Batteries

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 (registering DOI) - 12 Jun 2025

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)

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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)

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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

Viewed by 82

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)

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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

Viewed by 61

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

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27 pages, 7013 KiB

Open AccessArticle

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

Viewed by 41

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

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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

Viewed by 222

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)

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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

Viewed by 242

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

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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

Viewed by 143

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)

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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

Viewed by 216

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)

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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

Viewed by 369

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)

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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

Viewed by 338

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

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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

Viewed by 356

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

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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

Viewed by 362

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

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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

Viewed by 432

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)

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3 pages, 146 KiB

Open AccessEditorial

Towards a Smarter Battery Management System

by Zhi Cao, Naser Vosoughi Kurdkandi and Chris Mi

Batteries 2025, 11(6), 215; https://doi.org/10.3390/batteries11060215 - 31 May 2025

Viewed by 225

Abstract

Batteries play a critical role in achieving a sustainable energy future, enabling the integration of renewable energy sources and supporting electrified transportation and smart grids [...] Full article

(This article belongs to the Special Issue Towards a Smarter Battery Management System: 2nd Edition)

14 pages, 3834 KiB

Open AccessArticle

Comparative Study of Thermal Runaway Propagation and Material Barrier Effect of Lithium-Ion Batteries

by Yikai Mao, Yaoyu Chen, Yanglin Ye, Yin Chen and Mingyi Chen

Batteries 2025, 11(6), 214; https://doi.org/10.3390/batteries11060214 - 29 May 2025

Viewed by 223

Abstract

Battery thermal runaway (TR) is usually accompanied by a large amount of heat release, as well as a jet of flame. This not only causes harm to the surrounding environment but even exacerbates thermal runaway propagation (TRP). At this stage, many types of [...] Read more.

Battery thermal runaway (TR) is usually accompanied by a large amount of heat release, as well as a jet of flame. This not only causes harm to the surrounding environment but even exacerbates thermal runaway propagation (TRP). At this stage, many types of materials are used to suppress TRP, and people tend to focus on improving one characteristic of the material while ignoring other properties of the material. This may leave potential pitfalls for TRP suppression, suggesting the need to study multiple properties of multiple materials. In order to better weigh the advantages and disadvantages of different types of materials when suppressing TRP, we compared three typical materials for suppressing TRP behavior in lithium-ion batteries (LIBs). These materials are phase change materials (PCM), ceramic fibers, and glass fibers. They are all available in two different thicknesses, 2 mm and 3 mm. The experiments started with a comparative analysis of the TR experimental phenomena in the presence of the different materials. Then, the temperature and mass loss of the battery module during TR were analyzed separately and comparatively. The 3 mm glass fiber showed the best inhibition effect, which extended the TR interval between cells 1 and 2 to 894 s and successfully inhibited the TR of cell 3. Compared with the blank group, the total mass loss decreased from 194.3 g to 182.2 g, which is a 6.2% reduction. Subsequently, we comprehensively analyzed the performance of the three materials in suppressing TRP by combining their suppressing mechanisms. The experimental results show that glass fiber has the best effect in suppressing TRP due to its excellent thermal insulation and mechanical properties. This study may provide new insights into how to trade-off material properties for TRP suppression in the future. Full article

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

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14 pages, 6850 KiB

Open AccessArticle

Improving Electrochemical Performance of Cobalt Hexacyanoferrate as Magnesium Ion Battery Cathode Material by Nickel Doping

by Jinxing Wang, Peiyang Zhang, Jiaxu Wang, Guangsheng Huang, Jingfeng Wang and Fusheng Pan

Batteries 2025, 11(6), 213; https://doi.org/10.3390/batteries11060213 - 29 May 2025

Viewed by 286

Abstract

Magnesium metal has a high theoretical volume capacity and abundant reserves. Magnesium ion battery is theoretically secure and eco-friendly. In recent years, magnesium ion battery has attracted wide attention and is expected to become a competitive energy storage candidate in the next generation. [...] Read more.

