Batteries

38 pages, 2474 KB

Open AccessReview

A Comprehensive Review of Equivalent Circuit Models and Neural Network Models for Battery Management Systems

by Davide Pio Laudani, Davide Milillo, Michele Quercio, Francesco Riganti Fulginei and Lorenzo Sabino

Batteries 2026, 12(1), 37; https://doi.org/10.3390/batteries12010037 - 22 Jan 2026

Viewed by 855

Abstract

Lithium-ion batteries are the most widely used electrochemical energy storage technology due to their excellent performance. They play a crucial role in enabling the widespread adoption of sustainable transportation and renewable energy storage. Comprehensive battery monitoring, encompassing both performance and safety aspects, presents [...] Read more.

Lithium-ion batteries are the most widely used electrochemical energy storage technology due to their excellent performance. They play a crucial role in enabling the widespread adoption of sustainable transportation and renewable energy storage. Comprehensive battery monitoring, encompassing both performance and safety aspects, presents various challenges. Generally, this task is handled by a battery management system (BMS). Therefore, this paper provides a brief introduction to the key battery state parameters, such as the state of charge (SOC), state of health (SOH), and state of power (SOP). Subsequently, after a brief overview of BMS structural and software architectures, this work focuses on a detailed description of equivalent circuit models (ECMs) and artificial neural networks (ANNs), which represent part of the modeling approaches available in the literature, providing a characterization of the complex and nonlinear dynamics underlying lithium-ion batteries. These approaches are systematically evaluated, including hybrid methods to highlight their respective advantages, limitations, and suitability for different BMS functionalities. Full article

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

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19 pages, 1516 KB

Open AccessArticle

Energy-Dynamics Sensing for Health-Responsive Virtual Synchronous Generator in Battery Energy Storage Systems

by Yingying Chen, Xinghu Liu and Yongfeng Fu

Batteries 2026, 12(1), 36; https://doi.org/10.3390/batteries12010036 - 21 Jan 2026

Viewed by 277

Abstract

Battery energy storage systems (BESSs) are increasingly required to provide grid-support services under weak-grid conditions, where the stability of virtual synchronous generator (VSG) control largely depends on the health status and dynamic characteristics of the battery unit. However, existing VSG strategies typically assume [...] Read more.

Battery energy storage systems (BESSs) are increasingly required to provide grid-support services under weak-grid conditions, where the stability of virtual synchronous generator (VSG) control largely depends on the health status and dynamic characteristics of the battery unit. However, existing VSG strategies typically assume fixed parameters and neglect the intrinsic coupling between battery aging, DC-link energy variations, and converter dynamic performance, resulting in reduced damping, degraded transient regulation, and accelerated lifetime degradation. This paper proposes a health-responsive VSG control strategy enabled by real-time energy-dynamics sensing. By reconstructing the DC-link energy state from voltage and current measurements, an intrinsic indicator of battery health and instantaneous power capability is established. This energy-dynamics indicator is then embedded into the VSG inertia and damping loops, allowing the control parameters to adapt to battery health evolution and operating conditions. The proposed method achieves coordinated enhancement of transient stability, weak-grid robustness, and lifetime management. Simulation studies on a multi-unit BESS demonstrate that the proposed strategy effectively suppresses low-frequency oscillations, accelerates transient convergence, and maintains stability across different aging stages. Full article

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21 pages, 4803 KB

Open AccessArticle

Recovery of High-Purity Lithium Hydroxide Monohydrate from Lithium-Rich Leachate by Anti-Solvent Crystallization: Process Optimization and Impurity Incorporation Mechanisms

by Faizan Muneer, Ida Strandkvist, Fredrik Engström and Lena Sundqvist-Öqvist

Batteries 2026, 12(1), 35; https://doi.org/10.3390/batteries12010035 - 21 Jan 2026

Viewed by 481

Abstract

The increasing demand for lithium-ion batteries (LIBs) has intensified the need for efficient lithium (Li) recovery from secondary sources. This study focuses on anti-solvent crystallization for the recovery of high-purity lithium hydroxide monohydrate (LiOH·H₂O) from a Li-rich leachate, derived from the [...] Read more.

The increasing demand for lithium-ion batteries (LIBs) has intensified the need for efficient lithium (Li) recovery from secondary sources. This study focuses on anti-solvent crystallization for the recovery of high-purity lithium hydroxide monohydrate (LiOH·H₂O) from a Li-rich leachate, derived from the flue dust of a pilot-scale pyrometallurgical process for LIB material recycling. To optimize product yield and purity, a series of experiments were performed, focusing on the influence of parameters such as solvent type, organic-to-aqueous (O/A) volumetric ratio, crystallization time, stirring rate, and anti-solvent addition rate. Acetone was identified as the most effective anti-solvent, producing rectangular cuboid crystals with approximately 90% Li recovery and around 95% purity, under optimized conditions (O/A = 4, 3 h, 150 rpm, and solvent flow rate of 5 mL/min). The flow rate influenced crystal morphology and impurity entrapment, with 5 mL/min favoring nucleation-dominated crystallization regime, producing ~20 μm of well-dispersed crystals with reduced impurity incorporation. SEM-EDS, surface washing, and gradual dissolution of obtained LiOH·H₂O crystals revealed that the impurities sodium (Na), potassium (K), aluminum (Al), calcium (Ca) and chromium (Cr) were crystallized as conglomerates. It was found that Na, K, Al, and Ca primarily crystallized as highly soluble conglomerates, while Cr was crystallized as a lowly soluble conglomerate impurity. In contrast Zn was distributed throughout the crystal bulk, suggesting either the entrapment of soluble zincate species within the growing crystals or the formation of mixed Li-Zn phase. Therefore, to achieve battery-grade purity, further purification measures are necessary. Full article

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

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Graphical abstract

19 pages, 3222 KB

Open AccessArticle

State of Health Estimation for Energy Storage Batteries Based on Multi-Condition Feature Extraction

by Wentao Tang, Xun Liu, Xiaohang Li, Jiangxue Shen, Zhiyuan Liao and Minming Gong

Batteries 2026, 12(1), 34; https://doi.org/10.3390/batteries12010034 - 21 Jan 2026

Viewed by 389

Abstract

In the field of energy transformation, the application of batteries is widening. To address the challenge of health state estimation of energy storage batteries with multiple operating conditions, this study analyzes the aging cycle operation data of lithium-ion batteries and develops a scheme [...] Read more.

