Batteries

41 pages, 8472 KiB

Open AccessArticle

GITT Limitations and EIS Insights into Kinetics of NMC622

by Intizar Abbas, Huyen Tran Tran, Tran Thi Ngoc Tran, Thuy Linh Pham, Eui-Chol Shin, Chan-Woo Park, Sung-Bong Yu, Oh Jeong Lee, An-Giang Nguyen, Daeho Jeong, Bok Hyun Ka, Hoon-Hwe Cho, Jongwoo Lim, Namsoo Shin, Miran Gaberšček, Su-Mi Hur, Chan-Jin Park, Jaekook Kim and Jong-Sook Lee

Batteries 2025, 11(6), 234; https://doi.org/10.3390/batteries11060234 - 19 Jun 2025

Abstract

Conventional applications of the Galvanostatic Intermittent Titration Technique (GITT) and EIS for estimating chemical diffusivity in battery electrodes face issues such as insufficient relaxation time to reach equilibrium, excessively long pulse durations that violate the short-time diffusion assumption, and the assumption of sequential [...] Read more.

Conventional applications of the Galvanostatic Intermittent Titration Technique (GITT) and EIS for estimating chemical diffusivity in battery electrodes face issues such as insufficient relaxation time to reach equilibrium, excessively long pulse durations that violate the short-time diffusion assumption, and the assumption of sequential electrode reaction and diffusion processes. In this work, a quasi-equilibrium criterion of 0.1 mV h

^{- 1}

was applied to NMC622 electrodes, yielding 8–9 h relaxations below 3.8 V, but above 3.8 V, voltage decayed linearly and indefinitely, even upon discharging titration, showing unusual nonmonotonic relaxation behavior. The initial 36-second transients of a 10-minute galvanostatic pulse and diffusion impedance in series with the electrode reaction yielded consistent diffusivity values. However, solid-state diffusion in spherical active particles within porous electrodes, where ambipolar diffusion occurs in the pore electrolyte with

t_{+} = 0.3

, requires a physics-based three-rail transmission line model (TLM). The corrected diffusivity may be three to four times higher. An analytic two-rail TLM approximating the three-rail numerical model was applied to temperature- and frequency-dependent EIS data. This approach mitigates parameter ambiguity and unphysical correlations in EIS. Physics-based EIS enables the identification of multistep energetics and the diagnosis of performance and degradation mechanisms. Full article

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

23 pages, 1811 KiB

Open AccessArticle

Transforming Grid Systems for Sustainable Energy Futures: The Role of Energy Storage in Offshore Wind and Floating Solar

by Sajid Hussain Qazi, Marvi Dashi Kalhoro, Dimitar Bozalakov and Lieven Vandevelde

Batteries 2025, 11(6), 233; https://doi.org/10.3390/batteries11060233 - 16 Jun 2025

Abstract

Integrating offshore renewable energy (ORE) into power systems is vital for sustainable energy transitions. This paper examines the challenges and opportunities in integrating ORE, focusing on offshore wind and floating solar, into grid systems. A simulation was conducted using a 5 MW offshore [...] Read more.

Integrating offshore renewable energy (ORE) into power systems is vital for sustainable energy transitions. This paper examines the challenges and opportunities in integrating ORE, focusing on offshore wind and floating solar, into grid systems. A simulation was conducted using a 5 MW offshore wind turbine and a 2 MW floating PV (FPV) system, complemented by a 10 MWh battery energy storage system (BESS). The simulation utilized the typical load profile of Belgium and actual 2023 electricity price data, along with realistic wind and solar generation patterns for a location at the sea border of Belgium and the Netherlands. The use of real operational and market data ensures the practical relevance of the results. This study highlights the importance of BESS, targeting a significant revenue by participating in system imbalance and providing ancillary services (aFRR and mFRR). Key findings emphasize the need for grid infrastructure transformation to support ORE’s growing investments and deployment. This research underscores the essential role of technological innovation and strategic planning in optimizing the potential of ORE sources. Full article

► Show Figures

Figure 1

86 pages, 12164 KiB

Open AccessReview

Empowering the Future: Cutting-Edge Developments in Supercapacitor Technology for Enhanced Energy Storage

by Mohamed Salaheldeen, Thomas Nady A. Eskander, Maher Fathalla, Valentina Zhukova, Juan Mari Blanco, Julian Gonzalez, Arcady Zhukov and Ahmed M. Abu-Dief

Batteries 2025, 11(6), 232; https://doi.org/10.3390/batteries11060232 - 16 Jun 2025

Abstract

The accelerating global demand for sustainable and efficient energy storage has driven substantial interest in supercapacitor technology due to its superior power density, fast charge–discharge capability, and long cycle life. However, the low energy density of supercapacitors remains a key bottleneck, limiting their [...] Read more.