Magnesium metal has a high theoretical volume capacity and abundant reserves. Magnesium ion battery is theoretically secure and eco-friendly. In recent years, magnesium ion battery has attracted wide attention and is expected to become a competitive energy storage candidate in the next generation. However, due to the large polarization effect and slow migration kinetics of magnesium ions, magnesium ions are hard to insert/desert in cathode materials, resulting in a poor cycle and rate performance. CoHCF, a typical Prussian blue analog, has an open frame structure and double REDOX sites, and it is regarded as a candidate for rechargeable ion battery. Herein, a Ni-doping method was utilized to improve the performance of CoHCF. Compared with the original CoHCF, the maximum specific discharge capacity of the Ni-doped CoHCF at 50 mA/g charging and discharging current increased from 70 mAh/g to 89 mAh/g, and the cyclic performance and rate performance improved. These improvements result from the fact that the electrode reaction process of Ni-doped CoHCF changes from diffusion-driven to reaction-driven. The Ni-doped CoHCF is more stable, and the lattice changes during Mg²⁺ (de-)intercalation are smaller. This study can provide a reference for the development of Prussian blue analogs as cathode materials for magnesium ion batteries. Full article

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49 pages, 3392 KiB

Open AccessReview

Solid-State Batteries: Chemistry, Battery, and Thermal Management System, Battery Assembly, and Applications—A Critical Review

by Emre Biçer, Ahmet Aksöz, Recep Bakar, Çağla Odabaşı, Willar Vonk, Maria Inês Soares, Rafaela Gonçalves, Emanuel Lourenço, Atakan Uzel, Tülay Aksoy, Zeynep Özserçe Haste, Burcu Oral, Ömer Eroğlu, Burçak Asal and Saadin Oyucu

Batteries 2025, 11(6), 212; https://doi.org/10.3390/batteries11060212 - 27 May 2025

Viewed by 920

Abstract

Li-ion batteries (LIBs) have become the preferred choice in electric vehicles (EVs) for reducing CO₂ emissions, enhancing energy efficiency, and enabling rechargeability. They are extensively used in mobile electronics, EVs, grid storage, and other applications due to their high power, low self-discharge [...] Read more.

Li-ion batteries (LIBs) have become the preferred choice in electric vehicles (EVs) for reducing CO₂ emissions, enhancing energy efficiency, and enabling rechargeability. They are extensively used in mobile electronics, EVs, grid storage, and other applications due to their high power, low self-discharge rate, wide operating temperature range, lack of memory effect, and environmental friendliness. However, commercial LIBs face safety and energy density challenges, primarily due to volatile and flammable liquid electrolytes and moderate energy densities. To address these issues, advanced materials are being explored for improved performance in battery components such as the anode, cathode, and electrolyte. All-solid-state batteries (ASSEBs) emerge as a promising alternative to liquid electrolyte LIBs, offering higher energy density, better stability, and enhanced safety. Despite challenges like lower ionic transport, ongoing research is advancing ASSEBs’ commercial viability. This paper critically reviews the state of the art in ASSEBs, including electrolyte compositions, production techniques, battery management systems (BMSs), thermal management systems, and environmental performance. It also assesses ASSEB applications in EVs, consumer electronics, aerospace, defense, and renewable energy storage, highlighting the potential for a more sustainable and efficient energy future. Full article

(This article belongs to the Special Issue Electrolytes for Solid State Batteries—2nd Edition)

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18 pages, 5283 KiB

Open AccessArticle

Cycling Operation of a LiFePO₄ Battery and Investigation into the Influence on Equivalent Electrical Circuit Elements

by Michal Frivaldsky, Marek Simcak, Darius Andriukaitis and Dangirutis Navikas

Batteries 2025, 11(6), 211; https://doi.org/10.3390/batteries11060211 - 27 May 2025

Viewed by 270

Abstract

This study explores the significant effects of charge–discharge cycling on lithium iron phosphate (LiFePO₄)-based electrochemical cells, with a particular focus on the Sinopoly SP-LFP040AHA cell. As lithium-ion batteries undergo repeated charging and discharging cycles, their internal characteristics evolve, influencing performance, efficiency, [...] Read more.

This study explores the significant effects of charge–discharge cycling on lithium iron phosphate (LiFePO₄)-based electrochemical cells, with a particular focus on the Sinopoly SP-LFP040AHA cell. As lithium-ion batteries undergo repeated charging and discharging cycles, their internal characteristics evolve, influencing performance, efficiency, and longevity. Understanding these changes is crucial for optimizing battery management strategies and ensuring reliable operation across various applications. To analyze these effects, the study utilizes equivalent electrical circuits (EEC) to model the internal behavior of the battery. The individual components of the EEC—such as its resistive, capacitive, and inductive elements—are examined through 3D waveforms, offering a comprehensive visualization of how each parameter responds to cycling. One of the key contributions of this research is the development and implementation of an EEC identification approach that enables a systematic assessment of battery parameter evolution. This technique provides insights into the general trends and variations in electrical behavior based on the state of charge (SoC) of the cell. By analyzing data across a wide range of SoC values—from 0% (fully discharged) to 100% (fully charged)—and tracking changes over 100 charge–discharge cycles, the study highlights the progressive alterations in battery performance. The findings of this investigation offer valuable implications for battery health monitoring, predictive maintenance, and the refinement of state estimation models. Full article