In the field of energy transformation, the application of batteries is widening. To address the challenge of health state estimation of energy storage batteries with multiple operating conditions, this study analyzes the aging cycle operation data of lithium-ion batteries and develops a scheme to extract a number of raw features and their corresponding health status labels. Multidimensional candidate feature sets that capture aging information under different conditions are constructed. Subsequently, a three-stage feature selection strategy, including Pearson and Spearman correlation analysis, hierarchical redundancy elimination, and minimum redundancy maximum relevance, was applied to screen the candidate feature set of each condition, resulting in customized feature sets with condition adaptability. By analyzing the occurrence frequency and mean absolute correlation coefficient of each feature within the custom feature set, a comprehensive feature set with multi-condition adaptability was screened and determined. On this basis, by integrating temporal sequence information and operating condition information, a dual-path fusion estimation model with attention mechanism and condition modulation was established. The validation results of the lithium-ion battery multi-condition cycling aging dataset demonstrate that the model achieves accurate health state estimation, with mean absolute error and root mean square error of 0.8281% and 0.9835%, respectively. Finally, comparisons with other methods were conducted in terms of feature selection strategies and model estimation performance. The results demonstrate that the proposed approach achieves superior estimation accuracy and enhanced interpretability. Full article

(This article belongs to the Special Issue Advances in Lithium-Ion Battery Safety and Fire: 2nd Edition)

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20 pages, 4461 KB

Open AccessArticle

Advanced Battery Modeling Framework for Enhanced Power and Energy State Estimation with Experimental Validation

by Nemanja Mišljenović, Matej Žnidarec, Sanja Kelemen and Goran Knežević

Batteries 2026, 12(1), 33; https://doi.org/10.3390/batteries12010033 - 20 Jan 2026

Cited by 1 | Viewed by 369

Abstract

Accurate modeling of Battery Energy Storage Systems (BESS) is essential for optimizing system performance, ensuring operational safety, and extending service life in applications ranging from electric vehicles (EV) to large-scale grid storage. However, the simplifications inherent in conventional battery models often hinder optimal [...] Read more.

Accurate modeling of Battery Energy Storage Systems (BESS) is essential for optimizing system performance, ensuring operational safety, and extending service life in applications ranging from electric vehicles (EV) to large-scale grid storage. However, the simplifications inherent in conventional battery models often hinder optimal system design and operation, leading to conservative performance limits, inaccurate State-of-Energy (SOE) estimation, and reduced overall efficiency. This paper presents a framework for advanced battery modeling, developed to achieve higher fidelity in SOE estimation and improved power-capability prediction. The proposed model introduces a dynamic energy-based representation of the charging and discharging processes, incorporating a functional dependence of instantaneous power on stored energy. Experimental validation confirms the superiority of this modeling framework over existing state-of-the-art models. The proposed approach reduces SOE estimation error to 0.1% and cycle-time duration error to 0.82% compared to the measurements. Consequently, the model provides more accurate predictions of the maximum charge and discharge power limits than state-of-the-art solutions. The enhanced predictive accuracy improves energy utilization, mitigates premature degradation, and strengthens safety assurance in advanced battery management systems. Full article

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

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26 pages, 6197 KB

Open AccessArticle

Experimental Comparison of Different Techniques for Estimating Li-Ion Open-Circuit Voltage

by Mehrshad Pakjoo and Luigi Piegari

Batteries 2026, 12(1), 32; https://doi.org/10.3390/batteries12010032 - 17 Jan 2026

Viewed by 442

Abstract

Electrochemical energy storage systems are increasingly utilized across a wide range of applications, from small-scale consumer electronics to large-scale utility systems providing grid services. Among these, lithium-ion batteries have emerged as a preferred solution because of their high efficiency and power density. However, [...] Read more.

Electrochemical energy storage systems are increasingly utilized across a wide range of applications, from small-scale consumer electronics to large-scale utility systems providing grid services. Among these, lithium-ion batteries have emerged as a preferred solution because of their high efficiency and power density. However, accurately modeling the behavior of Li-ion cells remains a critical and complex task. It is particularly important to determine the open-circuit voltage (OCV), which is an essential component of most battery models. This paper presents the results of an experimental comparison of three common methods for measuring and estimating the OCV of lithium-ion cells with nickel–manganese–cobalt cathodes. Each method is described in detail, with particular attention given to the testing procedures and influence of the experimental parameters on the accuracy of the resulting OCV curves. The outcomes are then analyzed and compared to highlight the strengths, limitations, and practical considerations associated with each approach. The findings of this work will assist researchers and practitioners in selecting the most appropriate OCV measurement techniques for various applications, especially where time constraints or experimental limitations must be considered. Full article

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

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39 pages, 4912 KB

Open AccessSystematic Review

Grid-Scale Battery Energy Storage and AI-Driven Intelligent Optimization for Techno-Economic and Environmental Benefits: A Systematic Review

by Nipon Ketjoy, Yirga Belay Muna, Malinee Kaewpanha, Wisut Chamsa-ard, Tawat Suriwong and Chakkrit Termritthikun

Batteries 2026, 12(1), 31; https://doi.org/10.3390/batteries12010031 - 17 Jan 2026

Cited by 1 | Viewed by 1385

Abstract

Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full [...] Read more.

Grid-Scale Battery Energy Storage Systems (GS-BESS) play a crucial role in modern power grids, addressing challenges related to integrating renewable energy sources (RESs), load balancing, peak shaving, voltage support, load shifting, frequency regulation, emergency response, and enhancing system stability. However, harnessing their full potential and lifetime requires intelligent operational strategies that balance technological performance, economic viability, and environmental sustainability. This systematic review examines how artificial intelligence (AI)-based intelligent optimization enhances GS-BESS performance, focusing on its techno-economic, environmental impacts, and policy and regulatory implications. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we review the evolution of GS-BESS, analyze its advancements, and assess state-of-the-art applications and emerging AI techniques for GS-BESS optimization. AI techniques, including machine learning (ML), predictive modeling, optimization algorithms, deep learning (DL), and reinforcement learning (RL), are examined for their ability to improve operational efficiency and control precision in GS-BESSs. Furthermore, the review discusses the benefits of advanced dispatch strategies, including economic efficiency, emissions reduction, and improved grid resilience. Despite significant progress, challenges persist in data availability, model generalization, high computational requirements, scalability, and regulatory gaps. We conclude by identifying emerging opportunities to guide the next generation of intelligent energy storage systems. This work serves as a foundational resource for researchers, engineers, and policymakers seeking to advance the deployment of AI-enhanced GS-BESS for sustainable, resilient power systems. By analyzing the latest developments in AI applications and BESS technologies, this review provides a comprehensive perspective on their synergistic potential to drive sustainability, cost-effectiveness, and energy systems reliability. Full article

(This article belongs to the Special Issue AI-Powered Battery Management and Grid Integration for Smart Cities)

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19 pages, 3675 KB

Open AccessArticle

A Multiphysics Aging Model for SiO_x–Graphite Lithium-Ion Batteries Considering Electrochemical–Thermal–Mechanical–Gaseous Interactions

by Xiao-Ying Ma, Xue Li, Meng-Ran Kang, Jintao Shi, Xingcun Fan, Zifeng Cong, Xiaolong Feng, Jiuchun Jiang and Xiao-Guang Yang

Batteries 2026, 12(1), 30; https://doi.org/10.3390/batteries12010030 - 16 Jan 2026

Viewed by 1013

Abstract

Silicon oxide/graphite (SiO_x/Gr) anodes are promising candidates for high energy-density lithium-ion batteries. However, their complex multiphysics degradation mechanisms pose challenges for accurately interpreting and predicting capacity fade behavior. In particular, existing multiphysics models typically treat gas generation and solid electrolyte interphase [...] Read more.

