Batteries

15 pages, 1353 KB

Open AccessArticle

Battery State-of-Health Estimation with Embedded Impedance Spectrum Features Under Multiple Battery Chemistry and Temperature Conditions

by Yue Xiang, Dikshit Chauhan and Dipti Srinivasan

Batteries 2026, 12(2), 77; https://doi.org/10.3390/batteries12020077 - 20 Feb 2026

Viewed by 595

Abstract

The transition to clean energy and electrification of transportation requires accurate, real-time monitoring of the state of health (SoH) of lithium-ion batteries, which serve as critical components for energy storage. Conventional SoH estimation methods typically rely on fixed statistical feature extraction, have poor [...] Read more.

The transition to clean energy and electrification of transportation requires accurate, real-time monitoring of the state of health (SoH) of lithium-ion batteries, which serve as critical components for energy storage. Conventional SoH estimation methods typically rely on fixed statistical feature extraction, have poor generalization ability, and are unsuitable for multiple battery chemistry and temperature conditions. In this work, we propose a deep learning framework based on a transformer encoder and XGBoost to extract ageing-related electrochemical impedance spectroscopy (EIS) features, capturing low-, mid-, and high-frequency ageing characteristics, directly from daily operation profiles for capacity estimation. The approach requires only current, voltage, and temperature time-series data, making it suitable for edge deployment without the need for explicit EIS measurements. Validation on a dataset with two battery chemistries and three temperature conditions yields a root-mean-square error of 0.16% to 0.20% in capacity estimation. These results establish the feasibility of accurate SoH estimation during multiple operation of battery energy storage systems and electric vehicles. Full article

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

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48 pages, 5396 KB

Open AccessReview

Neural Architectures and Learning Strategies for State-of-Health Estimation of Lithium-Ion Batteries: A Critical Review

by Tai Duc Le, Jin-Hyeok Park and Moo-Yeon Lee

Batteries 2026, 12(2), 76; https://doi.org/10.3390/batteries12020076 - 19 Feb 2026

Viewed by 831

Abstract

Accurate state-of-health (SOH) estimation is a cornerstone of safe, reliable, and cost-effective operation of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. In recent years, rapid advances in artificial intelligence technology have led to the widespread adoption of neural-network-based SOH estimation [...] Read more.

Accurate state-of-health (SOH) estimation is a cornerstone of safe, reliable, and cost-effective operation of lithium-ion batteries (LIBs) in electric vehicles and energy storage systems. In recent years, rapid advances in artificial intelligence technology have led to the widespread adoption of neural-network-based SOH estimation methods, offering strong nonlinear modeling capability and improved adaptability compared with traditional model-based approaches. However, the growing diversity of neural architectures and learning strategies has led to fragmented development and inconsistent evaluation, hindering their practical deployment. This paper presents a critical and systematic review of the most recent representative studies on neural-network-based SOH estimation for LIBs between 2024 and 2025. A unified taxonomy is introduced to distinguish neural architectures from learning strategies. The neural architectures include artificial neural networks, convolutional and recurrent networks, attention-based models, Transformers, and physics-informed neural networks. The learning strategies encompass transfer learning, physics-constrained/physics-informed learning, robustness-oriented training and efficiency-aware design. The reviewed methods are analyzed in terms of modeling capability, generalization across operating conditions and chemistries, data efficiency, interpretability and deployability within battery management systems. Key challenges including nonlinear degradation, degradation diversity, data scarcity, and limited observability are critically examined. The roles of architecture-strategy co-design in addressing these issues are highlighted. Finally, open research directions and practical recommendations are discussed to guide the development of reliable, scalable and physically consistent SOH estimation frameworks. This review provides a structured reference for researchers and practitioners seeking to advance data-driven battery health monitoring toward real-world applications. Full article

(This article belongs to the Special Issue Artificial Intelligence-Based State-of-Health Estimation of Lithium-Ion Batteries: 3rd Edition)

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24 pages, 5042 KB

Open AccessArticle

Novel Anodic Material Sourced from Biomass Based on Amorphous Carbon Doped with Aluminum as an Efficient Alternative for Next-Generation Lithium-Ion Batteries

by Alifhers Mestra, Silvio Ceballos, Sergio Conejeros, Jaime Llanos, Karem Gallardo and Jonathan Cisterna

Batteries 2026, 12(2), 75; https://doi.org/10.3390/batteries12020075 - 18 Feb 2026

Viewed by 479

Abstract

This article focuses on the synthesis and characterization of an amorphous carbon derived from spent coffee grounds converted into a porous amorphous carbon (Cp₁) by carbonization up to 900 °C and subsequently combined with aluminum via mechanochemical treatment to obtain the [...] Read more.

This article focuses on the synthesis and characterization of an amorphous carbon derived from spent coffee grounds converted into a porous amorphous carbon (Cp₁) by carbonization up to 900 °C and subsequently combined with aluminum via mechanochemical treatment to obtain the composite Al@Cp₁. Powder X-Ray diffraction, Raman spectroscopy, and X-Ray photoelectron spectroscopy indicate turbostratic carbon domains (ID/IG ≈ 1.04) and an Al–O/Al–OH surface layer (Al₂O₃/Al(OH)₃) with a minor metallic Al contribution. Electrochemical performance in Li half-cells was evaluated by cyclic voltammetry, galvanostatic cycling, rate capability tests, and electrochemical impedance spectroscopy. At 0.02 A g⁻¹, Al@Cp₁ delivers 212.1 mAh g⁻¹, compared with 83.0 mAh g⁻¹ for Cp₁, with an initial coulombic efficiency of ~44%. Across increasing current densities, Al@Cp₁ retains higher reversible capacities than Cp₁ and shows stable cycling over extended tests (>160 cycles). Impedance analysis indicates a reduced interfacial/charge transfer resistance after electrode conditioning, consistent with interfacial stabilization by the Al-containing surface layer. These results demonstrate a simple, scalable route to upgrade coffee waste carbon into a higher-performance lithium-ion battery anode through mechanochemical interfacial engineering. Full article

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50 pages, 3749 KB

Open AccessReview

A Review of Nail Penetration and Thermal Abuse Tests of Lithium-Ion Batteries and Their Emission Characterization

by Ananthu Shibu Nair, Xiao-Yu Wu, Prodip K. Das and Michael Fowler

Batteries 2026, 12(2), 74; https://doi.org/10.3390/batteries12020074 - 18 Feb 2026

Viewed by 1501

Abstract

Lithium-ion batteries (LIBs) are pivotal in electric vehicles (EVs), grid storage, and portable electronics, but their high energy density introduces safety risks, particularly thermal runaway (TR). TR can lead to fires, explosions, and hazardous emissions, posing severe health and environmental threats. Experimental investigation [...] Read more.