The accelerating global demand for sustainable and efficient energy storage has driven substantial interest in supercapacitor technology due to its superior power density, fast charge–discharge capability, and long cycle life. However, the low energy density of supercapacitors remains a key bottleneck, limiting their broader application. This review provides a comprehensive and focused overview of the latest breakthroughs in supercapacitor research, emphasizing strategies to overcome this limitation through advanced material engineering and device design. We explore cutting-edge developments in electrode materials, including carbon-based nanostructures, metal oxides, redox-active polymers, and emerging frameworks such as metal–organic frameworks (MOFs) and covalent organic frameworks (COFs). These materials offer high surface area, tunable porosity, and enhanced conductivity, which collectively improve the electrochemical performance. Additionally, recent advances in electrolyte systems—ranging from aqueous to ionic liquids and organic electrolytes—are critically assessed for their role in expanding the operating voltage window and enhancing device stability. The review also highlights innovations in device architectures, such as hybrid, asymmetric, and flexible supercapacitor configurations, that contribute to the simultaneous improvement of energy and power densities. We identify persistent challenges in scaling up nanomaterial synthesis, maintaining long-term operational stability, and integrating materials into practical energy systems. By synthesizing these state-of-the-art advancements, this review outlines a roadmap for next-generation supercapacitors and presents novel perspectives on the synergistic integration of materials, electrolytes, and device engineering. These insights aim to guide future research toward realizing high-energy, high-efficiency, and scalable supercapacitor systems suitable for applications in electric vehicles, renewable energy storage, and next-generation portable electronics. Full article

(This article belongs to the Special Issue High-Performance Super-capacitors: Preparation and Application)

► Show Figures

Graphical abstract

21 pages, 4572 KiB

Open AccessArticle

Enhancing Grid Stability in Microgrid Systems with Vehicle-to-Grid Support and EDLC Supercapacitors

by Adrián Criollo, Dario Benavides, Paul Arévalo, Luis I. Minchala-Avila and Diego Morales-Jadan

Batteries 2025, 11(6), 231; https://doi.org/10.3390/batteries11060231 - 15 Jun 2025

Abstract

Grid stability in microgrids represents a critical challenge, particularly with the increasing integration of variable renewable energy sources and the loss of systematic inertia. This study analyzes the use of vehicle-to-grid (V2G) technology and supercapacitors as complementary solutions to improve grid stability. A [...] Read more.

Grid stability in microgrids represents a critical challenge, particularly with the increasing integration of variable renewable energy sources and the loss of systematic inertia. This study analyzes the use of vehicle-to-grid (V2G) technology and supercapacitors as complementary solutions to improve grid stability. A hybrid approach is proposed in which electric vehicles act as temporary storage units, supplying energy to regulate grid frequency. Supercapacitors, due to their rapid charging and discharging capabilities, are used to mitigate power fluctuations and provide immediate support during peak demand. The proposed management model integrates two strategies for frequency control, leveraging the linear relationship between power and frequency. Power smoothing is combined with Kalman filter-based frequency control, allowing for accurate estimation of the dynamic system state, even in the presence of noise or load fluctuations. This methodology improves grid stability and frequency regulation accuracy. A frequency variability analysis is also included, highlighting grid disturbance events related to renewable-energy penetration and demand changes. Furthermore, the effectiveness of the Kalman filter in improving grid stability control, ensuring an efficient dynamic response, is highlighted. The results obtained demonstrate that the combination of V2G and supercapacitors contributes significantly to reducing grid disturbances, optimizing energy efficiency, and enhancing system reliability. Full article

(This article belongs to the Special Issue Innovations in Batteries for Renewable Energy Storage in Remote Areas)

► Show Figures

Figure 1

34 pages, 1734 KiB

Open AccessReview

Progress, Challenges and Opportunities in Recycling Electric Vehicle Batteries: A Systematic Review Article

by Hamid Safarzadeh and Francesco Di Maria

Batteries 2025, 11(6), 230; https://doi.org/10.3390/batteries11060230 - 13 Jun 2025

Abstract

Objective: The rapid growth of electric vehicle (EV) adoption has led to an unprecedented increase in lithium-ion battery (LIB) demand and end-of-life waste, underscoring the urgent need for effective recycling strategies. This review evaluates current progress in EV battery recycling and explores future [...] Read more.

