOP more effectively reflects the overall operational characteristics of a battery pack. Therefore, this study proposes a parameter-optimized prediction method based on PSO and SVM, using SOP as a key health indicator for life prediction. In this method, the global optimal solution is obtained by simulating the collaborative search behavior of the particle swarm, dynamically updating particle positions and velocities; this solution determines the SVM’s critical parameters, namely the kernel parameter g and the penalty coefficient c, which are then used to train the SVM model to enhance its generalization ability and prediction accuracy. The results indicate that the PSO-SVM model can effectively capture the degradation characteristics of battery packs, achieving high-precision SOP prediction, with most prediction errors below 5% and the maximum error reaching 10.91%. Full article

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22 pages, 6983 KB

Open AccessArticle

Bagging-PiFormer: An Ensemble Transformer Framework with Cross-Channel Attention for Lithium-Ion Battery State-of-Health Estimation

by Shaofang Wu, Jifei Zhao, Weihong Tang, Xuhui Liu and Yuqian Fan

Batteries 2025, 11(12), 447; https://doi.org/10.3390/batteries11120447 - 5 Dec 2025

Viewed by 378

Abstract

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) is critical for prolonging battery life and ensuring safe operation. To address the limitations of existing data-driven models in robustness and feature coupling, this paper presents a new Bagging-PiFormer framework for SOH estimation. [...] Read more.

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) is critical for prolonging battery life and ensuring safe operation. To address the limitations of existing data-driven models in robustness and feature coupling, this paper presents a new Bagging-PiFormer framework for SOH estimation. The framework integrates ensemble learning with an improved Transformer architecture to achieve accurate and stable performance across various degradation conditions. Specifically, multiple PiFormer base models are trained independently under the Bagging strategy to enhance generalization. Each PiFormer consists of a stack of PiFormer layers, which combines a cross-channel attention mechanism to model voltage–current interactions and a local convolutional feed-forward network (LocalConvFFN) to extract local degradation patterns from charging curves. Residual connections and layer normalization stabilize gradient propagation in deep layers, while a purely linear output head enables precise regression of the continuous SOH values. Experimental results on three datasets demonstrate that the proposed method achieves the lowest MAE, RMSE, and MAXE values among all compared models, reducing overall error by 10–33% relative to mainstream deep-learning methods such as Transformer, CNN-LSTM, and GCN-BiLSTM. These results confirm that the Bagging-PiFormer framework significantly improves both the accuracy and robustness of battery SOH estimation. Full article

(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)

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19 pages, 5943 KB

Open AccessArticle

A Physics-Guided Transformer for Robust State of Charge Estimation in Aging Lithium-Ion Batteries

by Xiang Li, Guanru Wu, Fei Chang, Weidong Xia, Shaobin Sun and Yingjun Shen

Batteries 2025, 11(12), 446; https://doi.org/10.3390/batteries11120446 - 3 Dec 2025

Viewed by 539

Abstract

Accurate state of charge (SOC) estimation is a critical challenge for battery management systems (BMSs), hindered by the nonlinear electrochemistry of lithium-ion batteries, their sensitivity to temperature, and pervasive measurement noise. Crucially, battery aging significantly degrades estimation accuracy, posing a major hurdle for [...] Read more.

Accurate state of charge (SOC) estimation is a critical challenge for battery management systems (BMSs), hindered by the nonlinear electrochemistry of lithium-ion batteries, their sensitivity to temperature, and pervasive measurement noise. Crucially, battery aging significantly degrades estimation accuracy, posing a major hurdle for long-term system dependability. We propose the Physics-Informed Transformer (PI-Transformer), a novel framework that integrates high-fidelity electrochemical constraints from the PyBaMM (Version: 25.10.2) model directly into a Transformer architecture. This approach ensures physical consistency while leveraging the Transformer’s self-attention mechanism to model long-term temporal dependencies. The framework is specifically designed to be robust against the effects of battery aging, incorporating an attention-based noise modeling module to enhance resilience against sensor uncertainty and capacity fade. Evaluated on two public datasets under diverse conditions, including variable temperatures, fast-charging protocols, and multiple stages of battery degradation, the PI-Transformer consistently achieves state-of-the-art performance. It demonstrates exceptional robustness and maintains high accuracy under challenging low-temperature and severely aged battery scenarios, highlighting its strong potential for deployment in real-world ESS applications where aging is a primary concern. Full article