Lithium-ion batteries (LIBs) are pivotal in electric vehicles (EVs), grid storage, and portable electronics, but their high energy density introduces safety risks, particularly thermal runaway (TR). TR can lead to fires, explosions, and hazardous emissions, posing severe health and environmental threats. Experimental investigation of TR commonly relies on abuse testing methods, among which mechanical abuse via nail penetration (NP) and thermal abuse (TA) are widely used to simulate crash-induced and heat-driven failure scenarios, respectively. This review provides a comprehensive and comparative synthesis of NP and TA testing methodologies, examining how variations in test configuration, cell parameters (capacity, state of charge, and chemistry), and environmental conditions influence TR behavior and emission characteristics. Particular emphasis is placed on comparing reported emission profiles from NP- and TA-triggered TR events, including CO₂, CO, HF, hydrocarbons, and solvent vapors, and identifying the methodological origins of discrepancies across studies. By systematically linking emission variability to gas collection methods, analytical techniques, and data normalization approaches, this review highlights key limitations in current testing standards related to emission characterization. Finally, recommendations are offered for harmonizing abuse testing protocols and improving experimental design to enhance reproducibility, enabling meaningful cross-study comparison, and supporting safer deployment of LIBs in high-risk applications such as EVs and grid-scale energy storage. Full article

(This article belongs to the Special Issue Battery Management in Electric Vehicles: Current Status and Future Trends: 3rd Edition)

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20 pages, 4009 KB

Open AccessFeature PaperArticle

Strategies for Enhancing Battery Life Under Fast Charging: Insights from NMC-Based Cell Cycling

by Saiful Islam, Pete Barnes, Bumjun Park, Bianca Yi Wen Mak, Michael C. Evans, Eric J. Dufek and Tanvir R. Tanim

Batteries 2026, 12(2), 73; https://doi.org/10.3390/batteries12020073 - 17 Feb 2026

Viewed by 969

Abstract

Fast charging improves the usability of consumer electronics and electric vehicles (EVs) by reducing range anxiety and downtime but accelerates battery degradation and raises safety concerns. Optimizing operational conditions during fast-charging is critical to mitigating aging and ensuring safety. This study evaluated multilayer [...] Read more.

Fast charging improves the usability of consumer electronics and electric vehicles (EVs) by reducing range anxiety and downtime but accelerates battery degradation and raises safety concerns. Optimizing operational conditions during fast-charging is critical to mitigating aging and ensuring safety. This study evaluated multilayer Gr/NMC811 cells under various conditions, including depths of discharge (DODs of 68%, 84%, and 100%), upper charge cutoff voltages (4.1–4.2 V), and post-charge rest periods (2–30 min), using a 20 min fast charging protocol for up to 500 cycles (up to 150,000 miles of EV use assuming 3.3 mi/kWh vehicle level energy efficiency). Surprisingly, higher DODs under fast charging improved battery life and performance compared to lower DODs. Reducing the upper charge cut-off voltage helped mitigate degradation. A brief 2 min rest period after charging further reduced aging effects. The primary aging modes were loss of lithium inventory and cathode active material. Although minor lithium plating was observed within 500 cycles, it did not affect performance significantly. These findings suggest that, with optimized conditions, cells can sustain hundreds of fast charge cycles—equivalent to over 100,000 miles of EV use—without significant adverse effects on performance or longevity. Full article

(This article belongs to the Section Energy Storage System Aging, Diagnosis and Safety)

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21 pages, 9542 KB

Open AccessArticle

Architectural Evolution and Advanced Joining Techniques in High-Energy-Density Cylindrical Li-Ion Cells

by Masilamani Chelladurai Asirvatham, Puritut Nakhanivej, Vincent A. Perry-French, Ehman F. Altaf, Melanie J. Loveridge, Tanveerkhan S. Pathan and James D. McLaggan

Batteries 2026, 12(2), 72; https://doi.org/10.3390/batteries12020072 - 17 Feb 2026

Viewed by 876

Abstract

This study presents a comparative analysis of cylindrical lithium-ion cell architectures, tracing the evolution from the conventional tabbed design (18650/21700) to the large-format 4680 cell with its tabless current collectors. This architectural shift is driven by the imperative to minimise internal ohmic resistance [...] Read more.

This study presents a comparative analysis of cylindrical lithium-ion cell architectures, tracing the evolution from the conventional tabbed design (18650/21700) to the large-format 4680 cell with its tabless current collectors. This architectural shift is driven by the imperative to minimise internal ohmic resistance and enhance thermal management in high-power automotive battery applications. Forensic investigation reveals that the 4680 design replaces localised, high-resistance tab connections with a distributed, low-impedance interface, necessitating the adoption of advanced manufacturing techniques, including long ultrasonic torsional welding and highly controlled high-power density laser welding. Crucially, the welding of external aluminium busbars to the cell relies on sophisticated microstructural engineering, particularly for the challenging dissimilar Aluminium-Steel (Al-Steel) anode weld. This weld format employs a spiral laser path to limit the formation of brittle aluminium-iron (Al-Fe) intermetallic compounds (IMCs), leveraging the steel cell casing’s nickel plating to promote a more ductile Al-Fe-Ni phase for improved joint reliability. Furthermore, the 4680 cell incorporates a significantly thicker casing (≈0.54 to 0.7 mm) for enhanced mechanical strength. In conclusion, the 4680 cell achieves superior performance through robust mechanical design and advanced welding processes that prioritise microstructurally sound, low-resistance interfaces. Full article

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

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15 pages, 1569 KB

Open AccessArticle

Accelerated Electrochemical Impedance Spectroscopy of LFP Modules Using Gradient-Based Sensitivity and D-Optimal Selection

by Isabel Aguilar, Ekaitz Zulueta, Unai Fernandez and Javier Olarte

Batteries 2026, 12(2), 71; https://doi.org/10.3390/batteries12020071 - 16 Feb 2026

Viewed by 388

Abstract

Efficient and accurate characterization of lithium-ion battery packs is critical for both first-life applications and second-life reuse. Electrochemical Impedance Spectroscopy (EIS) provides detailed insight into internal electrochemical processes, but full-spectrum measurements are time-consuming, especially at low frequencies. This work presents a methodology combining [...] Read more.

Efficient and accurate characterization of lithium-ion battery packs is critical for both first-life applications and second-life reuse. Electrochemical Impedance Spectroscopy (EIS) provides detailed insight into internal electrochemical processes, but full-spectrum measurements are time-consuming, especially at low frequencies. This work presents a methodology combining cell-level equivalent circuit modeling, integrated gradients sensitivity analysis, and D-optimal frequency selection to reduce the number of measurement points while preserving parameter identifiability. Individual 16s5p LFP cells were characterized using full-spectrum EIS at 10 °C, and the resulting equivalent circuit models were scaled to the pack level. Integrated gradients were used to quantify the frequency-dependent influence of each parameter on the real and imaginary parts of the impedance, identifying the regions containing the most information. Using the per-frequency Jacobian and the Fisher Information Matrix, a D-optimal frequency selection was performed to demonstrate that a reduced set of measurements is sufficient to estimate key parameters reliably. The results show that variations in parameters due to aging are accurately captured using the reduced frequency set, validating the approach for fast, accurate, and traceable characterization at the pack level. The proposed methodology highlights a systematic strategy for frequency selection, enabling faster EIS measurements, maintaining sensitivity to aging and degradation mechanisms, and supporting standardized and sustainable evaluation of lithium-ion batteries. Full article

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19 pages, 6909 KB

Open AccessArticle

Glycolic Acid-Induced Surface Reconstruction and In Situ Carbon Coating for High-Electrochemical-Performance Lithium-Rich Manganese-Based Cathodes

by Xichen Yang, Jie Miao, Yongchao Chen, Yaoxun Fang, Hao Wang and Gongchang Peng

Batteries 2026, 12(2), 70; https://doi.org/10.3390/batteries12020070 - 15 Feb 2026

Viewed by 514

Abstract

Lithium-rich manganese-based cathode materials (LRMs, Li_1.2Mn_0.54Ni_0.13Co_0.13O₂) are promising prospects for subsequent-generation lithium-ion batteries owing to their elevated operating voltage, large specific capacity, and affordability. Nonetheless, their actual implementation is significantly impeded by irreversible [...] Read more.