Objective: The rapid growth of electric vehicle (EV) adoption has led to an unprecedented increase in lithium-ion battery (LIB) demand and end-of-life waste, underscoring the urgent need for effective recycling strategies. This review evaluates current progress in EV battery recycling and explores future prospects. Design: Review based on PRISMA 2020. Data sources: Scientific publications indexed in major databases such as Scopus, Web of Science, and ScienceDirect were searched for relevant studies published between 2020 and 15 April 2025. Inclusion criteria: Studies were included if they were published in English between 2020 and 15 April 2025, and focused on the recycling of electric vehicle batteries. Eligible studies specifically addressed (i) recycling methods, technologies, and material recovery processes for EV batteries; (ii) the impact of recycled battery systems on power generation processes and grid stability; and (iii) assessments of materials used in battery manufacturing, including efficiency and recyclability. Review articles and meta-analyses were excluded to ensure the inclusion of only original research data. Data extraction: Data were independently screened and extracted by two researchers and analyzed for recovery rates, environmental impact, and system-level energy contributions. One researcher independently screened all articles and extracted relevant data. A second researcher validated the accuracy of extracted data. The data were then organized and analyzed based on reported quantitative and qualitative indicators related to recycling methods, material recovery rates, environmental impact, and system-level energy benefits. Results: A total of 23 studies were included. Significant progress has been made in hydrometallurgical and direct recycling processes, with recovery rates of critical metals (Li, Co, Ni) improving. Second-life battery applications also show promise for grid stabilization and renewable energy storage. Furthermore, recycled batteries show potential in stabilizing power grids through second-life applications in BESS. Conclusion: EV battery recycling is a vital strategy for addressing raw material scarcity, minimizing environmental harm, and supporting energy resilience. However, challenges persist in policy harmonization, technology scaling, and economic viability. Future progress will depend on integrated efforts across sectors and regions to build a circular battery economy. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Second-Life Applications and Recycling of Li-Ion Batteries)

► Show Figures

Graphical abstract

59 pages, 11235 KiB

Open AccessReview

A Review of EV Adoption, Charging Standards, and Charging Infrastructure Growth in Europe and Italy

by Mahwish Memon and Claudio Rossi

Batteries 2025, 11(6), 229; https://doi.org/10.3390/batteries11060229 - 12 Jun 2025

Abstract

This work analyzes the electric vehicle (EV) sales trends of plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) and trends in the growth of Alternating Current (AC) and Direct Current (DC) charging infrastructure station scenarios in Europe and Italy. It offers [...] Read more.

This work analyzes the electric vehicle (EV) sales trends of plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs) and trends in the growth of Alternating Current (AC) and Direct Current (DC) charging infrastructure station scenarios in Europe and Italy. It offers a comprehensive view of market trends, technical developments, infrastructure development, and worldwide standardization initiatives for policymakers, researchers, and industry. A detailed classification of the charging technologies of EVs, i.e., conductive, wireless power transfer (WPT), battery swapping (BS), and different EV types, is presented. Finally, this work provides a comparative overview of charging standards and protocols, including the ones established by the Society of Automotive Engineers (SAE), International Electrotechnical Commission (IEC), and Standardization Administration of China (SAC), emphasizing interoperability and cross-border integration to accelerate the transition to clean transportation. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges)

► Show Figures

Graphical abstract

11 pages, 2225 KiB

Open AccessArticle

Electrochemical Performance of Diamond-like Carbon (DLC)-Coated Zn Anodes for Application to Aqueous Zinc-Ion Batteries

by Jinyoung Lee, Eunseo Lee and Sungwook Mhin

Batteries 2025, 11(6), 228; https://doi.org/10.3390/batteries11060228 - 12 Jun 2025

Abstract

The increasing demand for safe, cost-effective, and sustainable energy storage solutions has spotlighted aqueous zinc-ion batteries (AZIBs) as promising alternatives to lithium-ion systems. However, the practical deployment of AZIBs remains hindered by dendritic growth, hydrogen evolution, and surface corrosion at the zinc metal [...] Read more.