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12 pages, 2446 KiB

Open AccessArticle

Characterization of Industrial Black Mass from End-of-Life LiFePO₄-Graphite Batteries

by Nanna Bjerre-Christensen, Caroline Birksø Eriksen, Kristian Oluf Sylvester-Hvid and Dorthe Bomholdt Ravnsbæk

Batteries 2025, 11(6), 210; https://doi.org/10.3390/batteries11060210 - 26 May 2025

Viewed by 272

Abstract

The use of Li-ion batteries is drastically increasing, especially due to the growing sales of electric vehicles. Simultaneously, there is a shift towards exchanging the traditional Co- and Ni-rich electrode materials with more sustainable alternatives such as LiFePO₄. This transition challenges [...] Read more.

The use of Li-ion batteries is drastically increasing, especially due to the growing sales of electric vehicles. Simultaneously, there is a shift towards exchanging the traditional Co- and Ni-rich electrode materials with more sustainable alternatives such as LiFePO₄. This transition challenges conventional recycling practices, which typically rely on shredding batteries into a substance known as black mass, which is subsequently processed via hydrometallurgical or pyrometallurgical methods to extract valuable elements. These routes may not be economically viable for future sustainable chemistries with lower contents of high-value metal. Hence, new methods for processing the black mass, allowing, e.g., for physical separation and direct recycling, are direly needed. Such developments require that the black mass is thoroughly understood. In this study, we thoroughly characterize a commercially produced Graphite/LFP black mass sample from real battery waste using a suite of analytical techniques. Our findings reveal detailed chemical, morphological, and structural insights and show that the components in the black mass have different micro-size profiles, which may enable simple size separation. Unfortunately, our analysis also reveals that the employed processing of battery waste into black mass leads to the formation of an unknown Fe-containing compound, which may hamper direct recycling routes. Full article

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

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55 pages, 6250 KiB

Open AccessReview

Challenges and Issues Facing Ultrafast-Charging Lithium-Ion Batteries

by Amirreza Aghili Mehrizi, Firoozeh Yeganehdoust, Anil Kumar Madikere Raghunatha Reddy and Karim Zaghib

Batteries 2025, 11(6), 209; https://doi.org/10.3390/batteries11060209 - 26 May 2025

Viewed by 847

Abstract

Ultrafast-charging (UFC) technology for electric vehicles (EVs) and energy storage devices has brought with it an increase in demand for lithium-ion batteries (LIBs). However, although they pose advantages in driving range and charging time, LIBs face several challenges such as mechanical degradation, lithium [...] Read more.

Ultrafast-charging (UFC) technology for electric vehicles (EVs) and energy storage devices has brought with it an increase in demand for lithium-ion batteries (LIBs). However, although they pose advantages in driving range and charging time, LIBs face several challenges such as mechanical degradation, lithium dendrite formation, electrolyte decomposition, and concerns about thermal runaway safety. This review evaluates the key challenges and advances in LIB components (anodes, cathodes, electrolytes, separators, and binders), alongside innovations in charging protocols and safety concerns. Material-level solutions such as nanostructuring, doping, and composite architectures are investigated to improve ion diffusion, conductivity, and electrode stability. Electrolyte modifications, separator enhancements, and binder optimizations are discussed in terms of their roles in reducing high-rate degradation. Furthermore, charging protocols are addressed; adjustments can reduce mechanical and electrochemical stress on LIBs, decreasing capacity fade while providing rapid charging. This review highlights the key technological advancements that are enabling ultrafast charging and that are assisting us in overcoming severe limitations, paving the way for the development of next-generation high-performance LIBs. Full article

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

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22 pages, 2958 KiB

Open AccessArticle

Accurate Chemistry Identification of Lithium-Ion Batteries Based on Temperature Dynamics with Machine Learning

by Ote Amuta, Jiaqi Yao, Dominik Droese and Julia Kowal

Batteries 2025, 11(6), 208; https://doi.org/10.3390/batteries11060208 - 26 May 2025

Viewed by 360

Abstract

Lithium-ion batteries (LIBs) are widely used in diverse applications, ranging from portable ones to stationary ones. The appropriate handling of the immense amount of spent batteries has, therefore, become significant. Whether recycled or repurposed for second-life applications, knowing their chemistry type can lead [...] Read more.