Lithium-rich manganese-based cathode materials (LRMs, Li_1.2Mn_0.54Ni_0.13Co_0.13O₂) are promising prospects for subsequent-generation lithium-ion batteries owing to their elevated operating voltage, large specific capacity, and affordability. Nonetheless, their actual implementation is significantly impeded by irreversible lattice-oxygen redox reactions, surface structural disorder, and interfacial phase collapse, leading to low initial Coulombic efficiency (ICE), inadequate rate capability, and sluggish Li⁺ transport. Herein, we report a simple and mild glycolic acid-assisted surface-engineering strategy to enhance the electrochemical performance of LRM. Glycolic acid treatment induces controlled H⁺/Li⁺ ion exchange at the particle surface and anchors surface transition metals through the formation of transition metals (TM)–OH and TM–O–C=O bonds. Subsequent calcination constructs an in situ carbon layer-spinel-layered heterostructure, accompanied by the generation of coupled anionic and cationic vacancies. This reconstructed surface provides fast Li⁺ diffusion pathways and stabilized ion-transport channels, while the dual-vacancy configuration enhances lattice-oxygen reversibility and suppresses structural disorder. Consequently, the modified LRM delivers a high initial discharge capacity of 285.3 mAh⋅g⁻¹ with an ICE of 89.9%, while maintaining 81% capacity retention after 100 cycles. Notably, it exhibits a significantly suppressed voltage decay of only 1.7 mV/cycle at 3C, markedly outperforming the pristine LRM. Density Functional Theory (DFT) calculations reveal that the surface-modified sample possesses enhanced electronic conductivity, as evidenced by the improved Density of States (DOS), and achieves superior structural stability through increased binding energies. This environmentally benign surface-engineering strategy offers a practical and efficient route toward the industrial application of LRM. Full article

(This article belongs to the Special Issue Toward Next-Generation Rechargeable Lithium-Ion Batteries: Current Status and Future Prospects—2nd Edition)

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23 pages, 6544 KB

Open AccessArticle

Electrochemical Stability of Passive Films on β-TiZrNbTa Alloys in Seawater-Based Electrolytes: Influence of Fluoride, pH, and Scan Rate

by Manal A. El Sayed, Ibrahim H. Elshamy, Sami M. Alharbi and Magdy A. M. Ibrahim

Batteries 2026, 12(2), 69; https://doi.org/10.3390/batteries12020069 - 15 Feb 2026

Viewed by 953

Abstract

The corrosion behavior and passive-film stability of a β-TiZrNbTa (β-TZNT) alloy were investigated in artificial seawater (ASW), focusing on the effects of pH, temperature, immersion time, fluoride ion concentration, and potential scan rate. In addition to electrochemical methods such as open-circuit potential (OCP), [...] Read more.

The corrosion behavior and passive-film stability of a β-TiZrNbTa (β-TZNT) alloy were investigated in artificial seawater (ASW), focusing on the effects of pH, temperature, immersion time, fluoride ion concentration, and potential scan rate. In addition to electrochemical methods such as open-circuit potential (OCP), potentiodynamic polarization (PDP), and electrochemical impedance spectroscopy (EIS), scanning electron microscopy (SEM) and X-ray diffraction (XRD) were employed for surface characterization. The establishment of a stable and efficient passive layer enriched in Zr-, Nb-, and Ta-oxides was responsible for the β-TZNT alloy’s superior corrosion resistance in fluoride-free ASW when compared to commercially pure titanium. Reduced passive-film resistance resulted from corrosion kinetics being greatly accelerated by decreasing the pH and increasing the temperature. The presence of fluoride ions strongly affected the passivity of the alloy due to the chemical dissolution of TiO₂ through the formation of soluble fluoride complexes, resulting in an increase in the corrosion current densities by more than one order of magnitude. A bilayer passive structure with a compact inner barrier layer and a porous outer layer was identified by EIS analysis. The stability of this structure gradually decreased with increasing fluoride concentration and acidity. Over time, passive-film degradation was dominant in fluoride-free seawater, whereas prolonged exposure in fluoride-containing media promoted partial re-passivation. Overall, these results highlight the potential and limitations of the β-TZNT alloy for marine and offshore applications by offering new mechanistic insights into the synergistic effects of fluoride ions and environmental factors on corrosion performance. Full article

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24 pages, 2132 KB

Open AccessArticle

Toward Sustainable Anode Materials: LCA of Natural Graphite Processing in Québec

by Gary Vegh, Sajedi Sarah, Ivan Kantor, Khalil Amine, Muskan Srivastava, Mina Rezayi, Anil Kumar Madikere Raghunatha Reddy and Karim Zaghib

Batteries 2026, 12(2), 68; https://doi.org/10.3390/batteries12020068 - 15 Feb 2026

Viewed by 1195

Abstract

Graphite is a critical mineral used to produce anodes for lithium-ion batteries (LIBs). Battery-grade anode active material (AAM) is derived from natural graphite. As the electric vehicle (EV) market continues to expand across North America, establishing a local AAM supply chain has become [...] Read more.

Graphite is a critical mineral used to produce anodes for lithium-ion batteries (LIBs). Battery-grade anode active material (AAM) is derived from natural graphite. As the electric vehicle (EV) market continues to expand across North America, establishing a local AAM supply chain has become increasingly important. This new supply chain must be sustainable if critical minerals are to replace the internal combustion engine (ICE) powertrain in vehicles. Canada possesses abundant critical mineral resources, including natural graphite, which is mined and processed in the province of Québec. To better understand the environmental implications of this emerging supply chain, a life cycle assessment (LCA) was conducted on a Québec-based graphite mine and processing facility. The results showed that producing one ton of AAM in Québec generates approximately 1.44 tons of CO₂-equivalent (long-term) emissions, significantly lower than the 9.6 tons of CO₂ emitted per ton of graphite produced in China. Natural gas used for purification and coating at the process plant was the largest contributor of CO₂ in this study. Although this LCA in Québec represents a substantial reduction in carbon intensity, further opportunities must be explored to enhance sustainability and strengthen North America’s graphite supply chain. Full article

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

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24 pages, 17655 KB

Open AccessArticle

Mechanisms of Electrochemical Performance Degradation and Thermal Runaway Risk Evolution in LiFePO₄ Pouch Batteries After Extreme Low-Temperature Storage

by Feng Gao, Desheng Qiang, Yanping Bai, Zongliang Zhai, Yechang Gao, Weixing Lu and Ruixin Jia

Batteries 2026, 12(2), 67; https://doi.org/10.3390/batteries12020067 - 15 Feb 2026

Viewed by 792

Abstract

This research focuses on the passive behavior changes of 3 Ah pouch LiFePO₄ (LFP) batteries during low-temperature storage, a point often neglected in previous studies. This experiment examines the low-temperature non-operational endurance of fully charged batteries (FCB) at 25 °C, −10 °C, [...] Read more.

This research focuses on the passive behavior changes of 3 Ah pouch LiFePO₄ (LFP) batteries during low-temperature storage, a point often neglected in previous studies. This experiment examines the low-temperature non-operational endurance of fully charged batteries (FCB) at 25 °C, −10 °C, and −35 °C. Battery performance reliability under these conditions is evaluated through capacity retention and internal resistance (IR) analysis. Microstructural changes on the surfaces of thawed battery electrodes are acquired using scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques. After seven freeze–thaw cycles, the maximum usable capacity is marginally affected. Notably, a pronounced increase in polarization resistance (R_p) has been observed, particularly at −10 °C conditions, with an increase of about 40.57 mΩ. Microstructural analyses reveal that low-temperature storage significantly led to cracking of the electrolyte layer and of the particles in the anode material. Subsequently, at room temperature (RT, 25 °C), external short circuit (ESC) tests were performed on thawed batteries. At 50C, the peak temperatures recorded at the center of the FCB₋₁₀, FCB₂₅, and FCB₋₃₅ batteries are 104.35 °C, 94.67 °C, and 90.56 °C, respectively. The batteries exhibit rupture at approximately 47 s, 60 s, and 70 s during the ESC process. The results show that battery FCB₋₃₅ exhibits a slower temperature rise and delayed physical damage during ESC. Full article

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

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16 pages, 2866 KB

Open AccessArticle

Research on Three-Dimensional Localization of Pressure Relief Sound Source of Energy Storage Battery Pack Based on BP Neural Networks

by Shan Jiang, Chen Zhang, Qili Lin, Xingtong Li, Yangjun Wang, Zhikuan Wang, Yindi Wang, Jian Zhao, Zhengye Yang, Tianying Liu and Jifeng Song

Batteries 2026, 12(2), 66; https://doi.org/10.3390/batteries12020066 - 14 Feb 2026

Viewed by 366

Abstract

Thermal runaway events in energy storage power stations exhibit distinct acoustic characteristic signals. Three-dimensional localization of the sound source is of significant importance for achieving precise firefighting interventions. This study proposes an internal fault localization method for power stations based on the acoustic [...] Read more.