The increasing demand for safe, cost-effective, and sustainable energy storage solutions has spotlighted aqueous zinc-ion batteries (AZIBs) as promising alternatives to lithium-ion systems. However, the practical deployment of AZIBs remains hindered by dendritic growth, hydrogen evolution, and surface corrosion at the zinc metal anode, which severely compromise electrochemical stability. In this study, we propose an interfacial engineering strategy involving ultrathin diamond-like carbon (DLC) coatings applied to Zn anodes. The DLC films serve as conformal, ion-permeable barriers that mitigate parasitic side reactions and facilitate uniform Zn plating/stripping behavior. Materials characterizations of the DLC layer on the Zn anodes revealed the tunability of sp²/sp³ hybridization and surface morphology depending on DLC thickness. Electrochemical impedance spectroscopy demonstrated a significant reduction in interfacial resistance, particularly in the optimally coated sample (DLC2, ~20 nm), which achieved a favorable balance between mechanical integrity and ionic transport. Symmetric-cell tests confirmed enhanced cycling stability over 160 h, while full-cell configurations with an ammonium vanadate nanofiber-based cathode exhibited superior capacity retention over 900 cycles at 2 A g⁻¹. The DLC2-coated Zn anodes demonstrated the most effective performance, attributable to its moderate surface roughness, reduced disorder, and minimized charge-transfer resistance. These results provide insight into the importance of fine-tuning the DLC thickness and carbon bonding structure for suppressing dendrite formation and enhancing electrochemical stability. Full article

(This article belongs to the Special Issue Multifunctional Electrode Materials for Sustainable Energy Storage Devices)

► Show Figures

Graphical abstract

16 pages, 5211 KiB

Open AccessArticle

Measurement of Battery Aging Using Impedance Spectroscopy with an Embedded Multisine Coherent Measurement System

by Jorge Lourenço, Luis S. Rosado, Pedro M. Ramos and Fernando M. Janeiro

Batteries 2025, 11(6), 227; https://doi.org/10.3390/batteries11060227 - 10 Jun 2025

Abstract

This work describes the development of an embedded standalone measurement system that monitors the aging of batteries using impedance spectroscopy. The system generates a multisine stimulus that contains the frequency components at which the battery impedance is measured. Coherent generation and sampling is [...] Read more.

This work describes the development of an embedded standalone measurement system that monitors the aging of batteries using impedance spectroscopy. The system generates a multisine stimulus that contains the frequency components at which the battery impedance is measured. Coherent generation and sampling is assured, and Goertzel filters, one for each measurement frequency, are updated with each new sample. This architecture reduces memory requirements because the current and voltage of the measured samples are discarded after processing. Aging is monitored, as the system is able to automatically perform complete or partial charge/discharge cycles as well as measurement cycles without requiring user interaction. Full article

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

► Show Figures

Figure 1

27 pages, 4890 KiB

Open AccessArticle

Lifetime Prediction Analysis of Proton Exchange Membrane Fuel Cells Based on Empirical Mode Decomposition—Temporal Convolutional Network

by Chao Zheng, Changqing Du, Jiaming Zhang, Yiming Zhang, Jun Shen and Jiaxin Huang

Batteries 2025, 11(6), 226; https://doi.org/10.3390/batteries11060226 - 9 Jun 2025

Abstract

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essential [...] Read more.

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essential for enhancing longevity. Automotive PEMFC systems are complex and nonlinear, making lifespan prediction difficult. Recent studies suggest deep learning approaches hold promise for this task. This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. Parametric analysis shows optimal accuracy with the grouping parameter set to 4. Experimental validation, with a voltage lifetime threshold at 96% (3.228 V), shows the predicted degradation closely aligns with actual results. The model predicts voltage threshold times at 809 h and 876 h, compared to actual values of 807 h and 872 h, with a temporal prediction error margin of 0.250–0.460%. These results demonstrate the model’s high prediction fidelity and support proactive health management of PEMFC systems. Full article

► Show Figures

Figure 1

27 pages, 7013 KiB

Open AccessFeature PaperArticle

Detailed Characterization of Thermal Runaway Particle Emissions from a Prismatic NMC622 Lithium-Ion Battery

by Felix Elsner, Peter Gerhards, Gaël Berrier, Rémi Vincent, Sébastien Dubourg and Stefan Pischinger

Batteries 2025, 11(6), 225; https://doi.org/10.3390/batteries11060225 - 9 Jun 2025

Abstract

Particles ejected during thermal runaway (TR) of lithium-ion batteries carry a significant fraction of the total TR energy and can cause danger to other components in the battery system. The associated safety hazards should be addressed in the battery pack development process, which [...] Read more.