Silicon oxide/graphite (SiO_x/Gr) anodes are promising candidates for high energy-density lithium-ion batteries. However, their complex multiphysics degradation mechanisms pose challenges for accurately interpreting and predicting capacity fade behavior. In particular, existing multiphysics models typically treat gas generation and solid electrolyte interphase (SEI) growth as independent or unidirectionally coupled processes, neglecting their bidirectional interactions. Here, we develop an electro–thermal–mechanical–gaseous coupled model to capture the dominant degradation processes in SiO_x/Gr anodes, including SEI growth, gas generation, SEI formation on cracks, and particle fracture. Model validation shows that the proposed framework can accurately reproduce voltage responses under various currents and temperatures, as well as capacity fade under different thermal and mechanical conditions. Based on this validated model, a mechanistic analysis reveals two key findings: (1) Gas generation and SEI growth are bidirectionally coupled. SEI growth induces gas release, while accumulated gas in turn regulates subsequent SEI evolution by promoting SEI formation through hindered mass transfer and suppressing it through reduced active surface area. (2) Crack propagation within particles is jointly governed by the magnitude and duration of stress. High-rate discharges produce large but transient stresses that restrict crack growth, while prolonged stresses at low rates promote crack propagation and more severe structural degradation. This study provides new insights into the coupled degradation mechanisms of SiO_x/Gr anodes, offering guidance for performance optimization and structural design to extend battery cycle life. Full article

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

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19 pages, 7967 KB

Open AccessArticle

State-of-Charge Estimation of Lithium-Ion Batteries Based on GMMCC-AEKF in Non-Gaussian Noise Environment

by Fuxiang Li, Haifeng Wang, Hao Chen, Limin Geng and Chunling Wu

Batteries 2026, 12(1), 29; https://doi.org/10.3390/batteries12010029 - 14 Jan 2026

Viewed by 386

Abstract

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise [...] Read more.

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise and inaccurate initial conditions. Based on the GMMCC theory, the proposed algorithm introduces an adaptive mechanism and employs two generalized Gaussian kernels to construct a mixed kernel function, thereby formulating the generalized mixture correlation-entropy criterion. This enhances the algorithm’s adaptability to complex non-Gaussian noise. Simultaneously, by incorporating adaptive filtering concepts, the state and measurement covariance matrices are dynamically adjusted to improve stability under varying noise intensities and environmental conditions. Furthermore, the use of statistical linearization and fixed-point iteration techniques effectively improves both the convergence behavior and the accuracy of nonlinear system estimation. To investigate the effectiveness of the suggested method, experiments for SOC estimation were implemented using two lithium-ion cells featuring distinct rated capacities. These tests employed both dynamic stress test (DST) and federal test procedure (FTP) profiles under three representative temperature settings: 40 °C, 25 °C, and 10 °C. The experimental findings prove that when exposed to non-Gaussian noise, the GMMCC-AEKF algorithm consistently outperforms both the traditional EKF and the generalized mixture maximum correlation-entropy-based extended Kalman filter (GMMCC-EKF) under various test conditions. Specifically, under the 25 °C DST profile, GMMCC-AEKF improves estimation accuracy by 86.54% and 10.47% over EKF and GMMCC-EKF, respectively, for the No. 1 battery. Under the FTP profile for the No. 2 battery, it achieves improvements of 55.89% and 28.61%, respectively. Even under extreme temperatures (10 °C, 40 °C), GMMCC-AEKF maintains high accuracy and stable convergence, and the algorithm demonstrates rapid convergence to the true SOC value. In summary, the GMMCC-AEKF confirms excellent estimation accuracy under various temperatures and non-Gaussian noise conditions, contributing a practical approach for accurate SOC estimation in power battery systems. Full article

(This article belongs to the Special Issue Key Technologies in Battery Management Systems for New Energy Vehicles)

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20 pages, 3079 KB

Open AccessReview

Comparative Numerical Study on Flow Characteristics of 4 × 1 kW SOFC Stacks with U-Type and Z-Type Connection Configurations

by Xiaotian Duan, Haoyuan Yin, Youngjin Kim, Kunwoo Yi, Hyeonjin Kim, Kyongsik Yun and Jihaeng Yu

Batteries 2026, 12(1), 28; https://doi.org/10.3390/batteries12010028 - 14 Jan 2026

Viewed by 1033

Abstract

In this study, a high-fidelity, full-scale three-dimensional Computational Fluid Dynamics (CFD) model was developed to analyze the effects of U-type and Z-type interconnection configurations on flow and distribution uniformity within a 4 × 1 kW planar solid oxide fuel cell (SOFC) stack composed [...] Read more.

In this study, a high-fidelity, full-scale three-dimensional Computational Fluid Dynamics (CFD) model was developed to analyze the effects of U-type and Z-type interconnection configurations on flow and distribution uniformity within a 4 × 1 kW planar solid oxide fuel cell (SOFC) stack composed of 40 unit cells. Mesh independence was verified using the Richardson extrapolation method. The results reveal that on the anode (fuel inlet) side, the Z-type configuration exhibits significantly better flow and pressure uniformity than the U-type configuration and shows low sensitivity to variations in fuel utilization (

U_{f}

= 0.3–0.8), maintaining stable flow distribution under different conditions. On the cathode (air inlet) side, however, the U-type configuration demonstrates superior flow stability at an air utilization rate of 0.3. Therefore, it is recommended to employ the Z-type configuration for the anode and the U-type configuration for the cathode to achieve more uniform gas distribution and enhanced operational stability. These findings provide valuable insights for optimizing the design and operation of solid oxide fuel cells (SOFCs) and offer guidance for the development of more efficient fuel cell systems. Full article

(This article belongs to the Special Issue Solid Oxide Fuel Cells (SOFCs))

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19 pages, 10701 KB

Open AccessArticle

Numerical Simulation and Optimization of a Novel Battery Box Wall and Contour-Finned Structure in Air-Cooled Battery Thermal Management Systems

by Jingfei Chen, Weiguang Zheng and Jianguo Ye

Batteries 2026, 12(1), 27; https://doi.org/10.3390/batteries12010027 - 13 Jan 2026

Viewed by 427

Abstract

Lithium-ion batteries (LIBs) are currently widely used in the electric vehicle sector and have become one of the core components of new energy vehicles. To ensure that the maximum temperature (T_max) and maximum temperature difference (∆T_max) remain within acceptable [...] Read more.