Thermal runaway events in energy storage power stations exhibit distinct acoustic characteristic signals. Three-dimensional localization of the sound source is of significant importance for achieving precise firefighting interventions. This study proposes an internal fault localization method for power stations based on the acoustic signals from pressure relief valves of energy storage battery packs. By deploying four microphones to capture the acoustic signals from the battery pack pressure relief valves, the spatial location of the faulty pack can be calculated using a three-dimensional localization model trained on a Back Propagation (BP) neural network. The localization accuracy of this model is better than 0.5 m, with the majority of measurement points achieving an accuracy of less than 0.3 m, meeting the requirements for battery pack-level localization. A key advantage of this method is its low sensitivity to time delay measurement errors caused by reverberation and reflections in enclosed spaces. Reliable and stable localization of pressure relief sound sources can be achieved through multiple training sessions within the battery cabin, which facilitates practical deployment. Full article

(This article belongs to the Section Energy Storage System Aging, Diagnosis and Safety)

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28 pages, 4186 KB

Open AccessArticle

Comparative Evaluation of Power Management Strategies in Multi-Stack Fuel Cell-Battery Hybrid Truck via TOPSIS

by Sanghyun Yun and Jaeyoung Han

Batteries 2026, 12(2), 65; https://doi.org/10.3390/batteries12020065 - 14 Feb 2026

Viewed by 408

Abstract

Multi-stack Polymer electrolyte Membrane Fuel Cell (PEMFC) systems are increasingly adopted in heavy-duty mobility to overcome the power limitations and thermal instability of single-stack configurations. However, the overall energy efficiency, hydrogen utilization, and thermal behavior of multi-stack fuel cell trucks are highly dependent [...] Read more.

Multi-stack Polymer electrolyte Membrane Fuel Cell (PEMFC) systems are increasingly adopted in heavy-duty mobility to overcome the power limitations and thermal instability of single-stack configurations. However, the overall energy efficiency, hydrogen utilization, and thermal behavior of multi-stack fuel cell trucks are highly dependent on the applied Power Management System (PMS). In this study, high-fidelity, system-level dynamic model of multi-stack fuel cell truck was developed using Matlab/Simscape^TM, and three PMS approaches (rule-based control, state-machine control, and fuzzy logic control) were comparatively evaluated. The analysis includes coolant temperature regulation, hydrogen consumption, battery State of Charge (SoC) dynamics, and the parasitic power demand of Balance of Plant (BoP) components. Results show that the fuzzy logic PMS provides the most balanced operating profile by smoothing transient fuel cell loading and actively leveraging the battery during high-demand periods. In the thermal domain, the fuzzy logic PMS reduced temperature overshoot by up to 61.20%, demonstrating the most stable thermal control among the three strategies. Hydrogen consumption decreased by 3.08% and 0.89% compared with the rule-based and state-machine PMS, respectively, while parasitic power consumption decreased by 7.12% and 3.32%, confirming improvements in overall energy efficiency. TOPSIS-based multi-criteria decision analysis further showed that the fuzzy logic PMS achieved the highest closeness coefficient (0.9112), indicating superior system-level performance. These findings highlight the importance of PMS design for achieving energy-optimal and thermally stable operation of multi-stack PEMFC trucks and provide practical guidance for future control strategies, heavy-duty mobility applications, and next-generation hydrogen powertrain optimization. Full article

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

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31 pages, 6189 KB

Open AccessArticle

A Data-Driven Method Based on Feature Engineering and Physics-Constrained LSTM-EKF for Lithium-Ion Battery SOC Estimation

by Yujuan Sun, Shaoyuan You, Fangfang Hu and Jiuyu Du

Batteries 2026, 12(2), 64; https://doi.org/10.3390/batteries12020064 - 14 Feb 2026

Viewed by 603

Abstract

Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO₄ batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC [...] Read more.

Accurate estimation of the State of Charge (SOC) for lithium-ion batteries is a core function of the Battery Management System (BMS). However, LiFePO₄ batteries present specific challenges for SOC estimation due to the characteristic plateau in their open-circuit voltage (OCV) versus SOC relationship. Moreover, data-driven estimation approaches often face significant difficulties stemming from measurement noise and interference, the highly nonlinear internal dynamics of the battery, and the time-varying nature of key battery parameters. To address these issues, this paper proposes a Long Short-Term Memory (LSTM) model integrated with feature engineering, physical constraints, and the Extended Kalman Filter (EKF). First, the model’s temporal perception of the historical charge–discharge states of the battery is enhanced through the fusion of temporal voltage information. Second, a post-processing strategy based on physical laws is designed, utilizing the Particle Swarm Optimization (PSO) algorithm to search for optimal correction factors. Finally, the SOC obtained from the previous steps serves as the observation input to EKF filtering, enabling a probabilistically weighted fusion of the data-driven model output and the EKF to improve the model’s dynamic tracking performance. When applied to SOC estimation of LiFePO₄ batteries under various operating conditions and temperatures ranging from 0 °C to 50 °C, the proposed model achieves average Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as low as 0.46% and 0.56%, respectively. These results demonstrate the model’s excellent robustness, adaptability, and dynamic tracking capability. Additionally, the proposed approach only requires derived features from existing input data without the need for additional sensors, and the model exhibits low memory usage, showing considerable potential for practical BMS implementation. Furthermore, this study offers an effective technical pathway for state estimation under a “physical information–data-driven–filter fusion” framework, enabling accurate SOC estimation of lithium-ion batteries across multiple operating scenarios. Full article

(This article belongs to the Special Issue Battery Degradation: Behavior, Mechanisms, Modeling, Estimation, and Optimization Strategies)

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27 pages, 4613 KB

Open AccessArticle

A Reusable Framework for Dynamic Simulation of Grid-Scale Lithium-Ion Battery Energy Storage

by Renos Rotas, Panagiotis Karafotis, Petros Iliadis, Nikolaos Nikolopoulos, Dimitrios Rakopoulos and Ananias Tomboulides

Batteries 2026, 12(2), 63; https://doi.org/10.3390/batteries12020063 - 14 Feb 2026

Viewed by 593

Abstract

This paper presents a modeling framework for large-capacity lithium-ion battery energy storage systems (BESSs), developed within the Modelica LIBSystems library and focused on system-level integration. The framework builds on a combined analysis of the electrical, thermal and degradation behavior at the cell level [...] Read more.