(This article belongs to the Special Issue Batteries and Supercapacitors Aging III)

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17 pages, 5437 KB

Open AccessArticle

Battery Parameter Identification and SOC Estimation Based on Online Parameter Identification and MIUKF

by Liteng Zeng, Lei Zhao, Youwei Song, Yuli Hu and Guang Pan

Batteries 2025, 11(12), 445; https://doi.org/10.3390/batteries11120445 - 3 Dec 2025

Viewed by 435

Abstract

Accurate state of charge (SOC) estimation is crucial for the safety, reliability, and energy efficiency of lithium-ion battery systems. However, variations in battery parameters and the loss of historical information during the update steps of traditional unscented Kalman filters (UKFs) often lead to [...] Read more.

Accurate state of charge (SOC) estimation is crucial for the safety, reliability, and energy efficiency of lithium-ion battery systems. However, variations in battery parameters and the loss of historical information during the update steps of traditional unscented Kalman filters (UKFs) often lead to decreased estimation accuracy under dynamic operating conditions. To address these issues, this paper proposes a variable forgetting factor recursive least squares (VFFRLS) algorithm combined with a multi-innovation unscented Kalman filter (MIUKF) algorithm. First, a second-order RC equivalent circuit model is established, and the battery parameters are identified online using the VFFRLS method, enabling the model to dynamically adapt to changing operating conditions. Then, multi-innovation theory is incorporated into the standard UKF, extending the single-innovation matrix to a multi-innovation matrix, effectively enhancing the utilization of historical residuals and improving robustness to measurement noise and model uncertainty. Experimental validation under four typical dynamic operating conditions (FUDS, DST, BJDST, and US06) demonstrates that the proposed method significantly improves SOC estimation accuracy. Compared to the traditional UKF, MIUKF reduces MAE and RMSE by 25–30% while maintaining real-time performance, with single-step computation time reaching the microsecond level. Robustness tests under different initial SOC errors further validate MIUKF’s strong robustness to initial biases. In summary, the proposed method provides an effective solution for high-precision SOC estimation of batteries and has the potential for application in battery management systems. Full article

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50 pages, 3679 KB

Open AccessReview

Carbon-Based Anode Materials for Metal-Ion Batteries: Current Status, Challenges, and Future Directions

by Salim Hussain, Adeniyi Oyebade, Md Riyad Hossain, Fatima Abbas and Noureen Siraj

Batteries 2025, 11(12), 444; https://doi.org/10.3390/batteries11120444 - 3 Dec 2025

Viewed by 917

Abstract

The demand for effective, economical, and sustainable anode materials for metal-ion batteries (MIBs) has increased significantly due to the rapid growth of energy storage technologies. Among various candidates, carbon-based materials have emerged as highly promising due to their abundance, structural versatility, and favorable [...] Read more.

The demand for effective, economical, and sustainable anode materials for metal-ion batteries (MIBs) has increased significantly due to the rapid growth of energy storage technologies. Among various candidates, carbon-based materials have emerged as highly promising due to their abundance, structural versatility, and favorable electrochemical properties. This review highlights the current status and future directions of carbon-based anode materials in MIBs, with a particular focus on graphite, hard carbon, carbon nanotubes, heteroatom-doped carbons, carbon-based composites, and other related structures. Various synthesis strategies for these materials are presented, along with discussions on their physicochemical characteristics, including structural features that influence electrochemical performance. Furthermore, we provided an overview on the performance of newly developed carbon-based anode materials in lithium-, sodium-, potassium-, and other emerging metal-ion battery systems to assess the impact of different synthesis approaches. Special attention is given to surface engineering, heteroatom doping, and composite design that can address intrinsic challenges such as limited ion diffusion, low reversible capacity, and poor cycling stability in MIBs. This review does not cover any carbon materials which have been used as an additive. In addition, the review explores emerging opportunities enabled by advanced characterization techniques, computational modeling, and artificial intelligence for optimizing the design of next-generation carbon anode. Finally, this article provides future perspectives and insights into the design principles of novel carbon-based anode materials that can accelerate the development of high-performance, durable, and sustainable MIB technologies. Full article