Lithium-ion batteries (LIBs) are widely used in diverse applications, ranging from portable ones to stationary ones. The appropriate handling of the immense amount of spent batteries has, therefore, become significant. Whether recycled or repurposed for second-life applications, knowing their chemistry type can lead to higher efficiency. In this paper, we propose a novel machine learning-based approach for accurate chemistry identification of the electrode materials in LIBs based on their temperature dynamics under constant current cycling using gated recurrent unit (GRU) networks. Three different chemistry types, namely lithium nickel cobalt aluminium oxide cathode with silicon-doped graphite anode (NCA-GS), nickel cobalt aluminium oxide cathode with graphite anode (NCA-G), and lithium nickel manganese cobalt oxide cathode with graphite anode (NMC-G), were examined under four conditions, 0.2 C charge, 0.2 C discharge, 1 C charge, and 1 C discharge. Experimental results showed that the unique characteristics in the surface temperature measurement during the full charge or discharge of the different chemistry types can accurately carry out the classification task in both experimental setups, where the model is trained on data under different cycling conditions separately and jointly. Furthermore, experimental results show that the proposed approach for chemistry type identification based on temperature dynamics appears to be more universal than voltage characteristics. As the proposed approach has proven to be efficient in the chemistry identification of the electrode materials LIBs in most cases, we believe it can greatly benefit the recycling and second-life application of spent LIBs in real-life applications. Full article

(This article belongs to the Special Issue Recycling and Reuse of End-of-Life Lithium-Ion Batteries: Challenges and Strategies–2nd Edition)

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17 pages, 3748 KiB

Open AccessArticle

Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization

by Chen Fei, Zhuo Lu, Weiwei Jiang, Liang Zhao and Fan Zhang

Batteries 2025, 11(6), 207; https://doi.org/10.3390/batteries11060207 - 23 May 2025

Viewed by 574

Abstract

Due to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent estimation approach that [...] Read more.

Due to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent estimation approach that integrates data visualization and advanced machine learning techniques. Initially, the battery data are visualized using matplotlib to extract key features such as temperature difference, voltage difference, and average voltage. Subsequently, an XGBoost-based model is constructed to perform the initial SOH estimation. To further enhance the estimation accuracy, we introduce the Autoregressive Integrated Moving Average Model (ARIMA) model for post-estimation correction, effectively refining the preliminary results. Experimental results demonstrate that the proposed XGBoost–ARIMA model outperforms traditional algorithms, including Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), not only in estimation accuracy but also in generalization capability, showing significant improvements over five other regression models. Full article

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

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67 pages, 11913 KiB

Open AccessReview

MXenes and MXene-Based Composites: Preparation, Characteristics, Theoretical Investigations, and Application in Developing Sulfur Cathodes, Lithium Anodes, and Functional Separators for Lithium–Sulfur Batteries

by Narasimharao Kitchamsetti, Hyuksu Han and Sungwook Mhin

Batteries 2025, 11(6), 206; https://doi.org/10.3390/batteries11060206 - 23 May 2025

Viewed by 465

Abstract

Lithium–sulfur batteries (LSBs) are favorable candidates for advanced energy storage, boasting a remarkable theoretical energy density of 2600 Wh kg⁻¹. Moreover, several challenges hinder their practical implementation, including sulfur’s intrinsic electrical insulation, the shuttle effect of lithium polysulfides (LiPSs), sluggish redox [...] Read more.

Lithium–sulfur batteries (LSBs) are favorable candidates for advanced energy storage, boasting a remarkable theoretical energy density of 2600 Wh kg⁻¹. Moreover, several challenges hinder their practical implementation, including sulfur’s intrinsic electrical insulation, the shuttle effect of lithium polysulfides (LiPSs), sluggish redox kinetics of Li₂S₂/Li₂S, and the uncontrolled growth of Li dendrites. These issues pose significant obstacles to the commercialization of LSBs. A viable strategy to address these challenges involves using MXene materials, 2D transition metal carbides, and nitrides (TMCs/TMNs) as hosts, functional separators, or interlayers. MXenes offer exceptional electronic conductivity, adjustable structural properties, and abundant polar functional groups, enabling strong interactions with both S cathodes and Li anodes. Despite their advantages, current MXene synthesis methods predominantly rely on acid etching, which is associated with environmental concerns, low production efficiency, and limited structural versatility, restricting their potential in LSBs. This review provides a comprehensive overview of traditional and environmentally sustainable MXene synthesis techniques, emphasizing their applications in developing S cathodes, Li anodes, and functional separators for LSBs. Additionally, it discusses the challenges and outlines future directions for advancing MXene-based solutions in LSBs technology. Full article

(This article belongs to the Special Issue Development, Application, and Characterization of New Electrode Materials for Advanced Batteries)