This paper presents a modeling framework for large-capacity lithium-ion battery energy storage systems (BESSs), developed within the Modelica LIBSystems library and focused on system-level integration. The framework builds on a combined analysis of the electrical, thermal and degradation behavior at the cell level to model the BESS interconnection to the electrical grid. A semi-empirical aging model was incorporated following its validation at the cell level against capacity loss experimental measurements. Two case studies were conducted for a 10.5 MW/15 MWh BESS installed in the isolated power system of Terceira Island. The first analyzed the short-term response to a 5% load step decrease under 60% and 80% renewable penetration scenarios, yielding a frequency nadir improvement of 3 mHz and 21 mHz, respectively. The second projected long-term degradation under two dispatch strategies: one derived from historical time series, and another synthetically constructed to induce more frequent and deeper cycling. After 1000 days of operation, the state of health declined to 95.2% in the historical-based case and to 93.5% under the aggressive profile. The proposed framework establishes a unified, cross-domain modeling workbench for Li-ion BESS applications, enabling evaluation of the system design, control strategies, operation conditions, and system-level performance across both dynamic and long-term horizons. Full article

(This article belongs to the Special Issue Recent Advances in Numerical Modeling and Experimental Validation of Batteries)

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16 pages, 3373 KB

Open AccessArticle

Intelligent Assessment Framework of Unmanned Air Vehicle Health Status Based on Bayesian Stacking

by Junfu Qiao, Jinqin Guo, Yu Zhang and Yongwei Li

Batteries 2026, 12(2), 62; https://doi.org/10.3390/batteries12020062 - 14 Feb 2026

Viewed by 393

Abstract

This paper proposed a stacking-based ensemble model to replace the traditional single machine learning model prediction approach, significantly improving the evaluation efficiency of SoC and SoH of lithium batteries. Firstly, a dataset was constructed including three input variables (temperature, current, and voltage) and [...] Read more.

This paper proposed a stacking-based ensemble model to replace the traditional single machine learning model prediction approach, significantly improving the evaluation efficiency of SoC and SoH of lithium batteries. Firstly, a dataset was constructed including three input variables (temperature, current, and voltage) and two output variables (SoC and SoH). Pearson correlation coefficients and histograms were used for preliminary analysis of the correlations and distributions of the dataset. The multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and extreme gradient boosting tree (XGB) were used as base prediction models. Bayesian optimization (BO) was used to fine-tune the parameters of these models, then three statistical indicators were compared to assess the prediction accuracy of the four ML models. Furthermore, MLP, SVM, and RF were selected as base models, while XGB was used as the meta-model, enhancing the integrated performance of the prediction models. SHAP was used to quantify the influence of the output variables on SoC. Finally, linked measures for the prediction model were proposed to achieve autonomous monitoring of drones. The results showed that XGB exhibited superior prediction accuracy, with R² of 0.93 and RMSE of 0.14. The ensemble model obtained using stacking reduced the number of outliers by 89.4%. Current was identified as the key variable influencing both SoC and SoH. Furthermore, the intelligent prediction model proposed in this paper can be integrated with controllers, visualization web pages, and other systems to enable the health status assessment of drones. Full article

(This article belongs to the Section Energy Storage System Aging, Diagnosis and Safety)

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30 pages, 3911 KB

Open AccessArticle

Uncertainty-Aware Lightweight Design of CFRP Battery Enclosure Under Extreme Cold Side-Pole Impact via Bayesian Surrogates

by Desheng Zhang, Jieguo Liao, Longbin Wang, Zhenxin Sun and Han Zhang

Batteries 2026, 12(2), 61; https://doi.org/10.3390/batteries12020061 - 13 Feb 2026

Viewed by 507

Abstract

Mass M (kg) and peak intrusion L (mm) are jointly minimized for a CFRP-enabled battery pack enclosure under the GB 38031-2025 −40° side-pole extrusion condition. A 50-run explicit FE design of experiments is conducted and deterministically partitioned into 37/5/5/3 for initial training, two [...] Read more.

Mass M (kg) and peak intrusion L (mm) are jointly minimized for a CFRP-enabled battery pack enclosure under the GB 38031-2025 −40° side-pole extrusion condition. A 50-run explicit FE design of experiments is conducted and deterministically partitioned into 37/5/5/3 for initial training, two sequential enrichment batches, and an independent hold-out test. Bayesian additive regression trees are trained as the primary surrogates for M, L, and Stress, and stress acceptability is enforced through a probability-of-feasibility (PoF) gate anchored to a baseline-scaled cap, σ_lim = 1.2 σ_base = 410.4 MPa. NSGA-II performed on the feasible surrogate landscape yields a bimodal feasible non-dominated set. The two branches correspond to two discrete levels of a key thickness variable x₄: a low-mass regime (n = 106) with M = 100.61–104.81 kg and L = 5.430–5.516 mm at x₄ ≈ 5.60 mm, and a stiffer regime (n = 94) with M = 110.69–115.08 kg and L = 5.362–5.430 mm at x₄ ≈ 8.00 mm. PoF screening eliminates part of the intermediate region where feasibility confidence is insufficient. Independent FE reruns further indicate that the PoF gate reduces deterministic misclassification near the stress boundary (e.g., one near-threshold candidate exceeds σ_lim, whereas others satisfy the cap with margin). Overall, the proposed workflow offers a traceable lightweighting route under extreme-cold uncertainty within a constrained FE budget. Full article

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

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21 pages, 4643 KB

Open AccessArticle

When Electrolytes Are Semiconductors: A Feature, Not a Bug for Solid-State Batteries

by Beatriz M. Gomes, Manuela C. Baptista and M. Helena Braga

Batteries 2026, 12(2), 60; https://doi.org/10.3390/batteries12020060 - 13 Feb 2026

Viewed by 620

Abstract

The development of stable and efficient solid electrolytes is essential for advancing solid-state battery technologies. In this study, we present a comparative study of three sulfide-based electrolytes, Li₆PS₅Cl (LPSCl), Li₆PS₅Br (LPSBr), and Li₁₀GeP [...] Read more.

The development of stable and efficient solid electrolytes is essential for advancing solid-state battery technologies. In this study, we present a comparative study of three sulfide-based electrolytes, Li₆PS₅Cl (LPSCl), Li₆PS₅Br (LPSBr), and Li₁₀GeP₂S₁₂ (LGPS), combining Density Functional Theory (DFT) and hybrid (HSE06) simulations for electrochemical, charge carrier transport, and structural characterization. DFT and HSE06 simulations revealed semiconductor-like direct band gaps for LPSCl, with a 2.45 eV (DFT) −3.30 eV (HSE06) and 2.32 eV (DFT) −3.34 eV (HSE06) for LPSBr, and indirect band gap with 2.13 eV (DFT) −3.22 eV (HSE06) for LGPS, along with work functions of 3.40 eV for the argyrodites and 3.67 eV for LGPS. Scanning Kelvin Probe (SKP) analyses, performed at both micrometric and nanometric resolution, showed consistently negative surface potentials and interfacial polarons associated with electron tunneling through the surface of the electrolyte. Potentiostatic electrochemical impedance spectroscopy (PEIS) and cyclic voltammetry (CV) confirmed enhanced ionic conductivity with increasing temperature. While LPSCl and LGPS exhibited stable behavior at almost all temperatures, from −20 to 60 °C, LPSBr displayed noise-like activity at 0 °C with Au symmetric electrodes. This integrated experimental/theoretical approach highlights differences in electronic structure, interfacial charge distribution, and electrochemical stability, all showing affinity to react with lithium, providing key insights for the design and optimization of solid electrolytes for next-generation batteries. Full article

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9 pages, 3359 KB

Open AccessCommunication

Hybrid Ionic Liquid–Organic Electrolyte Technologies for Sodium-Ion Batteries

by Daniela Ariaudo, Antonio Rinaldi, Rodolfo Araneo, Alessandro Dell’Era and Giovanni Battista Appetecchi

Batteries 2026, 12(2), 59; https://doi.org/10.3390/batteries12020059 - 12 Feb 2026

Viewed by 924

Abstract

An innovative ionic liquid–organic hybrid electrolyte technology was developed to obtain safer and more reliable sodium-ion battery (SIB) systems. The formulation is based on the 1-ethyl-3-methyl-imidazolium bis(flurosulfonyl)imide (EMIFSI) ionic liquid combined with the Diglyme cosolvent, which showed good performance in SIBs. The hybrid [...] Read more.