(This article belongs to the Special Issue Advances in Hybrid Supercapacitors: Materials, Devices, Models, Systems, and Applications)

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17 pages, 1652 KB

Open AccessArticle

Boron-Doped Bamboo-Derived Porous Carbon via Dry Thermal Treatment for Enhanced Electrochemical Performance

by Hyeon-Hye Kim, Cheol-Ki Cho, Ju-Hwan Kim, Hye-Min Lee, Kay-Hyeok An, Dong-Cheol Chung and Byung-Joo Kim

Batteries 2025, 11(12), 443; https://doi.org/10.3390/batteries11120443 - 2 Dec 2025

Viewed by 384

Abstract

In this study, boron was introduced into bamboo-derived porous carbon (BPC) through dry thermal treatment using boric acid. During heat treatment, boric acid was converted to B₂O₃, which subsequently interacted with the oxygen-containing surface groups of BPC, leading to [...] Read more.

In this study, boron was introduced into bamboo-derived porous carbon (BPC) through dry thermal treatment using boric acid. During heat treatment, boric acid was converted to B₂O₃, which subsequently interacted with the oxygen-containing surface groups of BPC, leading to the formation and evolution of B–O–B and B–C bonds. This boron-induced bonding network reconstruction enhanced π-electron delocalization and surface polarity, while maintaining the intrinsic microporous framework of BPC. Among the prepared samples, B-BPC-1 exhibited an optimized balance between the conductive domains and defect concentration, resulting in lower internal resistance and improved ion transport behavior. Correspondingly, B-BPC-1 delivered a better capacitive performance than both undoped BPC and commercial activated carbon. These results indicate that controlling boron incorporation under appropriate heat-treatment conditions effectively improves charge-transfer kinetics while maintaining a stable pore morphology. The proposed dry thermal doping method provides a practical and environmentally benign route for developing high-performance porous carbon electrodes for electric double-layer capacitor applications. Full article

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

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38 pages, 3819 KB

Open AccessReview

Battery Passport and Online Diagnostics for Lithium-Ion Batteries: A Technical Review of Materials–Diagnostics Interactions and Online EIS

by Muhammad Usman Tahir, Tarek Ibrahim and Tamas Kerekes

Batteries 2025, 11(12), 442; https://doi.org/10.3390/batteries11120442 - 1 Dec 2025

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Abstract

Digital battery passports are being adopted to provide traceable records of lithium-ion batteries across their lifecycle, credible performance, and durability. However, it requires continuous diagnostics rather than lab-based tests and conditions. This review establishes a materials-informed system that links (i) battery-passport frameworks, (ii) [...] Read more.

Digital battery passports are being adopted to provide traceable records of lithium-ion batteries across their lifecycle, credible performance, and durability. However, it requires continuous diagnostics rather than lab-based tests and conditions. This review establishes a materials-informed system that links (i) battery-passport frameworks, (ii) cell-level design, and (iii) online electrochemical impedance spectroscopy (EIS) observables. Therefore, a chemistry-aware indicator set is proposed for passport reporting that relies on capacity and impedance indices, each accompanied by explicit tests. A review of the common and commercial LIBs (LCO, NCA, NMC, LMO, LFP) explains differences and characteristics. In addition, online EIS is reviewed, and different techniques for battery online diagnostics and state estimation are described, with details on how this online analysis is incorporated into the battery passport framework. This review covers the battery passport framework, the materials used in commercial batteries that must be documented and traced, and how these materials evolve throughout the degradation process. It concludes with the state of the art in online battery cell inspection, which enables comparable health reporting, conformity assessment, and second-life grading. Finally, it outlines key implementation priorities related to the reliability and accuracy of battery passport deployment and online battery diagnostics. Full article