Particles ejected during thermal runaway (TR) of lithium-ion batteries carry a significant fraction of the total TR energy and can cause danger to other components in the battery system. The associated safety hazards should be addressed in the battery pack development process, which requires a deep understanding of TR particle characteristics. In this study, these characteristics are determined by applying several measurement techniques. Among them, dynamic image analysis and large particle image processing are applied to battery abuse particles for the first time, allowing their size and shape to be quantified in detail. Particles are collected from three overheating tests on a prismatic 51 Ah NMC622 cell under vacuum conditions in an autoclave environment. Battery abuse particles cover a wide size range, from micrometers to millimeters, with the largest particle reaching 51.4 mm. They are non-spherical, whereby sphericity, symmetry, and aspect ratio decrease for larger particles. Re-solidified copper droplets and intact separator pieces indicate particle temperatures of ~200–1100 °C at the time of cell ejection. Particles are partially combustible, with an exothermic onset at ~500 °C associated with graphite oxidation. Reactivity is non-linearly size dependent. Implications of these findings for battery system development are discussed. Full article

► Show Figures

Figure 1

22 pages, 6853 KiB

Open AccessArticle

Optimization of Battery Thermal Management for Real Vehicles via Driving Condition Prediction Using Neural Networks

by Haozhe Zhang, Jiashun Zhang, Tianchang Song, Xu Zhao, Yulong Zhang and Shupeng Zhao

Batteries 2025, 11(6), 224; https://doi.org/10.3390/batteries11060224 - 8 Jun 2025

Abstract

In the context of the global energy transition, thermal management of electric vehicle batteries faces severe challenges due to temperature rise and energy consumption under dynamic operating conditions. Traditional strategies rely on real-time feedback and suffer from response lag and energy efficiency imbalance. [...] Read more.

In the context of the global energy transition, thermal management of electric vehicle batteries faces severe challenges due to temperature rise and energy consumption under dynamic operating conditions. Traditional strategies rely on real-time feedback and suffer from response lag and energy efficiency imbalance. In this study, we propose a neural network-based synergistic optimization method for driving conditions prediction and dynamic thermal management, which collects multi-scenario real-vehicle data (358 60-s condition segments) by naturalistic driving data collection method, extracts four typical conditions (congestion, highway, urban, and suburbia) by combining with K-means clustering, and constructs a BP (backpropagation neural network) model (20 neurons in the input layer and 60 neurons in the output layer) to predict the vehicle speed in the next 60 s. Based on the prediction results, the coupled PID control and temperature feedback mechanism dynamically adjusts the coolant flow rate (maximum reduction of 17.6%), which reduces the maximum temperature of the battery by 3.8 °C, the maximum temperature difference by 0.3 °C, and the standard deviation of temperature fluctuation at ambient temperatures of 25~40 °C is 0.2 °C in AMESim simulation and experimental validation. The results show that the strategy significantly improves battery safety and system economy under complex working conditions by prospectively optimizing heat dissipation and energy consumption, providing an efficient solution for intelligent thermal management. Full article

(This article belongs to the Special Issue Batteries Safety and Thermal Management for Electric Vehicles)

► Show Figures

Graphical abstract

20 pages, 5554 KiB

Open AccessArticle

The Application of BiGRU-MSTA Based on Multi-Scale Temporal Attention Mechanism in Predicting the Remaining Life of Lithium-Ion Batteries

by Luping Wang and Shanze Wang

Batteries 2025, 11(6), 223; https://doi.org/10.3390/batteries11060223 - 7 Jun 2025

Abstract

Lithium-ion batteries are an indispensable component of numerous contemporary applications, such as electric vehicles and renewable energy systems. However, accurately predicting their remaining service life is a significant challenge due to the complexity of degradation patterns and time series data. To tackle these [...] Read more.

Lithium-ion batteries are an indispensable component of numerous contemporary applications, such as electric vehicles and renewable energy systems. However, accurately predicting their remaining service life is a significant challenge due to the complexity of degradation patterns and time series data. To tackle these challenges, this study introduces a novel Multi-Scale Time Attention (MSTA) mechanism designed to enhance the modeling of both short-term fluctuations and long-term degradation trends in battery performance. This mechanism is integrated with the Bidirectional Gated Recurrent Unit (BiGRU) to develop the BiGRU-MSTA framework. This framework effectively captures multi-scale temporal features and enhances prediction accuracy, even with limited training data. The BiGRU-MSTA model is evaluated via two sets of experiments. First, using the NASA lithium-ion battery dataset, the experimental results demonstrate that the proposed model outperforms the LSTM, BiGRU, CNN-LSTM, and BiGRU-Attention models across all evaluation metrics. Second, experiments conducted on the CALCE dataset not only examine the impact of varying time scales within the MSTA mechanism but also compare the model against state-of-the-art architectures such as Transformer and LSTM–Transformer. The findings indicate that the BiGRU-MSTA model exhibits significantly superior performance in terms of prediction accuracy and stability. These experimental results underscore the potential of the BiGRU-MSTA model for application in battery management systems and sustainable energy storage solutions. Full article

► Show Figures

Figure 1

15 pages, 2144 KiB

Open AccessArticle

Optimizing Porous Transport Layers in PEM Water Electrolyzers: A 1D Two-Phase Model

by Lu Zhang, Jie Liu and Shaojie Du

Batteries 2025, 11(6), 222; https://doi.org/10.3390/batteries11060222 - 6 Jun 2025

Abstract

The proton exchange membrane electrolyzer (PEMWE) has been regarded as a promising technology for converting surplus intermittent renewable energy into green hydrogen through electrochemical water splitting. However, the multiphase mass and charge transport processes with countercurrent flow within the PEMWE create complex structure–property [...] Read more.