An innovative ionic liquid–organic hybrid electrolyte technology was developed to obtain safer and more reliable sodium-ion battery (SIB) systems. The formulation is based on the 1-ethyl-3-methyl-imidazolium bis(flurosulfonyl)imide (EMIFSI) ionic liquid combined with the Diglyme cosolvent, which showed good performance in SIBs. The hybrid electrolyte formulation was qualified in terms of thermal and ion transport properties and electrochemical stability. The results, reported and discussed in the present manuscript, showed how it is feasible to improve conductivity without decreasing the safety and electrochemical stability of ionic liquid-based electrolytes. Full article

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

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17 pages, 5108 KB

Open AccessArticle

Optimizing Type and Thickness of Fire-Resistant Materials for Liquid Nitrogen Suppression of Lithium-Ion Battery Fires

by Dunbin Xu, Xing Deng, Lingdong Su, Xiao Zhang and Xin Xu

Batteries 2026, 12(2), 58; https://doi.org/10.3390/batteries12020058 - 10 Feb 2026

Viewed by 475

Abstract

Lithium-ion batteries are widely used in electrochemical energy storage due to their advantages such as fast response and good scalability, but they are prone to thermal runaway (TR) under abusive conditions. Liquid nitrogen has been proven effective in suppressing lithium-ion cell TR in [...] Read more.

Lithium-ion batteries are widely used in electrochemical energy storage due to their advantages such as fast response and good scalability, but they are prone to thermal runaway (TR) under abusive conditions. Liquid nitrogen has been proven effective in suppressing lithium-ion cell TR in previous studies owing to its excellent cooling capacity. To further enhance the suppression capability of liquid nitrogen on lithium-ion cell TR, a method combining liquid nitrogen with fire-resistant materials was proposed. All experiments were conducted under strictly controlled conditions to ensure result comparability. Experiments on the synergistic suppression of lithium-ion cell TR propagation were conducted with the type and thickness of the fire-resistant materials as variables. The results demonstrated that installing porous fire-resistant materials inside the lithium-ion battery module significantly enhanced the efficacy of liquid nitrogen in suppressing TR propagation. The maximum rebound temperature of the cell after nitrogen injection cessation was reduced by up to 32.7% compared to the condition without fire-resistant materials. Both material characteristics and thickness influenced the heat exchange process—ceramic fiber aerogel, with its low thermal conductivity, achieved a maximum cooling rate of 2.45 °C/s on the TR cell surface, exhibiting the optimal enhancement effect; as the material thickness increased, the synergistic fire suppression performance was further enhanced with increasing material thickness, with the 9 mm thick ceramic fiber aerogel performing better than the thinner (6 mm and 3 mm) variants. The research findings provide a valuable reference for module-level thermal runaway suppression in energy storage systems. Full article

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12 pages, 4759 KB

Open AccessArticle

A Grape-like Co₃O₄@N-Doped Graphene Oxide/N-Doped Carbon Nanotube Ternary Nanocomposite for Efficient Supercapacitor Performance

by Qianxin Liu, Yuyang Zou, Yudie Li, Peigen Wang, Yiren Chen, Gang Chen, Bo Han, Yunfeng Tian and Kaisheng Xia

Batteries 2026, 12(2), 57; https://doi.org/10.3390/batteries12020057 - 9 Feb 2026

Viewed by 403

Abstract

Cobalt-based oxides are promising candidates for supercapacitor electrodes, but their practical application is often hindered by poor electrical conductivity, limited ion diffusion, and insufficient cycling stability. Herein, we present a novel strategy to improve the electrochemical performance of Co₃O₄ by [...] Read more.

Cobalt-based oxides are promising candidates for supercapacitor electrodes, but their practical application is often hindered by poor electrical conductivity, limited ion diffusion, and insufficient cycling stability. Herein, we present a novel strategy to improve the electrochemical performance of Co₃O₄ by growing grape-like Co₃O₄ clusters on a nitrogen-doped carbon framework consisting of nitrogen-doped graphene oxide (NGO) and nitrogen-doped carbon nanotubes (NCNTs) through a controlled hydrothermal process. The nitrogen functionalities in the carbon matrix not only facilitate strong interactions between the NGO and NCNTs but also provide abundant nucleation sites for the growth of Co₃O₄ spinel nanoparticles (30–50 nm). This unique structure promotes an efficient electron conduction and ion transport network, which significantly improves the electrochemical performance of the Co₃O₄ electrode. The Co₃O₄@NGO/NCNT ternary nanocomposite, containing 39% Co₃O₄ and featuring a high specific surface area of 162 m² g⁻¹, delivers a specific capacitance of 269 F g⁻¹ at 1 A g⁻¹ and maintains 82% of its capacitance when the current density increases to 10 A g⁻¹. Notably, the nanocomposite demonstrates outstanding cycling stability, with negligible capacitance decay after 2000 charge–discharge cycles at a current density of 5 A g⁻¹, underscoring its excellent electrochemical robustness. This Co₃O₄@NGO/NCNT nanocomposite represents a promising and efficient material for high-performance supercapacitor electrodes. Full article

(This article belongs to the Section Supercapacitors)

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21 pages, 5095 KB

Open AccessArticle

A Parametric LFP Battery Degradation Model for Techno-Economic Assessment of European System-Imbalance Services

by Samuel O. Ezennaya and Julia Kowal

Batteries 2026, 12(2), 56; https://doi.org/10.3390/batteries12020056 - 8 Feb 2026

Viewed by 627

Abstract

Battery energy storage systems (BESSs) are increasingly deployed by European Balance Responsible Parties (BRPs) to mitigate system-imbalance exposure; yet, techno-economic assessments often represent degradation using fixed-lifetime or equivalent-full-cycle assumptions that obscure the dependence of wear on operating policy and sizing. This study develops [...] Read more.

Battery energy storage systems (BESSs) are increasingly deployed by European Balance Responsible Parties (BRPs) to mitigate system-imbalance exposure; yet, techno-economic assessments often represent degradation using fixed-lifetime or equivalent-full-cycle assumptions that obscure the dependence of wear on operating policy and sizing. This study develops a data-driven, parameterised degradation framework for LiFePO₄ (LFP) BESS operating under imbalance duty. Using historical imbalance datasets from five European countries spanning eight transmission system operators (TSOs), annual cycle-induced capacity loss, calendar-induced capacity loss, and total annual capacity loss at 25 °C are mapped as explicit functions of energy-to-power ratio (duration), maximum power rating, depth of discharge, state-of-charge operating bounds, and daily cycling intensity. A degree-2 Ridge specification yields compact, auditable coefficients that transfer across entities (including an out-of-time full-year hold-out for Belgium, 2025). The fitted response surfaces reveal consistent EU-wide operating regimes: cycling-dominant ageing for durations

\leq 3

h, a mixed regime for durations

\approx 3

–6 h, and calendar-dominant ageing for durations

\geq 6

h, indicating a practical compromise around ≈4–5.5 h. The resulting coefficientised outputs are Techno-Economic Assessment (TEA)-ready and enable risk-aware sizing and state-of-charge policy design for imbalance-focused BESS portfolios. Full article

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

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30 pages, 10053 KB

Open AccessArticle

A Methodological Framework for Incremental Capacity-Based Feature Engineering and Unsupervised Learning Across First-Life and Second-Life Battery Datasets

by Matthew Beatty, Dani Strickland and Pedro Ferreira

Batteries 2026, 12(2), 55; https://doi.org/10.3390/batteries12020055 - 6 Feb 2026

Viewed by 574

Abstract

Accurately assessing battery health across mixed datasets remains a challenge due to differences in chemistry, format, and usage history. This study presents a reproducible framework for preparing battery cycling data using incremental capacity analysis (ICA), with the aim of supporting machine learning (ML) [...] Read more.