(This article belongs to the Special Issue Battery Management Systems Based on Electrochemical Impedance Spectroscopy)

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34 pages, 7903 KB

Open AccessReview

Precisely Engineering Interfaces for High-Energy Rechargeable Lithium Batteries

by Kah Chun Lau and Xiangbo Meng

Batteries 2025, 11(12), 441; https://doi.org/10.3390/batteries11120441 - 29 Nov 2025

Viewed by 677

Abstract

While we are pursuing a fully electrified society, high-energy rechargeable batteries are undergoing intensive investigation. In this respect, atomic and molecular layer deposition (ALD and MLD) have been drawing increasing interest, due to their unmatched capabilities to precisely modify electrodes’ surfaces for better [...] Read more.

While we are pursuing a fully electrified society, high-energy rechargeable batteries are undergoing intensive investigation. In this respect, atomic and molecular layer deposition (ALD and MLD) have been drawing increasing interest, due to their unmatched capabilities to precisely modify electrodes’ surfaces for better electrochemical performance. In this work, we reviewed the recent studies using ALD/MLD for interface engineering of several important electrode materials, including nickel (Ni)-rich metal oxide cathodes, silicon (Si), and lithium (Li) anodes in lithium-ion and lithium metal batteries. We particularly discussed the most promising coatings from these studies and explored the underlying mechanisms based on experiments and modeling. We anticipate that this work will inspire more studies using ALD/MLD as an important technique for securing new solutions for batteries. Full article

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

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26 pages, 1864 KB

Open AccessArticle

Lithium-Ion Battery Lifetime Prediction Model Based on a Fusion Expert Network

by Yawei Meng, Qiang Sun, Zhi Wang, Qizheng Yang, Yuchen Song, Rui Xie, Quanyi Liu, Yang Lin and Fei Ren

Batteries 2025, 11(12), 440; https://doi.org/10.3390/batteries11120440 - 27 Nov 2025

Viewed by 865

Abstract

Accurate prediction of the State of Health (SOH) of lithium-ion batteries is essential for improving the safety and longevity of energy storage systems. This paper introduces ExpertMixer, a novel model based on a fused expert network for SOH estimation. By combining the strengths [...] Read more.

Accurate prediction of the State of Health (SOH) of lithium-ion batteries is essential for improving the safety and longevity of energy storage systems. This paper introduces ExpertMixer, a novel model based on a fused expert network for SOH estimation. By combining the strengths of state space models and recurrent neural networks, the model effectively handles the joint optimization of long-sequence dependency modeling and complex dynamic feature extraction. To improve temporal representation, ExpertMixer utilizes sampling time-based rotary position encoding (RoPE). It consists of two expert modules: a Mamba module designed to capture global degradation trends and an LSTM module focused on modeling local dynamic fluctuations. These are adaptively fused through a learnable gating mechanism that supports multi-scale feature integration. Experiments performed on the NASA PCoE dataset show that ExpertMixer achieves optimal performance on the NASA L subset, with an average MAE of 1.047 and RMSE of 1.603. It surpasses the traditional CNN BiGRU model, which had an MAE of 2.286, by 54.2%, and improves upon the advanced SambaMixer model, which had an MAE of 1.072, by 2.3%. Under low-temperature conditions using Battery 47, the model reduces the prediction error for nonlinear degradation to an MAE of 0.539, significantly exceeding all compared methods. Ablation studies verify the effectiveness of the dual-expert structure and fusion mechanism; removing the gating module results in an 18.7% decrease in performance. This research offers a new framework for lithium battery life prediction that demonstrates improved accuracy and generalization capability, suggesting potential practical value for intelligent energy storage management. Full article

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17 pages, 4410 KB

Open AccessArticle

Experimental Evidence on the Effect of Temperature on the Performance of a Lithium-Ion Battery

by Maurizio Bressan, Elena Campagnoli and Valter Giaretto

Batteries 2025, 11(12), 439; https://doi.org/10.3390/batteries11120439 - 26 Nov 2025

Viewed by 1002

Abstract

The current energy transition highlights the importance not only of energy production, but also of its efficient storage, for which lithium-ion batteries are currently the leading technology. In many applications, these devices operate outdoors at temperatures below 0 °C, and consequently, their performance [...] Read more.

The current energy transition highlights the importance not only of energy production, but also of its efficient storage, for which lithium-ion batteries are currently the leading technology. In many applications, these devices operate outdoors at temperatures below 0 °C, and consequently, their performance is reduced due to the lower mobility of the ions. With the aim of evaluating this decrease in performance, measurements were carried out on a commercial LiFePO₄ module in the temperature range −20–+55 °C. The results show that the battery capacity decreases by 15% compared to the value measured at room temperature when the operating temperature drops to approximately −10 °C, and by 35% at approximately −20 °C. The paper also introduces a modified version of the Arrhenius kinetic model that allows for the analytical evaluation of the change in battery capacity as a function of temperature. The modified model proposes a quadratic dependence of the activation energy on the temperature through a temperature coefficient that for the two tested modules is equal to 8.0 × 10⁻⁵ eV/K² and 6.7 × 10⁻⁵ eV/K², respectively. Full article

(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)

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4 pages, 124 KB

Open AccessEditorial

Modeling, Reliability, and Health Management of Lithium-Ion Batteries (2nd Edition)—A Summary of Contributions and Future Outlook

by Fei Feng

Batteries 2025, 11(12), 438; https://doi.org/10.3390/batteries11120438 - 26 Nov 2025

Viewed by 724

Abstract

Lithium-ion batteries (LIBs) are a cornerstone technology driving transportation electrification and renewable energy storage systems [...] Full article

(This article belongs to the Special Issue Modeling, Reliability and Health Management of Lithium-Ion Batteries—2nd Edition)

16 pages, 22701 KB

Open AccessArticle

Research on External Short Circuit Fault Evaluation Method for Li-Ion Batteries Based on Impedance Spectrum Feature Extraction

by Zhongshen Hong, Jinyuan Gao and Yujie Wang

Batteries 2025, 11(12), 437; https://doi.org/10.3390/batteries11120437 - 25 Nov 2025

Viewed by 440

Abstract

Accurate evaluation of the severity of external short-circuit (ESC) faults in li-ion batteries is critical to ensuring the safety and reliability of battery systems. This study proposes a novel ESC fault assessment method based on electrochemical impedance spectroscopy (EIS) and differential feature extraction [...] Read more.

Accurate evaluation of the severity of external short-circuit (ESC) faults in li-ion batteries is critical to ensuring the safety and reliability of battery systems. This study proposes a novel ESC fault assessment method based on electrochemical impedance spectroscopy (EIS) and differential feature extraction from relaxation time distributions. By comparing EIS responses before and after the short circuit, differential curves are constructed, and relevant peak descriptors are extracted to form physically interpretable feature vectors without requiring equivalent circuit modeling. Standardized feature data are further analyzed using principal component analysis (PCA) and K-Means clustering to perform unsupervised classification of fault severity. In addition, a differential evolution algorithm is employed to adaptively optimize the feature weights, enhancing the monotonic correlation between the weighted scores and actual short-circuit durations. The resulting SeverityScore provides an interpretable, mechanism-driven indicator of ESC fault severity. Experimental results demonstrate that the proposed method effectively distinguishes between mild and moderate short-circuit conditions and generalizes well across four independent battery groups. The model, trained on a single group, demonstrates strong robustness by accurately classifying the fault severity for three unseen validation groups. This data-driven framework offers a robust and model-free approach for fault evaluation, providing a promising tool for health monitoring and risk assessment in li-ion batteries. Full article

(This article belongs to the Special Issue Control, Modelling, and Management of Batteries)

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