The proton exchange membrane electrolyzer (PEMWE) has been regarded as a promising technology for converting surplus intermittent renewable energy into green hydrogen through electrochemical water splitting. However, the multiphase mass and charge transport processes with countercurrent flow within the PEMWE create complex structure–property relationships that are difficult to optimize. The interdependent effects of multiple structural parameters on the coupled heat transfer, mass transfer, and charge transfer processes further obscure performance optimization mechanisms. To decouple these phenomena and elucidate the underlying mechanisms, a multiphase one-dimensional mathematical model was developed and experimentally validated. Based on the model, the mass transfer, charge conduction, and heat transfer processes inside the PEMWE have been systematically investigated, with a particular focus on the performance-related parameters of the porous transport layer (PTL). The results reveal that PTL thickness and porosity exhibit opposite effects on activation and ohmic overpotential at an elevated current density. Furthermore, a sharp performance decline occurs when PTL gas permeability falls below the critical threshold. These findings provide quantitative guidelines for multiphysics-informed component optimization in high-performance PEMWEs. Full article

(This article belongs to the Special Issue Challenges, Progress, and Outlook of High-Performance Fuel Cells)

► Show Figures

Figure 1

28 pages, 5131 KiB

Open AccessArticle

Early Remaining Useful Life Prediction for Lithium-Ion Batteries Using a Gaussian Process Regression Model Based on Degradation Pattern Recognition

by Linlin Fu, Bo Jiang, Jiangong Zhu, Xuezhe Wei and Haifeng Dai

Batteries 2025, 11(6), 221; https://doi.org/10.3390/batteries11060221 - 6 Jun 2025

Abstract

Lithium-ion batteries experience nonlinear degradation characteristics during long-term operation. Accurate estimation of their remaining useful life (RUL) is of significant importance for early fault diagnosis and residual value evaluation. However, existing RUL prediction approaches often suffer from limited accuracy and insufficient specificity. To [...] Read more.

Lithium-ion batteries experience nonlinear degradation characteristics during long-term operation. Accurate estimation of their remaining useful life (RUL) is of significant importance for early fault diagnosis and residual value evaluation. However, existing RUL prediction approaches often suffer from limited accuracy and insufficient specificity. To address these limitations, this study proposes an RUL prediction methodology based on Gaussian process regression, which incorporates degradation pattern recognition and auxiliary features derived from knee points. First, 9 health-related features are extracted from the first 100 charge/discharge cycles of the battery. Based on these extracted features, clustering and classification techniques are employed to categorize the batteries into three distinct degradation patterns. Moreover, feature importance is assessed to identify and eliminate redundant indicators, thereby enhancing the relevance of the feature set for prediction. Subsequently, for each degradation pattern, GPR-based models with composite kernel functions are constructed by integrating knee point positions and their corresponding slopes. The model hyperparameters are further optimized through the particle swarm optimization (PSO) algorithm to improve the adaptability and generalization capability of the predictive models. Experimental results demonstrate that the proposed method achieves a high level of predictive accuracy, with an overall mean absolute percentage error (MAPE) of approximately 8.70%. Furthermore, compared with conventional prediction methods, the proposed approach exhibits superior performance and can serve as an effective evaluation tool for diverse application scenarios, including lithium-ion battery health monitoring, early prognostics, and echelon utilization. Full article

(This article belongs to the Special Issue State-of-Health Estimation of Batteries)

► Show Figures

Graphical abstract

29 pages, 4963 KiB

Open AccessReview

Protective Layer and Current Collector Design for Interface Stabilization in Lithium-Metal Batteries

by Dayoung Kim, Cheolhwan Song and Oh B. Chae

Batteries 2025, 11(6), 220; https://doi.org/10.3390/batteries11060220 - 5 Jun 2025

Abstract

Recent advancements in lithium-metal-based battery technology have garnered significant attention, driven by the increasing demand for high-energy storage devices such as electric vehicles (EVs). Lithium (Li) metal has long been considered an ideal negative electrode due to its high theoretical specific capacity (3860 [...] Read more.