Accurately assessing battery health across mixed datasets remains a challenge due to differences in chemistry, format, and usage history. This study presents a reproducible framework for preparing battery cycling data using incremental capacity analysis (ICA), with the aim of supporting machine learning (ML) workflows across both first-life and second-life battery datasets. The methodology includes IC curve generation, feature extraction, encoding and scaling, feature reduction, and unsupervised learning exploration. A two-tiered outlier detection system was introduced during preprocessing to flag edge-case samples. Two clustering algorithms, K-means and HDBSCAN, were applied to the engineered feature space to explore patterns in the IC feature space. K-means revealed broad health-related groupings with overlapping boundaries, while HDBSCAN identified finer clusters and flagged additional ambiguous samples as noise. To support interpretation, PCA and t-SNE were used to visualise the feature space in reduced dimensions. Rather than using clustering as a classification tool, the resulting cluster and noise labels are proposed as structure-aware meta-features for supervised learning. The framework accommodates heterogeneous battery datasets and addresses the challenges of integrating data from mixed sources with varying histories and characteristics. These outputs provide a structured foundation for future supervised classification of battery state of health. Full article

(This article belongs to the Special Issue Batteries: 10th Anniversary)

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19 pages, 4770 KB

Open AccessArticle

Effects of Mechanical Deformation Depth and Size on the Electrochemical Impedance Response of Large-Format Lithium-Ion Batteries

by Christoph Drießen, Jun Yin, Maximilian Schinagl, Patrick Höschele and Christian Ellersdorfer

Batteries 2026, 12(2), 54; https://doi.org/10.3390/batteries12020054 - 6 Feb 2026

Viewed by 532

Abstract

This study uses electrochemical impedance spectroscopy (EIS) to investigate coupled effects of mechanical deformation depth and size on impedance responses of large-format prismatic lithium-ion batteries (LIBs). Stepwise out-of-plane deformations were applied using hemispherical impactors of two different diameters (30 mm and 180 mm), [...] Read more.

This study uses electrochemical impedance spectroscopy (EIS) to investigate coupled effects of mechanical deformation depth and size on impedance responses of large-format prismatic lithium-ion batteries (LIBs). Stepwise out-of-plane deformations were applied using hemispherical impactors of two different diameters (30 mm and 180 mm), representing localized and global mechanical loading while maintaining consistent contact conditions. Cells were deformed to 25%, 50%, 75%, and 95% of the internal short-circuit deformation depth, with EIS measurements conducted at each level. Relative changes of measured impedance parameters and fitted equivalent circuit model (ECM) parameters were analyzed. Results show that localized deformation decreases charge transfer resistance ΔR₁ up to 8.0% and total impedance ΔZ up to 1.6%, indicating enhanced charge mobility due to internal structural damage. In contrast, global compression increases ohmic resistance ΔR₀ up to 2.1% and ΔZ up to 2.0%, likely due to reduced separator porosity. Phase angle ΔPhase showed opposite trends under localized and global loading, reflecting different capacitive responses. These results reveal that deformation depth and size significantly influence EIS measurements, with non-linear interactions and transition points indicative of irreversible damage. These results support the use of EIS as a non-destructive diagnostic tool for identifying mechanical damage in LIBs. Full article

(This article belongs to the Section Energy Storage System Aging, Diagnosis and Safety)

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31 pages, 24268 KB

Open AccessArticle

Experimental Assessment of Multi-Domain Degradation-Based Risk in NMC Lithium-Ion Batteries Under Combined Thermal and Electrical Operating Conditions

by Ziad M. Ali, Foad H. Gandoman, Faisal Aldawsari and Shady Abdel Aleem

Batteries 2026, 12(2), 53; https://doi.org/10.3390/batteries12020053 - 5 Feb 2026

Viewed by 583

Abstract

The widespread adoption of electric mobility has accelerated decarbonization in transportation applications, increasing the reliance on lithium-ion batteries (Li-IBs) in electric vehicles (EVs) and energy storage systems. To analyze battery risk under different combinations of ambient temperature, discharge C-rate, and state-of-charge (SoC) windows, [...] Read more.

The widespread adoption of electric mobility has accelerated decarbonization in transportation applications, increasing the reliance on lithium-ion batteries (Li-IBs) in electric vehicles (EVs) and energy storage systems. To analyze battery risk under different combinations of ambient temperature, discharge C-rate, and state-of-charge (SoC) windows, this study experimentally investigates power fade (PF) and capacity fade (CF) as degradation-based risk indicators. In addition to experimental observations, degradation conditions reported in previous studies are considered to identify reliable and unreliable operating zones. Several variables, including operating temperature, current rate, and SoC, influence the short- and long-term performance of Li-IBs in EV applications and should be evaluated from a safety perspective. Under combined thermal and electrical operating conditions, battery degradation progresses, associated with reductions in usable energy and power, increased internal heat generation, and increased safety risks. Due to the nonlinear behavior of Li-IBs, conventional risk models may not always fully represent battery performance; therefore, qualitative analysis and risk assessment are employed. Aging is monitored using discharge capacity, discharge energy, power rating, internal resistance, and open-circuit voltage within the proposed framework. The experimental results show that operational risk increases under high discharge C-rates combined with low ambient temperature. Discharging at 0.2 C at 25 °C with an SoC of 80% is identified as a critical operating scenario within the investigated conditions, as it results in both CF and PF. In contrast, Li-IB safety is not significantly affected under CF conditions at 4 C and 3 C at 10 °C at the same SoC level, nor under PF conditions at 0.2 C at 10 °C with SoC levels of 80% and 50%. The multi-indicator risk assessment combines individual indicators to compare operating conditions in terms of associated safety risk. Finally, the results confirm that relying on a single performance indicator tends to underestimate degradation, while a combined multi-indicator approach provides a better representation of Li-IB performance over battery lifetime. Full article

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23 pages, 2752 KB

Open AccessArticle

Deep Neural Network Optimization for Lithium-Ion Battery State of Health Prediction in Electric Vehicles: Outperforming Hybrid Models

by Saad El Fallah, Jaouad Kharbach, Jonas Vanagas, Ahmed Lakhssassi, Hassan Qjidaa and Mohammed Ouazzani Jamil

Batteries 2026, 12(2), 52; https://doi.org/10.3390/batteries12020052 - 4 Feb 2026

Viewed by 704

Abstract

It is now crucial to accurately monitor the state of health (SoH) of batteries in a setting where the use of electric vehicles (EVs) and renewable energy technologies is still growing. To solve this issue and evaluate the SoH, this paper makes use [...] Read more.

It is now crucial to accurately monitor the state of health (SoH) of batteries in a setting where the use of electric vehicles (EVs) and renewable energy technologies is still growing. To solve this issue and evaluate the SoH, this paper makes use of deep learning technology. The suggested method incorporates voltage, current, and temperature data, which are important indications of the SoH and can potentially be obtained directly from the battery management system (BMS). Although deep neural networks (DNNs) have previously been employed for SoH estimation, our study distinguishes itself by implementing a robust, completely configurable DNN application in MATLAB/Simulink R2019a. This design enables the adjustment of activation functions, layer depth, and neuron count to adapt to different battery aging conditions. To achieve optimal performance, numerous configurations were examined, highlighting the relevance of hyperparameter setting. Our technique avoids traditional feature engineering while providing a practical, adaptive, and accurate SoH estimate framework appropriate for real-world integration. The precision of the improved model was then verified against a Li-ion battery dataset with various discharge profiles given by the national aeronautics and space administration (NASA). The collected findings revealed that the proposed method is more accurate and robust than other regularly used models. The DNN model achieved a Mean absolute error (MAE) of 1.433% and a Coefficient of determination of 0.99998, outperforming previous methods such as CNN-BiGRU, which reported an MAE of 2.448% in a recent publication. This study demonstrates the reliable performance of the DNN in predicting the SoH of Li-ion cells. Full article

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

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50 pages, 5392 KB

Open AccessReview

Advances in All-Solid-State Batteries Based on Chloride Solid Electrolytes

by Lihao Tang, Zijun Cui, Fei Xie, Xiaohui Rong, Yong-Sheng Hu and Yaxiang Lu

Batteries 2026, 12(2), 51; https://doi.org/10.3390/batteries12020051 - 4 Feb 2026

Viewed by 1008

Abstract

Driven by the imperative for enhanced battery safety, solid electrolytes have emerged as a leading strategy in next-generation energy storage technologies. Beyond conventional polymer, oxide, and sulfide systems, chloride-based inorganic solid electrolytes have recently garnered significant attention due to their unique combination of [...] Read more.

Driven by the imperative for enhanced battery safety, solid electrolytes have emerged as a leading strategy in next-generation energy storage technologies. Beyond conventional polymer, oxide, and sulfide systems, chloride-based inorganic solid electrolytes have recently garnered significant attention due to their unique combination of high ionic conductivity, favorable electrochemical stability, and processability. This work presents a comprehensive review of chloride solid electrolytes, examining their crystal structures, synthesis approaches, ionic transport mechanisms, and physicochemical stability under operational conditions. Furthermore, we discuss critical considerations for integrating these materials into practical all-solid-state batteries (ASSBs), including performance across wide temperature ranges, scalable cell fabrication methods, and cost-effectiveness. By bridging fundamental material properties with device-level engineering challenges, this review aims to provide a roadmap for future research and development, highlighting the substantial promise of chloride electrolytes in enabling safe, high-performance solid-state batteries. Full article

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

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20 pages, 28542 KB

Open AccessArticle

Accurate State of Charge Estimation for Lithium-Ion Batteries Using a Temporal Convolutional Network and Bidirectional Long Short-Term Memory Hybrid Model

by Jie Qiu, Zhendong Zhang, Zehua Zhu and Chenqiang Luo

Batteries 2026, 12(2), 50; https://doi.org/10.3390/batteries12020050 - 2 Feb 2026

Viewed by 631

Abstract

Lithium-ion batteries are extensively employed in new energy vehicles, where accurate State of Charge (SOC) estimation is fundamental for optimal battery management. However, existing methods often rely on single-model approaches and fail to leverage the complementary advantages of multiple models. This study proposes [...] Read more.

Lithium-ion batteries are extensively employed in new energy vehicles, where accurate State of Charge (SOC) estimation is fundamental for optimal battery management. However, existing methods often rely on single-model approaches and fail to leverage the complementary advantages of multiple models. This study proposes an innovative hybrid estimation model integrating a Temporal Convolutional Network (TCN) that efficiently captures long-range temporal dependencies via dilated convolution and residual blocks, with a Bidirectional Long Short-Term Memory Network (BiLSTM) that extracts bidirectional context information to enhance the accuracy of SOC estimation. First, the Panasonic datasets are utilized, with current, voltage, and cell temperature selected as input features. Subsequently, the proposed model is evaluated under various temperature conditions and driving cycles, demonstrating high accuracy and robustness. Finally, comparative experiments are conducted against traditional methods, such as standalone TCN and Long Short-Term Memory (LSTM) networks, under both 10 °C and −10 °C operating conditions. The results show that the hybrid model achieves superior performance in error metrics. Specifically, based on a second-order resistor-capacitor network, at −10 °C, the Root Mean Squared Error is reduced by 0.948%, and at 10 °C, it decreases by 0.398%. Additionally, the Maximum Absolute Error is lowered by 2.751% at −10 °C and by 2.192% at 10 °C. These improvements highlight the model’s significant potential as an effective solution for SOC estimation in lithium-ion batteries. Full article

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

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24 pages, 5109 KB

Open AccessArticle

Adaptive Dual-Anchor Fusion Framework for Robust SOC Estimation and SOH Soft-Sensing of Retired Batteries with Heterogeneous Aging

by Hai Wang, Rui Liu, Yupeng Guo, Yijun Liu, Jiawei Chen, Yan Jiang and Jianying Li

Batteries 2026, 12(2), 49; https://doi.org/10.3390/batteries12020049 - 1 Feb 2026

Viewed by 454

Abstract

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to [...] Read more.

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to divergence under dynamic loads. To overcome these challenges, this paper proposes a Dual-Anchor Adaptive Fusion Framework for robust State of Charge (SOC) estimation and State of Health (SOH) soft-sensing. Specifically, to establish a reliable physical baseline, an automated Dynamic Relaxation Interval Selection (DRIS) strategy is introduced. By minimizing the fitting Root Mean Square Error (RMSE), DRIS systematically extracts high-fidelity parameters to construct two “anchor models” that rigorously define the boundaries of the aging space. Subsequently, a residual-driven Bayesian fusion mechanism is developed to seamlessly interpolate between these anchors based on real-time voltage feedback, enabling the model to adapt to uncalibrated target batteries. Concurrently, a novel “SOH Soft-Sensing” capability is unlocked by interpreting the adaptive fusion weights as real-time health indicators. Experimental results demonstrate that the proposed framework achieves robust SOC estimation with an RMSE of 0.42%, significantly outperforming the standard Adaptive Extended Kalman Filter (A-EKF, RMSE 1.53%), which exhibits parameter drift under dynamic loading. Moreover, the a posteriori voltage tracking residual is compressed to ~0.085 mV, effectively approaching the hardware’s ADC quantization limit. Furthermore, SOH is inferred with a relative error of 0.84% without additional capacity tests. This work establishes a robust methodological foundation for calibration-free state estimation in heterogeneous retired battery packs. Full article

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

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18 pages, 9292 KB

Open AccessArticle

Physics-Informed Transformer Using Degradation-Sensitive Indicators for Long-Term State-of-Health Estimation of Lithium-Ion Batteries

by Sang Hoon Park and Seon Hyeog Kim

Batteries 2026, 12(2), 48; https://doi.org/10.3390/batteries12020048 - 1 Feb 2026

Viewed by 579

Abstract

Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer [...] Read more.

Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer model is proposed for long-term SOH estimation by incorporating physically interpretable, degradation-sensitive indicators into a self-attention framework. Incremental Capacity Analysis (ICA)-derived features and thermal-gradient indicators are used as auxiliary inputs to provide physics-consistent inductive bias, enabling the model to focus on degradation-relevant regions of the charging trajectory. The proposed approach is validated using four lithium-ion battery cells exhibiting diverse aging behaviors, including severe non-linear capacity fade. Experimental results demonstrate that the proposed model consistently outperforms an LSTM baseline, achieving an RMSE below 1.5% even for the most degraded cell. Furthermore, attention map analysis reveals that the model autonomously emphasizes voltage regions associated with electrochemical phase transitions, providing clear physical interpretability. These results indicate that the proposed physics-informed Transformer offers a robust and explainable solution for battery health monitoring under practical aging conditions. Full article

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

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