Lithium-ion batteries (LIBs) are currently widely used in the electric vehicle sector and have become one of the core components of new energy vehicles. To ensure that the maximum temperature (T_max) and maximum temperature difference (∆T_max) remain within acceptable limits after high-rate discharge, this study proposes a novel air-cooled battery thermal management system (BTMS). This BTMS features innovative design elements in its novel battery case walls and contour-following fin structure. Through physical testing of 21,700 LIB discharges and comparative numerical simulations, the accuracy of the simulation model is ensured. Orthogonal experimental analysis is conducted at four distinct levels for each of the four structural factors involved. The final results demonstrate that the novel battery pack wall and contour-shaped fin structure proposed in this paper significantly enhance the heat dissipation capability of air-cooled BTMS. The proposed Model 9 configuration exhibits optimal thermal performance metrics. The T_max after 3C rate discharge reaches 39.4 °C, with a ∆T_max of 7.4 °C. This study demonstrates significant application potential in the structural implementation of air-cooled BTMSs. Full article

(This article belongs to the Special Issue Advanced Lithium-Ion Battery Management in Renewable Energy Systems: 2nd Edition)

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17 pages, 6384 KB

Open AccessArticle

Numerical Investigation of Heat Dissipation Components and Thermal Management System in PEM Fuel Cell Engines

by Yuchen Zhou, Zhuqian Zhang, Haojie Zhang, Heyao Li, Xianglong Meng, Luwei Zhu and Xinyu Liao

Batteries 2026, 12(1), 26; https://doi.org/10.3390/batteries12010026 - 13 Jan 2026

Viewed by 388

Abstract

A one-dimensional analytical model for a proton exchange membrane fuel cell (PEMFC) engine is presented. The model is structured into three main subsystems: the fuel cell stack, the intake and exhaust system, and the thermal management system. The modeling of the thermal management [...] Read more.

A one-dimensional analytical model for a proton exchange membrane fuel cell (PEMFC) engine is presented. The model is structured into three main subsystems: the fuel cell stack, the intake and exhaust system, and the thermal management system. The modeling of the thermal management system specifically encompasses key components such as the expansion tank, thermostat, pump, fan, and radiator. The heat transfer and fluid flow within key thermal management components—primarily fans and radiators—are analyzed via three-dimensional modeling. A porous media model represents the unit parallel-flow radiator, where the complex fin structures are replaced by a homogenized medium. This allows for the efficient calculation of 3D thermal and flow fields once the necessary constitutive parameters are identified. Ultimately, the one-dimensional (1D) thermal management system is coupled with the three-dimensional (3D) flow field analysis. This integrated 1D-3D co-simulation framework is implemented to enhance the computational fidelity of the PEMFC engine’s thermal management model. Full article

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21 pages, 30287 KB

Open AccessArticle

Online Estimation of Lithium-Ion Battery State of Charge Using Multilayer Perceptron Applied to an Instrumented Robot

by Kawe Monteiro de Souza, José Rodolfo Galvão, Jorge Augusto Pessatto Mondadori, Maria Bernadete de Morais França, Paulo Broniera Junior and Fernanda Cristina Corrêa

Batteries 2026, 12(1), 25; https://doi.org/10.3390/batteries12010025 - 10 Jan 2026

Viewed by 456

Abstract

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) [...] Read more.

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS. Full article

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

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15 pages, 2954 KB

Open AccessArticle

Experimental Investigation of Liquid Nitrogen Fire Suppression in Lithium-Ion Battery Fires: Effects of Nozzle Diameter and Injection Strategy

by Boyan Jia, Ziwen Cai, Peng Zhang, Bingyu Li and Hongyu Wang

Batteries 2026, 12(1), 24; https://doi.org/10.3390/batteries12010024 - 10 Jan 2026

Viewed by 436

Abstract

A growing number of fires and explosions in energy storage plants have been triggered by the thermal runaway of lithium-ion batteries. Owing to the complex physicochemical properties of these batteries, their fire safety issues remain unresolved and constitute a major obstacle to the [...] Read more.

A growing number of fires and explosions in energy storage plants have been triggered by the thermal runaway of lithium-ion batteries. Owing to the complex physicochemical properties of these batteries, their fire safety issues remain unresolved and constitute a major obstacle to the large-scale deployment of energy storage systems. Compared with conventional extinguishing media, liquid nitrogen (LN₂) offers a dual suppression mechanism, i.e., rapid endothermic vaporization and oxygen displacement by inert nitrogen gas, making it highly suitable for lithium-ion battery fire control. However, the key operational parameters governing its suppression efficiency remain unclear, leading to excessive or insufficient LN₂ use in practice. This study established a dedicated experimental platform and designed 10 experimental conditions, each repeated three times, to investigate the propagation of thermal runaway between adjacent batteries and to quantify the suppression performance of LN₂ under varying nozzle diameters and injection strategies. Results demonstrate that under identical injection pressures, larger nozzle diameters significantly outperform smaller ones in cooling and suppression efficiency. The optimal nozzle diameter was found to be 14 mm, achieving a cooling efficiency of 40%. Furthermore, intermittent LN₂ injection of equal total mass outperformed continuous injection, with a 45 s intermittent duration achieving a cooling efficiency of 63%, 23% higher than continuous injection. These findings provide quantitative guidance for the design of LN₂-based suppression systems in large-scale lithium-ion battery energy storage modules. Full article

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18 pages, 5540 KB

Open AccessArticle

Numerical and Experimental Study on Jet Flame Behavior and Smoke Pattern Characteristics of 50 Ah NCM Lithium-Ion Battery Thermal Runaway

by Xuehui Wang, Zilin Fan, Zhuo’er Sun, Xin Fu, Mingyu Jin, Yang Shen, Shu Lin and Zhi Wang

Batteries 2026, 12(1), 23; https://doi.org/10.3390/batteries12010023 - 8 Jan 2026

Viewed by 552

Abstract

This paper investigates the flame behavior and smoke pattern characteristics of lithium-ion battery (LIB) fires using an integrated experimental and numerical simulation approach. Based on fire dynamics theory, a jet flame model for LIB thermal runaway (TR) is developed to analyze the flame [...] Read more.

This paper investigates the flame behavior and smoke pattern characteristics of lithium-ion battery (LIB) fires using an integrated experimental and numerical simulation approach. Based on fire dynamics theory, a jet flame model for LIB thermal runaway (TR) is developed to analyze the flame height and dynamic characteristics. The results reveal two distinct regimes in LIB jet flames: momentum-controlled dominance in the early TR stage (lasting approximately 3 s) and buoyancy-controlled dominance in subsequent combustion. The jet flame shifts from a momentum-dominated regime (Fr > 5) to a buoyancy-dominated plume (Fr < 5) as the vent velocity decays below 12 m/s. The simulated flame heights align with experimental measurements and the Delichatsios model, validating the numerical approach. Furthermore, the distribution of flame components (e.g., H₂, CO, CO₂, CH₄, C₂H₄) is analyzed, highlighting the influence of multi-component gases on combustion heterogeneity. Smoke pattern analysis demonstrates that soot deposition varies significantly between momentum- and buoyancy-controlled stages, with the former producing darker, concentrated deposits and the latter yielding wider, lighter patterns. These findings provide a theoretical basis for forensic fire investigation (accident reconstruction) and targeted suppression strategies for different combustion stages. Full article

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

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20 pages, 4124 KB

Open AccessArticle

Experimental Investigation of the Impact of V2G Cycling on the Lifetime of Lithium-Ion Cells Based on Real-World Usage Data

by George Darikas, Mehmet Cagin Kirca, Nessa Fereshteh Saniee, Muhammad Rashid, Ihsan Mert Muhaddisoglu, Truong Quang Dinh and Andrew McGordon

Batteries 2026, 12(1), 22; https://doi.org/10.3390/batteries12010022 - 8 Jan 2026

Viewed by 798

Abstract

This work investigated the impact of vehicle-to-grid (V2G) cycling on the service life of lithium-ion cells, using real-world V2G data from commercial electric vehicle (EV) battery chargers. Commercially available cylindrical lithium-ion cells were subjected to long-term storage and V2G cycling under varying state [...] Read more.

This work investigated the impact of vehicle-to-grid (V2G) cycling on the service life of lithium-ion cells, using real-world V2G data from commercial electric vehicle (EV) battery chargers. Commercially available cylindrical lithium-ion cells were subjected to long-term storage and V2G cycling under varying state of charge (SOC), depth of discharge (DOD), and temperature conditions. The ageing results demonstrate that elevated temperature (40 °C) is the dominant factor accelerating degradation, particularly at a high storage SOC (>80% SOC) and increased cycle depths (30–80% SOC, 30–95% SOC). A comparison between V2G cycling and calendar ageing over a similar storage period revealed that shallow V2G cycling (30–50% SOC) leads to comparable capacity fade to storage at a high SOC (≥80% SOC). The comparative analysis indicated that 62% of a full equivalent cycle (FEC) of V2G cycling can be achieved daily, without compromising the cell’s lifetime, demonstrating the viability of V2G adoption during EV idle/charging periods, which can offer potential operational benefits in terms of cost reduction and emissions savings. Furthermore, this work introduced the concept of a V2X capability metric as a novel cell-level specification, along with a corresponding experimental evaluation method. Full article

(This article belongs to the Special Issue 10th Anniversary of Batteries: Battery Health, Aging and Degradation Mechanisms)

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16 pages, 6964 KB

Open AccessArticle

Application of Li₃InCl₆-PEO Composite Electrolyte in All-Solid-State Battery

by Han-Xin Mei, Paolo Piccardo and Roberto Spotorno

Batteries 2026, 12(1), 21; https://doi.org/10.3390/batteries12010021 - 6 Jan 2026

Viewed by 839

Abstract

Poly(ethylene oxide) (PEO)-based solid polymer electrolytes typically suffer from limited ionic conductivity at near-room temperature and often require inorganic reinforcement. Halide solid-state electrolytes such as Li₃InCl₆ (LIC) offer fast Li⁺ transport but are moisture-sensitive and typically require pressure-assisted densification. [...] Read more.

Poly(ethylene oxide) (PEO)-based solid polymer electrolytes typically suffer from limited ionic conductivity at near-room temperature and often require inorganic reinforcement. Halide solid-state electrolytes such as Li₃InCl₆ (LIC) offer fast Li⁺ transport but are moisture-sensitive and typically require pressure-assisted densification. Here, we fabricate a flexible LIC–PEO composite electrolyte via slurry casting in acetonitrile with a small amount of LiPF₆ additive. The free-standing membrane delivers an ionic conductivity of 1.19 mS cm⁻¹ at 35 °C and an electrochemical stability window up to 5.15 V. Compared with pristine LIC, the composite shows improved moisture tolerance, and its conductivity can be recovered by mild heating after exposure. The electrolyte enables stable Li|LIC–PEO|Li cycling for >620 h and supports Li|LIC–PEO|NCM111 cells with capacity retentions of 84.2% after 300 cycles at 0.2 C and 80.6% after 150 cycles at 1.2 C (35 °C). Structural and surface analyses (XRD, SEM/EDX, XPS) elucidate the composite microstructure and interfacial chemistry. Full article

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

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15 pages, 7236 KB

Open AccessArticle

Ultrafast Microwave-Assisted Resin Curing Forming a Dense Cross-Linked Network on Bamboo: Toward High-Performance Hard Carbon Anodes for Sodium-Ion Batteries

by Ziming Liu, Xiang Zhang, Wanqian Li, Min Li, Gonggang Liu, Jinbo Hu, Binghui Xu, Xianjun Li and Hui Tong

Batteries 2026, 12(1), 20; https://doi.org/10.3390/batteries12010020 - 5 Jan 2026

Viewed by 514

Abstract

Resin curing coating is an effective approach to mitigate the intrinsic defects of lignocellulosic biomass-derived hard carbon, which facilitates its large-scale application in sodium-ion batteries due to their improved specific capacity, initial coulombic efficiency, and carbon yield. However, current traditional curing processes suffer [...] Read more.

Resin curing coating is an effective approach to mitigate the intrinsic defects of lignocellulosic biomass-derived hard carbon, which facilitates its large-scale application in sodium-ion batteries due to their improved specific capacity, initial coulombic efficiency, and carbon yield. However, current traditional curing processes suffer from issues such as uneven cross-linking encapsulation and long curing cycles, significantly affecting the electrochemical performance of the derived carbon and production efficiency/cost. In this study, a phenolic resin solution impregnation combined with microwave-accelerated curing has been employed, and its curing process, along with the electrochemical performance of the derived carbon, was investigated. The results show that uniformly phenolic resin-coated bamboo could be achieved within 120 s. A dense cross-linked network not only leads to a high hard carbon yield and low specific surface area but also creates an abundant pseudographene-like structure with more closed pores. Under optimal crosslinking conditions, the obtained hard carbon sample shows a significantly enhanced reversible capacity (371.73 mAh g⁻¹) and high initial coulombic efficiency of 84.54%, far exceeding the bamboo-derived hard carbon (229.23 mAh g⁻¹, 74.90%) and the hard carbon sample prepared by traditional heating curing (304.31 mAh g⁻¹, 80.63%). Additionally, the designed sample displays excellent structural stability, maintaining 80% of their capacity after 500 cycles at a high current density of 300 mA g⁻¹. This fast and simple resin coating strategy shows great potential for the scalable synthesis of high-performance hard carbon anode materials. Full article

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

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2 pages, 878 KB

Open AccessCorrection

Correction: Zhang, H. Study on Thermal Runaway Behavior and Jet Characteristics of a 156 Ah Prismatic Ternary Lithium Battery. Batteries 2024, 10, 282

by Huipeng Zhang

Batteries 2026, 12(1), 19; https://doi.org/10.3390/batteries12010019 - 5 Jan 2026

Viewed by 287

Abstract

In the original publication [...] Full article

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28 pages, 2911 KB

Open AccessReview

Recent Progress of Biomass-Derived Carbon for Supercapacitors: A Review

by Anlin Li, Junming Xu and Jipeng Cheng

Batteries 2026, 12(1), 18; https://doi.org/10.3390/batteries12010018 - 1 Jan 2026

Cited by 1 | Viewed by 1033

Abstract

Carbon materials are very important for the commercial production of supercapacitors and they are crucial electrode materials. The porous carbon prepared with biomass materials as a precursor is of significance due to its sustainability, environmental friendliness, and low cost. Biomass-derived carbon (BDC) has [...] Read more.

Carbon materials are very important for the commercial production of supercapacitors and they are crucial electrode materials. The porous carbon prepared with biomass materials as a precursor is of significance due to its sustainability, environmental friendliness, and low cost. Biomass-derived carbon (BDC) has been widely investigated and reported as the electrode of supercapacitors due to its abundant pores and high surface areas. In this work, the recent advancement of BDC for supercapacitors in the last three years is reviewed. The energy storage mechanism, synthesis techniques, and biomass classification of BDC are briefly summarized at the beginning of this work. Some new typical cases with different biomass resources as raw materials are addressed. Then, effective strategies to further improve the specific capacitance of BDC, including heteroatoms doping, designing composites, novel processes, enhancing graphitic degree, and unique preparation methods, are discussed in detail. Finally, the challenges and future perspectives of porous BDC for supercapacitors are outlined. Full article

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

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33 pages, 3089 KB

Open AccessArticle

Designing a Sustainable Off-Grid EV Charging Station: Analysis Across Urban and Remote Canadian Regions

by Muhammad Nadeem Akram and Walid Abdul-Kader

Batteries 2026, 12(1), 17; https://doi.org/10.3390/batteries12010017 - 1 Jan 2026

Viewed by 789

Abstract

Electric vehicles are becoming more commonplace as we shift towards cleaner transportation. However, current charging infrastructure is immature, especially in remote and off-grid regions, making electric vehicle adoption challenging. This study presents an architecture for a standalone renewable energy-based electric vehicle charging station. [...] Read more.

Electric vehicles are becoming more commonplace as we shift towards cleaner transportation. However, current charging infrastructure is immature, especially in remote and off-grid regions, making electric vehicle adoption challenging. This study presents an architecture for a standalone renewable energy-based electric vehicle charging station. The proposed renewable energy system comprises wind turbines, solar photovoltaic panels, fuel cells, and a hydrogen tank. As an energy storage system, second-life electric vehicle batteries are considered. This study investigates the feasibility and performance of the charging station with respect to two vastly different Canadian regions, Windsor, Ontario (urban), and Eagle Plains, Yukon (remote). In modeling these two regions using HOMER Pro software, this study concludes that due to its higher renewable energy availability, Windsor shows a net-present cost of $2.80 million and cost of energy of $0.201/kWh as compared to the severe climate of Eagle Plains, with a net-present cost of $3.61 million and cost of energy of $0.259/kWh. In both cases, we see zero emissions in off-grid configurations. A sensitivity analysis shows that system performance can be improved by increasing wind turbine hub heights and solar photovoltaic panel lifespans. With Canada’s goal of transitioning towards 100% zero-emission vehicle sales by 2035, this study provides practical insights regarding site-specific resource optimization for electric vehicle infrastructure that does not rely on grid energy. Furthermore, this study highlights a means to progress the sustainable development goals, namely goals 7, 9, and 13, through the development of more accessible electric vehicle charging stations. Full article

(This article belongs to the Special Issue Battery Management and Advanced Energy Storage/Conversion Technologies in Renewable Power Systems: From Batteries to Fuel Cells and Hybrid Solutions)

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26 pages, 1730 KB

Open AccessArticle

Two-Stage Game-Based Charging Optimization for a Competitive EV Charging Station Considering Uncertain Distributed Generation and Charging Behavior

by Shaohua Han, Hongji Zhu, Jinian Pang, Xuan Ge, Fuju Zhou and Min Wang

Batteries 2026, 12(1), 16; https://doi.org/10.3390/batteries12010016 - 1 Jan 2026

Viewed by 557

Abstract

The widespread adoption of electric vehicles (EVs) has turned charging demand into a substantial load on the power grid. To satisfy the rapidly growing demand of EVs, the construction of charging infrastructure has received sustained attention in recent years. As charging stations become [...] Read more.

The widespread adoption of electric vehicles (EVs) has turned charging demand into a substantial load on the power grid. To satisfy the rapidly growing demand of EVs, the construction of charging infrastructure has received sustained attention in recent years. As charging stations become more widespread, how to attract EV users in a competitive charging market while optimizing the internal charging process is the key to determine the charging station’s operational efficiency. This paper tackles this issue by presenting the following contributions. Firstly, a simulation method based on prospect theory is proposed to simulate EV users’ preferences in selecting charging stations. The selection behavior of EV users is simulated by establishing coupling relationship among the transportation network, power grid, and charging network as well as the model of users’ preference. Secondly, a two-stage joint stochastic optimization model for a charging station is developed, which considers both charging pricing and energy control. At the first stage, a Stackelberg game is employed to determine the day-ahead optimal charging price in a competitive market. At the second stage, real-time stochastic charging control is applied to maximize the operational profit of the charging station considering renewable energy integration. Finally, a scenario-based Alternating Direction Method of Multipliers (ADMM) approach is introduced in the first stage for optimal pricing learning, while a simulation-based Rollout method is applied in the second stage to update the real-time energy control strategy based on the latest pricing. Numerical results demonstrate that the proposed method can achieve as large as 33% profit improvement by comparing with the competitive charging stations considering 1000 EV integration. Full article

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

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30 pages, 2529 KB

Open AccessArticle

State-of-Health Predictive Energy Management and Grid-Forming Control for Battery Energy Storage Systems

by Yingying Chen, Xinghu Liu and Yongfeng Fu

Batteries 2026, 12(1), 15; https://doi.org/10.3390/batteries12010015 - 31 Dec 2025

Viewed by 649

Abstract

This paper proposes a unified state-of-health (SoH) predictive energy management and adaptive grid-forming (GFM) control framework for battery energy storage systems, addressing the conflict between battery lifetime degradation and dynamic stability under grid-support operation. A composite degradation model is incorporated into a multi-timescale [...] Read more.

This paper proposes a unified state-of-health (SoH) predictive energy management and adaptive grid-forming (GFM) control framework for battery energy storage systems, addressing the conflict between battery lifetime degradation and dynamic stability under grid-support operation. A composite degradation model is incorporated into a multi-timescale EMS to anticipate aging trends, while real-time virtual inertia, damping, and impedance are adjusted according to instantaneous SoH. Simulation results demonstrate that, compared with conventional non-SoH-aware control, the proposed method reduces transient overshoot by up to 32%, shortens settling time by 25–40%, and lowers peak battery current stress by 12–23% under aged (60% SoH) conditions. Moreover, the unified framework maintains consistent damping performance across different aging stages, whereas traditional approaches exhibit significant degradation. These quantitative improvements confirm that jointly embedding SoH prediction into both dispatch scheduling and GFM control can effectively enhance transient performance while extending battery lifetime. Full article

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19 pages, 4836 KB

Open AccessArticle

Experimental Study of Pouch-Type Battery Cell Thermal Characteristics Operated at High C-Rates

by Marius Vasylius, Deivydas Šapalas, Benas Dumbrauskas, Valentinas Kartašovas, Audrius Senulis, Artūras Tadžijevas, Pranas Mažeika, Rimantas Didžiokas, Ernestas Šimkutis and Lukas Januta

Batteries 2026, 12(1), 14; https://doi.org/10.3390/batteries12010014 - 28 Dec 2025

Viewed by 773

Abstract

This paper investigates pouch-type lithium-ion battery cells with a nominal voltage of 3.7 V and a nominal capacity of 57 Ah. A numerical model of the cell was developed and implemented using the NTGK method, which accurately predicts electrochemical and thermal processes. The [...] Read more.

This paper investigates pouch-type lithium-ion battery cells with a nominal voltage of 3.7 V and a nominal capacity of 57 Ah. A numerical model of the cell was developed and implemented using the NTGK method, which accurately predicts electrochemical and thermal processes. The results of numerical modeling matched with the experimental results of battery cell temperature measurements—the average deviation was about 4.5%; therefore, it can be considered reliable for further engineering research and construction of battery modules. In the experimental part of the paper, the battery cell was loaded in various C-rates (from 0.5 to 2 C), using heat flux sensors, thermocouples, and a thermal imaging camera. The studies revealed that the highest temperature is in the tabs area of cells. The temperature on the face of the cell surface exceeds 35 °C already from a load of 1.35 C, which accelerates cell degradation and reduces the number of cycles. Thermal imaging revealed uneven temperature distribution, whereby the top of the cell heats up more than the bottom of the cell and the temperature gradient can reach 2–4 °C. It was observed that during faster charge/discharge modes, the temperature rises from the tabs of the cell, and during slower ones, more in the middle face surface of the cell. The studies highlight the need to apply additional cooling solutions, especially cooling of the upper cell face, to ensure durability and uniform heat distribution. Full article

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

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18 pages, 8803 KB

Open AccessArticle

Tailoring Primary Particle Growth via Controlled Ammonia Feeding for Enhanced Electrochemical Stability of Hierarchical NCM622 Cathodes

by Khaja Hussain Shaik, Hyeon Jun Choi and Joo-Hyung Kim

Batteries 2026, 12(1), 13; https://doi.org/10.3390/batteries12010013 - 27 Dec 2025

Viewed by 749

Abstract

Ni-rich layered LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622) cathodes are the most promising candidates for high-energy lithium-ion batteries, but their performance is often limited by structural instability and capacity fading due to large primary particle sizes and surface degradation. Precise [...] Read more.

Ni-rich layered LiNi_0.6Co_0.2Mn_0.2O₂ (NCM622) cathodes are the most promising candidates for high-energy lithium-ion batteries, but their performance is often limited by structural instability and capacity fading due to large primary particle sizes and surface degradation. Precise control of the primary particle size significantly impacts the performance of NCM622 cathodes and can mitigate fatigue mechanisms, but the underlying processes remain unclear. In this study, NCM622 cathodes with various primary particle sizes were synthesized by applying a controlled co-precipitation strategy by systematically controlling the ammonia feed rate and solution pH during precursor formation. Interestingly, higher ammonia feed rates promoted the formation of smaller, more ordered primary particles, whereas lower feed rates and reduced pH produced larger primary particles in spherical secondary structures. Electrochemical evaluation revealed that cathodes composed of smaller primary particles exhibited enhanced Li⁺ diffusion kinetics and superior electrochemical performance compared to those synthesized under lower ammonia feeding or reduced pH conditions. Moreover, the optimized NCM622 electrode demonstrated excellent rate capability and maintained a stable layered microstructure during cycling, retaining ~86% of its initial capacity. These results demonstrate that fine-tuning the ammonia feeding conditions during co-precipitation provides a simple and effective approach to control primary particle growth, thereby improving the structural integrity and electrochemical durability of NCM622 cathode materials. Full article

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

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21 pages, 2857 KB

Open AccessArticle

Distributed Energy Storage Configuration Method for AC/DC Hybrid Distribution Network Based on Bi-Level Optimization

by Jianjun Zhao, Jianqi Wang, Mengke Gao, Yinfeng Sun, Yang Li, Zhenhao Wang and Xu Zhao

Batteries 2026, 12(1), 9; https://doi.org/10.3390/batteries12010009 - 26 Dec 2025

Viewed by 357

Abstract

Aiming at prominent voltage quality problems in AC/DC hybrid distribution networks with a high proportion of distributed energy and diversified loads, this paper proposes a bi-level energy storage system (ESS) optimization model. The upper level optimizes the ESS configuration with the goal of [...] Read more.

Aiming at prominent voltage quality problems in AC/DC hybrid distribution networks with a high proportion of distributed energy and diversified loads, this paper proposes a bi-level energy storage system (ESS) optimization model. The upper level optimizes the ESS configuration with the goal of minimizing the cost, and the lower level optimizes the real-time running state of the ESS. Considering multiple constraints, the improved PSO algorithm and the Gurobi solver are used to solve the problem. The test on the modified IEEE-33 node system verified that the model effectively improved voltage quality and reduced power system costs, which provides theoretical and engineering support for the scientific configuration of the ESS. Full article

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

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36 pages, 117874 KB

Open AccessReview

Synergistic Experimental and Computational Strategies for MXene-Based Zinc-Ion Batteries

by Man Li and Seunghyun Song

Batteries 2026, 12(1), 8; https://doi.org/10.3390/batteries12010008 - 26 Dec 2025

Viewed by 785

Abstract

Zinc-ion batteries (ZIBs) are regarded as one of the promising next-generation energy storage technologies due to their high volumetric capacity, cost-effectiveness, and high safety. MXene materials, featuring a unique two-dimensional (2D) layered structure, excellent conductivity, and tunable surface chemistry, have been widely applied [...] Read more.

Zinc-ion batteries (ZIBs) are regarded as one of the promising next-generation energy storage technologies due to their high volumetric capacity, cost-effectiveness, and high safety. MXene materials, featuring a unique two-dimensional (2D) layered structure, excellent conductivity, and tunable surface chemistry, have been widely applied in energy storage systems. This review summarizes the recent progress in experimental and computational strategies for MXene-based ZIBs. The construction of MXene-based electrodes and the effect mechanisms of Zn-ion transport facilitation, electrode cycling stability, and anode dendrite suppression are discussed. Subsequently, the theoretical simulation strategies for MXene performance investigation are analyzed, including surface chemistry and defect engineering of MXene-based electrodes and the rational design of heterostructure interfaces for enhancing conductivity and suppressing Zn dendrite growth. Finally, the review outlines the major challenges that currently hinder the applications of MXene in ZIBs and proposes future research directions, offering insights that may guide the continued advancement of next-generation MXene-based energy storage systems. Full article

(This article belongs to the Special Issue Two-Dimensional Materials for Advanced Batteries)

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28 pages, 26223 KB

Open AccessArticle

Prediction of the Remaining Useful Life of Lithium-Ion Batteries Based on the Optimized TTAO-VMD-BiLSTM

by Pengcheng Wang, Lu Liu, Qun Yu, Dongdong Hou, Enjie Li, Haijun Yu, Shumin Liu, Lizhen Qin and Yunhai Zhu

Batteries 2026, 12(1), 12; https://doi.org/10.3390/batteries12010012 - 26 Dec 2025

Viewed by 546

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been [...] Read more.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical for ensuring the safe operation of equipment, optimizing industrial cost management, and promoting the sustainable development of the renewable energy sector. Although various deep learning-based approaches for RUL prediction have been proposed, their performance is highly dependent on the availability of large training datasets. As a result, these methods generally achieve satisfactory accuracy only when sufficient training samples are available. To address this limitation, this study proposes a novel hybrid strategy that combines a parameter-optimized signal decomposition algorithm with an enhanced neural network architecture, aiming to improve RUL prediction reliability under small-sample conditions. Specifically, we develop a lithium-ion battery capacity prediction method that integrates the Triangle Topology Aggregation Optimizer (TTAO), Variational Mode Decomposition (VMD), and a Bidirectional Long Short-Term Memory (BiLSTM) network. First, the TTAO algorithm is used to optimize the number of modes and the quadratic penalty factor in VMD, enabling the decomposition of battery capacity data into multiple intrinsic mode functions (IMFs) while minimizing the impact of phenomena such as capacity regeneration. Key features highly correlated with battery life are then extracted as inputs for prediction. Subsequently, a BiLSTM network is employed to capture subtle variations in the capacity degradation process and to predict capacity based on the decomposed sequences. The prediction results are effectively integrated, and comprehensive experiments are conducted on the NASA and CALCE lithium-ion battery aging datasets. The results show that the proposed TTAO-VMD-BiLSTM model exhibits a small number of parameters, low memory consumption, high prediction accuracy, and fast convergence. The root mean square error (RMSE) does not exceed 0.8%, and the maximum mean absolute error (MAE) is less than 0.5%. Full article

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40 pages, 5707 KB

Open AccessReview

Graph Representation Learning for Battery Energy Systems in Few-Shot Scenarios: Methods, Challenges and Outlook

by Xinyue Zhang and Shunli Wang

Batteries 2026, 12(1), 11; https://doi.org/10.3390/batteries12010011 - 26 Dec 2025

Viewed by 684

Abstract

Graph representation learning (GRL) has emerged as a unifying paradigm for modeling the relational and heterogeneous nature of battery energy storage systems (BESS), yet a systematic synthesis focused on data-scarce (few-shot) battery scenarios is still lacking. Graph representation learning offers a natural way [...] Read more.

Graph representation learning (GRL) has emerged as a unifying paradigm for modeling the relational and heterogeneous nature of battery energy storage systems (BESS), yet a systematic synthesis focused on data-scarce (few-shot) battery scenarios is still lacking. Graph representation learning offers a natural way to describe the structure and interaction of battery cells, modules and packs. At the same time, battery applications often suffer from very limited labeled data, especially for new chemistries, extreme operating conditions and second-life use. This review analyzes how graph representation learning can be combined with few-shot learning to support key battery management tasks under such data-scarce conditions. We first introduce the basic ideas of graph representation learning, including models based on neighborhood aggregation, contrastive learning, autoencoders and transfer learning, and discuss typical data, model and algorithm challenges in few-shot scenarios. We then connect these methods to battery state estimation problems, covering state of charge, state of health, remaining useful life and capacity. Particular attention is given to approaches that use graph neural models, meta-learning, semi-supervised and self-supervised learning, Bayesian deep networks, and federated learning to extract transferable features from early-cycle data, partial charge–discharge curves and large unlabeled field datasets. Reported studies show that, with only a small fraction of labeled samples or a few initial cycles, these methods can achieve state and life prediction errors that are comparable to or better than conventional models trained on full datasets, while also improving robustness and, in some cases, providing uncertainty estimates. Based on this evidence, we summarize the main technical routes for few-shot battery scenarios and identify open problems in data preparation, cross-domain generalization, uncertainty quantification and deployment on real battery management systems. The review concludes with a research outlook, highlighting the need for pack-level graph models, physics-guided and probabilistic learning, and unified benchmarks to advance reliable graph-based few-shot methods for next-generation intelligent battery management. Full article

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

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13 pages, 2011 KB

Open AccessArticle

Data-Driven State-of-Health Estimation by Reconstructing Virtual Full-Charge Segments

by Dongxu Guo, Zhenghang Zou, Xin Lai and Yuejiu Zheng

Batteries 2026, 12(1), 10; https://doi.org/10.3390/batteries12010010 - 26 Dec 2025

Viewed by 537

Abstract

The rapid growth of new energy vehicles necessitates accurate battery state of health (SOH) assessment to ensure safety and reliability. However, real-world SOH estimation is challenging because users rarely perform full charge–discharge cycles, leaving only fragmented charging segments that obscure true battery capacity. [...] Read more.

The rapid growth of new energy vehicles necessitates accurate battery state of health (SOH) assessment to ensure safety and reliability. However, real-world SOH estimation is challenging because users rarely perform full charge–discharge cycles, leaving only fragmented charging segments that obscure true battery capacity. To address this, we propose a data-driven method that reconstructs a virtual full-charge cycle. By clustering charging segments based on temperature and current, the approach creatively splices multiple incomplete curves from similar mileages and conditions into a complete charging profile. This enables robust full-capacity estimation on a large-scale real-world vehicle dataset, achieving estimation errors below 2% when compared with offline validation tests. The method offers a practical and scalable solution for SOH monitoring and fleet management using field data. Full article

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

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