Recent advancements in lithium-metal-based battery technology have garnered significant attention, driven by the increasing demand for high-energy storage devices such as electric vehicles (EVs). Lithium (Li) metal has long been considered an ideal negative electrode due to its high theoretical specific capacity (3860 mAh g⁻¹) and low redox potential. However, the commercialization of Li-metal batteries (LMBs) faces significant challenges, primarily related to the safety and cyclability of the negative electrodes. The formation of lithium dendrites and uneven solid electrolyte interphases, along with volumetric expansion during cycling, severely hinder the commercial viability of LMBs. Among the various strategies developed to overcome these challenges, the introduction of artificial protective layers and the structural engineering of current collectors have emerged as highly promising approaches. These techniques are critical for regulating Li deposition behavior, mitigating dendrite growth, and enhancing interfacial and mechanical stability. This review summarizes the current state of Li-negative electrodes and introduces methods of enhancing their performance using a protective layer and current collector design. Full article

(This article belongs to the Special Issue Optimized Structure and Electrolyte Design for High-Performance Lithium Metal Batteries)

► Show Figures

Figure 1

15 pages, 2005 KiB

Open AccessArticle

Multi-Level Thermal Modeling and Management of Battery Energy Storage Systems

by Zhe Lv, Zhonghao Sun, Lei Wang, Qi Liu and Jianbo Zhang

Batteries 2025, 11(6), 219; https://doi.org/10.3390/batteries11060219 - 2 Jun 2025

Abstract

With the accelerating global transition toward sustainable energy, the role of battery energy storage systems (ESSs) becomes increasingly prominent. This study employs the isothermal battery calorimetry (IBC) measurement method and computational fluid dynamics (CFD) simulation to develop a multi-domain thermal modeling framework for [...] Read more.

With the accelerating global transition toward sustainable energy, the role of battery energy storage systems (ESSs) becomes increasingly prominent. This study employs the isothermal battery calorimetry (IBC) measurement method and computational fluid dynamics (CFD) simulation to develop a multi-domain thermal modeling framework for battery systems, spanning from individual cells to modules, clusters, and ultimately the container level. Experimental validation confirms the model’s accuracy, with the simulated maximum cell temperature of 36.2 °C showing only a 1.8 °C deviation from the measured value of 34.4 °C under real-world operating conditions. Furthermore, by integrating on-site calibrated thermodynamic parameters of the container, a battery system energy efficiency model is established. Combined with the battery aging engineering model, a coupled lifetime–energy efficiency model is constructed. Six different control strategies are simulated and analyzed to quantify the system’s comprehensive lifecycle benefits. The results demonstrate that the optimized control strategy enhances the overall energy storage station revenue by 2.63%, yielding an additional cumulative profit of CNY 13.676 million over the entire lifecycle. This research provides an effective simulation framework and decision-making basis for the thermal management optimization and economic evaluation of battery ESSs. Full article

► Show Figures

Figure 1

17 pages, 5129 KiB

Open AccessArticle

Quantification of Degradation Processes in Lithium-Ion Batteries Through Internal Strain Measurement with Fiber Bragg Grating Sensors

by Leonard Kropkowski, Tim Oestreich, Fangqi Li, Alexandra Burger, Antonio Nedjalkov, Andreas Würsig and Wolfgang Schade

Batteries 2025, 11(6), 218; https://doi.org/10.3390/batteries11060218 - 1 Jun 2025

Abstract

An important aspect of lithium-ion batteries related to lifetime and aging is the change in state within the cells, which results from the expansion of the electrode materials and causes internal stress during operation. In this work, fiber optical sensors by means of [...] Read more.

An important aspect of lithium-ion batteries related to lifetime and aging is the change in state within the cells, which results from the expansion of the electrode materials and causes internal stress during operation. In this work, fiber optical sensors by means of Bragg gratings are utilized to determine the internal strain in the anode material. The collected data were employed to approximate aging-related changes in anode strain using a combination of established methods, such as the differential voltage and incremental capacity analysis. Moreover, additional methodologies are proposed and explored, substituting electrical data with optical strain measurements to quantify degradation effects linked to changes in strain. During the cycling of the cell, changes in the strain behavior have been observed and can be partially attributed to changes in the cell’s electrochemical composition. The methods suggested have proven effective in providing additional insights into the current state of the cells and tracking changes over time due to detected degradation effects. Full article

► Show Figures

Figure 1

28 pages, 3512 KiB

Open AccessArticle

State-of-Health Estimation for Lithium-Ion Batteries via Incremental Energy Analysis and Hybrid Deep Learning Model

by Yan Zhang, Anxiang Wang, Chaolong Zhang, Peng He, Kui Shao, Kaixin Cheng and Yujie Zhou

Batteries 2025, 11(6), 217; https://doi.org/10.3390/batteries11060217 - 1 Jun 2025

Abstract

Accurate State-of-Health (SOH) estimation is a key technology for ensuring battery safety, optimizing energy management, and enhancing lifecycle value. This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional [...] Read more.