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13 pages, 4830 KiB

Open AccessArticle

Synergistic Cationic–Anionic Regulation in Ni-Doped FeSe@C Anodes with Se Vacancies for High-Efficiency Sodium Storage

by Liang Wang, Shutong Cai, Dingwen Wang, Xiangyi Wang and Yang Cheng

Batteries 2025, 11(6), 205; https://doi.org/10.3390/batteries11060205 - 23 May 2025

Viewed by 526

Abstract

Sodium-ion batteries present an economical energy storage solution, yet their anode kinetics remain slow, impeding rate performance and cyclability. Layered FeSe anodes, characterized by metallic conductivity, hold potential, but structural decay and insufficient active sites during cycling continue to pose challenges. Herein, these [...] Read more.

Sodium-ion batteries present an economical energy storage solution, yet their anode kinetics remain slow, impeding rate performance and cyclability. Layered FeSe anodes, characterized by metallic conductivity, hold potential, but structural decay and insufficient active sites during cycling continue to pose challenges. Herein, these challenges are addressed through the implementation of dual Ni doping and Se vacancy engineering in FeSe@C to synergistically regulate cationic/anionic configurations. The ionic substitution of larger Fe²⁺ ions (0.78 Å ionic radius) with smaller Ni²⁺ ions (0.69 Å) induces lattice distortion and generates abundant Se vacancies, enhancing electron transport, active site accessibility, and Na⁺ adsorption. These synergistic modifications effectively boost Na⁺ diffusion kinetics and electrolyte compatibility, creating a favorable electrochemical environment for fast sodium storage. Consequently, the optimized 2%Ni-FeSe@C electrode retains an exceptional discharge specific capacity of 307.67mAh g⁻¹ after 1000 cycles at an ultrahigh current density of 5 Ag⁻¹, showcasing superior rate capability and long-term cycling stability, paving the way for practical high-power SIBs. Full article

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26 pages, 2188 KiB

Open AccessReview

Physics-Informed Neural Networks for Advanced Thermal Management in Electronics and Battery Systems: A Review of Recent Developments and Future Prospects

by Zichen Du and Renhao Lu

Batteries 2025, 11(6), 204; https://doi.org/10.3390/batteries11060204 - 22 May 2025

Viewed by 838

Abstract

The growing complexities, power densities, and cooling demands of modern electronic systems and batteries—such as three-dimensional integrated circuit chip packaging, printed circuit board assemblies, and electronics enclosures—have pushed the urgency for efficient and dynamic thermal management strategies. Traditional numerical methods like computational fluid [...] Read more.

The growing complexities, power densities, and cooling demands of modern electronic systems and batteries—such as three-dimensional integrated circuit chip packaging, printed circuit board assemblies, and electronics enclosures—have pushed the urgency for efficient and dynamic thermal management strategies. Traditional numerical methods like computational fluid dynamics (CFD) and the finite element method (FEM) are computationally impractical for large-scale or real-time thermal analysis, especially when dealing with complex geometries, temperature-dependent material properties, and rapidly changing boundary conditions. These approaches typically require extensive meshing and repeated simulations for each new scenario, making them inefficient for design exploration or optimization tasks. Physics-informed neural networks (PINNs) emerge as a powerful alternative approach that incorporates physical principles such as mass and energy conservation equations into deep learning models. This approach delivers rapid and adaptable resolutions to the partial differential equations that govern heat transfer and fluid dynamics. This review examines the basic principle of PINN and its role in thermal management for electronics and batteries, from the small unit scale to the system scale. We highlight recent advancements in PINNs, particularly their superior performance compared to traditional CFD methods. For example, studies have shown that PINNs can be up to 300,000 times faster than conventional CFD solvers, with temperature prediction differences of less than 0.1 K in chip thermal models. Beyond speed, we explore the potential of PINNs in enabling efficient design space exploration and predicting outcomes for previously unseen scenarios. However, challenges such as training convergence in fine-grained or large-scale applications remain. Notably, research combining PINNs with LSTM networks for battery thermal management at a 2.0 C charging rate has achieved impressive results—an R² of 0.9863, a mean absolute error (MAE) of 0.2875 °C, and a root mean square error (RMSE) of 0.3306 °C—demonstrating high predictive accuracy. Finally, we propose future research directions that emphasize the integration of PINNs with advanced hardware and hybrid modeling techniques to advance thermal management solutions for next-generation electronics and battery systems. Full article

(This article belongs to the Special Issue Machine Learning for Advanced Battery Systems)

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Batteries, Volume 11, Issue 6 (June 2025) – 26 articles

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