Accurate State-of-Health (SOH) estimation is a key technology for ensuring battery safety, optimizing energy management, and enhancing lifecycle value. This paper proposes a novel SOH estimation method for lithium-ion batteries, utilizing incremental energy features and a hybrid deep learning model that combines Convolutional Neural Network (CNN), Kolmogorov–Arnold Network (KAN), and Bidirectional Long Short-Term Memory (BiLSTM) (CNN-KAN-BiLSTM). First, the battery’s voltage, current, temperature, and other data during the charging stage were measured and recorded through experiments. Incremental Energy Analysis (IEA) was conducted on the charging data to extract various incremental energy characteristics. The Pearson correlation method was used to verify the strong correlation between the proposed characteristics and the battery SOH. This paper includes experimental verification of the method for both battery cells and battery pack. For the battery cell, a complete multi-feature sequence was formed based on the incremental energy curve characteristics combined with temperature characteristics. For the battery pack, the characteristics of the incremental energy curve were supplemented with Variance of Voltage Means (VVM) as an inconsistent feature, combined with Standard Deviation of Temperature Means (SDTM), to create a complete multi-feature sequence. The features were then input into the CNN-KAN-BiLSTM deep learning model developed in this study for training, successfully estimating the SOH of lithium batteries. The results demonstrate that the proposed method can accurately estimate the SOH of lithium batteries, even though the SOH degradation of lithium batteries has significant nonlinear characteristics. The Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for the lithium battery pack were 0.3910 and 0.4797, respectively, with an average coefficient of determination (R²) exceeding 99%. The final SOH estimation MAE values for battery cells at different charging rates of 0.1 C (250 mA), 0.2 C (500 mA), and 0.5 C (1250 mA) were 0.2728, 0.3301, and 0.2094. The RMSE were 0.3792, 0.4494, and 0.2699, respectively. The corresponding R² values were 98.76%, 97.07%, and 99.37%, respectively. Finally, the effectiveness and universality of the method proposed in this paper were verified using the NASA battery dataset and the CALCE battery dataset. Full article

► Show Figures

Figure 1

28 pages, 5473 KiB

Open AccessReview

Advances in the Battery Thermal Management Systems of Electric Vehicles for Thermal Runaway Prevention and Suppression

by Le Duc Tai and Moo-Yeon Lee

Batteries 2025, 11(6), 216; https://doi.org/10.3390/batteries11060216 - 1 Jun 2025

Abstract

In response to the global imperative to reduce greenhouse gas emissions and fossil fuel dependency, electric vehicles (EVs) have emerged as a sustainable transportation alternative, primarily utilizing lithium-ion batteries (LIBs) due to their high energy density and efficiency. However, LIBs are highly sensitive [...] Read more.

In response to the global imperative to reduce greenhouse gas emissions and fossil fuel dependency, electric vehicles (EVs) have emerged as a sustainable transportation alternative, primarily utilizing lithium-ion batteries (LIBs) due to their high energy density and efficiency. However, LIBs are highly sensitive to temperature fluctuations, significantly affecting their performance, lifespan, and safety. One of the most critical threats to the safe operation of LIBs is thermal runaway (TR), an uncontrollable exothermic process that can lead to catastrophic failure under abusive conditions. Moreover, thermal runaway propagation (TRP) can rapidly spread failures across battery cells, intensifying safety threats. To address these challenges, developing advanced battery thermal management systems (BTMS) is essential to ensure optimal temperature control and suppress TR and TRP within LIB modules. This review systematically evaluates advanced cooling strategies, including indirect liquid cooling, water mist cooling, immersion cooling, phase change material (PCM) cooling, and hybrid cooling based on the latest studies published between 2020 and 2025. The review highlights their mechanisms, effectiveness, and practical considerations for preventing TR initiation and suppressing TRP in battery modules. Finally, key findings and future directions for designing next-generation BTMS are proposed, contributing valuable insights for enhancing the safety and reliability of LIB applications. Full article

(This article belongs to the Special Issue Thermal Management in Lithium-Ion Batteries: Latest Advances and Prospects)

► Show Figures

Figure 1

Journal Description

Batteries

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI