Batteries

19 pages, 4770 KB

Open AccessArticle

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

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

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

Abstract

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

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

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

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31 pages, 24268 KB

Open AccessArticle

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

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

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

Abstract

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

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

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23 pages, 2752 KB

Open AccessArticle

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

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

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

Abstract

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

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

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

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52 pages, 4772 KB

Open AccessReview

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

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

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

Abstract

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

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

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

20 pages, 28542 KB

Open AccessArticle

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

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

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

Abstract

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

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

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

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25 pages, 3133 KB

Open AccessArticle

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

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

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

Abstract

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

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

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

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Graphical abstract

18 pages, 9292 KB

Open AccessArticle

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

by Sang Hoon Park and Seon Hyeog Kim

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

Abstract

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

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

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

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Graphical abstract

21 pages, 4626 KB

Open AccessArticle

Thermally Aware Design of Large-Format Batteries Driven by an Equivalent Circuit Network-Based Electro-Thermal Model

by Junlong Niu, Hua Tang, Hongwei Li, Caiping Zhang, Linjing Zhang, Bingxiang Sun, Kai Gao, Tong Li and Tao Zhu

Batteries 2026, 12(2), 47; https://doi.org/10.3390/batteries12020047 - 30 Jan 2026

Abstract

Large-format pouch cells enable higher pack-level energy density and simplified system architecture, yet they pose significant thermal challenges due to long internal conduction paths, pronounced spatial gradients, and limited access to core temperature. This work develops a high-fidelity electro-thermal model for large-format cells [...] Read more.

Large-format pouch cells enable higher pack-level energy density and simplified system architecture, yet they pose significant thermal challenges due to long internal conduction paths, pronounced spatial gradients, and limited access to core temperature. This work develops a high-fidelity electro-thermal model for large-format cells based on an equivalent circuit network that mirrors the physical assembly of tabs, welds, and electrode stacks. The model couples three-dimensional ohmic conduction in tabs, welds, and current collectors with node-level equivalent circuit models in the stack, and uses measurement-anchored parameters. The model is used to study thermally critical design factors for a 44 Ah pouch cell, including thermal management configurations, tab width, tab thickness, and tab welding. Simulation results indicate that among four active cooling options, two-sided stack surface cooling achieves the lowest temperatures and the best uniformity, lowering the average temperature by about 11 °C relative to natural convection and reducing the temperature standard deviation to 1.43 °C. It also decreases the core maximum temperature by more than 9 °C, whereas other configurations provide only 4 to 5 °C core reductions. Changes to tab geometry and welding have minor effects except under one-sided tab cooling. Full article

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

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21 pages, 3253 KB

Open AccessArticle

Physics-Informed Neural Network-Based Intelligent Control for Photovoltaic Charge Allocation in Multi-Battery Energy Systems

by Akeem Babatunde Akinwola and Abdulaziz Alkuhayli

Batteries 2026, 12(2), 46; https://doi.org/10.3390/batteries12020046 - 30 Jan 2026

Abstract

The rapid integration of photovoltaic (PV) generation into modern power networks introduces significant operational challenges, including intermittent power production, uneven charge distribution, and reduced system reliability in multi-battery energy storage systems. Addressing these challenges requires intelligent, adaptive, and physically consistent control strategies capable [...] Read more.

The rapid integration of photovoltaic (PV) generation into modern power networks introduces significant operational challenges, including intermittent power production, uneven charge distribution, and reduced system reliability in multi-battery energy storage systems. Addressing these challenges requires intelligent, adaptive, and physically consistent control strategies capable of operating under uncertain environmental and load conditions. This study proposes a Physics-Informed Neural Network (PINN)-based charge allocation framework that explicitly embeds physical constraints—namely charge conservation and State-of-Charge (SoC) equalization—directly into the learning process, enabling real-time adaptive control under varying irradiance and load conditions. The proposed controller exploits real-time measurements of PV voltage, current, and irradiance to achieve optimal charge distribution while ensuring converter stability and balanced battery operation. The framework is implemented and validated in MATLAB/Simulink under Standard Test Conditions of 1000 W·m⁻² irradiance and 25 °C ambient temperature. Simulation results demonstrate stable PV voltage regulation within the 230–250 V range, an average PV power output of approximately 95 kW, and effective duty-cycle control within the range of 0.35–0.45. The system maintains balanced three-phase grid voltages and currents with stable sinusoidal waveforms, indicating high power quality during steady-state operation. Compared with conventional Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC) methods, the PINN-based approach achieves faster SoC equalization, reduced transient fluctuations, and more than 6% improvement in overall system efficiency. These results confirm the strong potential of physics-informed intelligent control as a scalable and reliable solution for smart PV–battery energy systems, with direct relevance to renewable microgrids and electric vehicle charging infrastructures. Full article

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

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24 pages, 9446 KB

Open AccessArticle

Coupling Model and Early-Stage Internal Short Circuits Fault Diagnosis for Gel Electrolyte Lithium-Ion Batteries

by Liye Wang, Jinlong Wu, Chunxiao Ma, Xianzhong Sun, Lifang Wang and Chenglin Liao

Batteries 2026, 12(2), 45; https://doi.org/10.3390/batteries12020045 - 28 Jan 2026

Abstract

This paper presents a method for modeling and predicting ISC in gel-electrolyte lithium-ion batteries, addressing critical safety concerns in electric vehicles. While gel-electrolytes are highlighted for their superior stability and performance advantages over liquid-electrolytes, they remain susceptible to IISC due to factors such [...] Read more.

This paper presents a method for modeling and predicting ISC in gel-electrolyte lithium-ion batteries, addressing critical safety concerns in electric vehicles. While gel-electrolytes are highlighted for their superior stability and performance advantages over liquid-electrolytes, they remain susceptible to IISC due to factors such as dendrite formation or mechanical stress. This study provides a detailed analysis of the unique ISCs mechanism in gel-electrolytes, emphasizing the differences between gel-electrolyte and liquid-electrolyte batteries in terms of ion transport dynamics and thermal performance. Based on these characteristics, an electrochemical–thermal–ISC coupling model was developed, and an external short-circuit resistance test was conducted to validate the model’s accuracy. By simulating various ISC states using the coupling model, a comprehensive dataset of battery ISC parameters was obtained, encompassing voltage, current, temperature, SOC, capacity loss, and internal resistance. ISC prediction models were subsequently developed using BP, CNN, and LSTM networks, with a comparative analysis of their prediction accuracy. This research advances the ISC prediction framework for gel-electrolyte batteries and demonstrates the potential of CNN-based models to achieve higher accuracy in fault prediction. Accurate ISC prediction is crucial for ensuring safe battery operation in electric vehicles. Full article

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

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14 pages, 3043 KB

Open AccessArticle

Enhanced Electrochemical Performance of Surface-Modified LiNi_0.5Mn_1.5O₄ Cathode at High Voltages

by Zeng Yan, Songsong Wang, Fulong Hu, Shuai Lu, Yang Liu, Qian Peng, Zhen Yao and Wei Liu

Batteries 2026, 12(2), 44; https://doi.org/10.3390/batteries12020044 - 26 Jan 2026

Abstract

Spinel LiNi_0.5Mn_1.5O₄ (LNMO) has emerged as a highly competitive cobalt-free cathode material for higher-energy-density lithium-ion batteries. However, its practical application is hindered by severe capacity degradation, particularly under high-voltage operation. To solve this problem, we put forward a [...] Read more.

Spinel LiNi_0.5Mn_1.5O₄ (LNMO) has emerged as a highly competitive cobalt-free cathode material for higher-energy-density lithium-ion batteries. However, its practical application is hindered by severe capacity degradation, particularly under high-voltage operation. To solve this problem, we put forward a surface modification strategy employing a Li_0.4La_0.54TiO₃ (LLTO) coating. The LLTO coating forms a protective cathode–electrolyte interphase that helps to inhibit interfacial side reactions, enabling enhanced electrochemical performance up to 5 V. As a result, the optimized 1 wt% LLTO-coated LNMO exhibits a remarkable capacity retention of 96.5% after 200 cycles at 0.1 C and delivers a high-rate capacity of 103.5 mAh g⁻¹ at 2 C, significantly outperforming its pristine counterpart (86.8% and 89.6 mAh g⁻¹, respectively). This work provides a viable and efficient surface modification approach for achieving robust high-voltage LNMO cathode material, underscoring its great potential for next-generation energy storage systems. Full article

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

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30 pages, 24329 KB

Open AccessArticle

Selective Extraction of Lithium from Li Batteries by Leaching the Black Mass in Oxalic Acid

by Kristina Talianova, Martina Laubertová, Zita Takáčová, Jakub Klimko, Jaroslav Briančin, Simon Nagy and Dušan Oráč

Batteries 2026, 12(2), 43; https://doi.org/10.3390/batteries12020043 - 25 Jan 2026

Abstract

In this work, a method for leaching black mass from spent Li batteries using oxalic acid was developed and experimentally verified with the objective of selectively separating lithium and cobalt. Oxalic acid proved to be an efficient and selective leaching agent. Under 1 [...] Read more.

In this work, a method for leaching black mass from spent Li batteries using oxalic acid was developed and experimentally verified with the objective of selectively separating lithium and cobalt. Oxalic acid proved to be an efficient and selective leaching agent. Under 1 M C₂H₂O₄, 120 min, L:S = 20, 80 °C and 300 rpm, a lithium yield of 90% was achieved, while cobalt dissolution remained low at 1.57%. Subsequently, cobalt spontaneously precipitated from the leachate within several hours, and the solid phase was fully separated after 24 h. The leachate contained minor amounts of accompanying metals, with dissolution yields of 0.5% Mn, 8% Fe and 1.4% Cu. These impurities were removed from the leachate by controlled pH adjustment using NaOH at ambient temperature and 450 rpm, with complete precipitation at pH 12. This procedure generated a purified lithium-rich leachate, which was converted into lithium oxalate by crystallisation at 105 °C. Subsequent calcination of the resulting solid at 450 °C for 30 min produced Li₂CO₃ with a purity of 91%. Based on the experimental findings, a conceptual technological process for selective lithium leaching using oxalic acid was proposed, demonstrating the potential of this method for sustainable lithium recovery. Full article

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

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27 pages, 3544 KB

Open AccessArticle

Dynamic Estimation of Load-Side Virtual Inertia with High Power Density Support of EDLC Supercapacitors

by Adrián Criollo, Dario Benavides, Danny Ochoa-Correa, Paul Arévalo-Cordero, Luis I. Minchala-Avila and Daniel Jerez

Batteries 2026, 12(2), 42; https://doi.org/10.3390/batteries12020042 - 23 Jan 2026

Abstract

The increasing penetration of renewable energy has led to a decrease in system inertia, challenging grid stability and frequency regulation. This paper presents a dynamic estimation framework for load-side virtual inertia, supported with high-power-density electrical double-layer supercapacitors (EDLCs). By leveraging the fast response [...] Read more.

The increasing penetration of renewable energy has led to a decrease in system inertia, challenging grid stability and frequency regulation. This paper presents a dynamic estimation framework for load-side virtual inertia, supported with high-power-density electrical double-layer supercapacitors (EDLCs). By leveraging the fast response and high power density of EDLCs, the proposed method enables the real-time emulation of demand-side inertial behavior, enhancing frequency support capabilities. A hybrid estimation algorithm has been developed that combines demand forecasting and adaptive filtering to track virtual inertia parameters under varying load conditions. Simulation results, based on a 150 kVA distributed system with 27% renewable penetration and 33% demand variability, demonstrate the effectiveness of the approach in improving transient stability and mitigating frequency deviations within ±0.1 Hz. The integration of ESS-based support offers a scalable and energy-efficient solution for future smart grids, ensuring operational reliability under real-world variability. Full article

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

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14 pages, 637 KB

Open AccessArticle

Research on the State of Charge Estimation of Electric Forklift Batteries Based on an Improved Transformer Model

by Jia Wang, Shenglong Zhang and Xia Hu

Batteries 2026, 12(2), 41; https://doi.org/10.3390/batteries12020041 - 23 Jan 2026

Abstract

The state of charge (SoC) is one of the critical parameters in battery management systems, as it directly determines the operational safety and reliability of batteries. To accurately predict the SoC of an electric forklift under varying operating conditions, two surrogate models, an [...] Read more.

The state of charge (SoC) is one of the critical parameters in battery management systems, as it directly determines the operational safety and reliability of batteries. To accurately predict the SoC of an electric forklift under varying operating conditions, two surrogate models, an improved Transformer and an improved Transformer 2, are developed. The experimental data obtained through real-vehicle tests are multi-dimensional and contain multiple sources of noise, resulting in poor prediction accuracy when only a single preprocessing algorithm is used. Therefore, this paper first discusses the effect of the preprocessing algorithms on SoC estimation. Compared with the original experimental data and the Kalman filter algorithm, the Kalman filter–principal component analysis (PCA) method is more suitable for preprocessing the original electric forklift data. The mean absolute error (MAE) and root mean square error (RMSE) of the improved Transformer model obtained using the Kalman filter – PCA method are reduced by 26.32% and 27.73% respectively, compared to the single Kalman method. Then, this study investigates the impact of data with different dimensions on the prediction performance of the improved Transformer mode. The results show that five-dimensional data can more effectively train the improved Transformer model, since the MAE decreases by 14.63% and 19.54%, and the RMSE decreases by 14.85% and 20.37% compared to three-dimensional and seven-dimensional data. Through the analysis of the improved Transformer model, an improved Transformer 2 model with higher prediction accuracy is obtained. Then, the improved Transformer 2 model is compared with the LSTM and CNN algorithms. The results indicate that the improved Transformer 2 model can predict SoC more stably and accurately than the single LSTM and CNN algorithms. Specifically, compared with the LSTM model, the proposed Transformer 2 model reduces the MAE by 77.16% and the RMSE by 91.75%. In comparison with the CNN model, the MAE is reduced by 71.81% and the RMSE by 80%. 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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16 pages, 4145 KB

Open AccessArticle

Improving the Effective Utilization of Liquid Nitrogen for Suppressing Thermal Runaway in Lithium-Ion Battery Packs

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

Batteries 2026, 12(2), 40; https://doi.org/10.3390/batteries12020040 - 23 Jan 2026

Abstract

In recent years, the energy revolution has driven the rapid development of lithium-ion batteries (LIBs). A fire suppression system capable of rapidly and effectively extinguishing LIB fires constitutes the last line of defense for ensuring the safe operation of the LIB industry. In [...] Read more.

In recent years, the energy revolution has driven the rapid development of lithium-ion batteries (LIBs). A fire suppression system capable of rapidly and effectively extinguishing LIB fires constitutes the last line of defense for ensuring the safe operation of the LIB industry. In this study, an experimental platform simulating the storage environment of LIBs in energy-storage stations was constructed, and liquid nitrogen (LN) was employed to conduct fire suppression tests on LIBs. The effective utilization of 17.4 kg of LN during the suppression process inside the battery module was quantified. In addition, fire compartments were established within the battery module, and a strategy for enhancing the LN suppression effectiveness was proposed. The results indicate that, without intervention, the thermal runaway propagation (TRP) rate within the LIB module gradually accelerates. After LN injection, the effective utilization of LN for extinguishing individual LIBs decreases progressively along the sequence of TRP. Creating fire compartments inside the PACK using 6 mm aerogel blankets effectively reduces the transfer of energy from the region undergoing thermal runaway (TR) to other regions, while simultaneously enhancing the extinguishing performance of LN. Under the same LN dosage, the introduction of fire compartments increases the effective utilization from 0.037 to 0.051. However, as the compartment volume decreases, the degree of improvement in LN utilization is reduced. This work is expected to provide guidance for the engineering application of LN-based fire suppression systems to inhibit LIB TR and its propagation. Full article

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21 pages, 6329 KB

Open AccessArticle

Transfer Learning-Enhanced Safety Modeling for Lithium-Ion Batteries Under Mechanical Abuse

by Hong Liang, Renjing Gao, Haihe Zhao and Zeyu Chen

Batteries 2026, 12(2), 39; https://doi.org/10.3390/batteries12020039 - 23 Jan 2026

Abstract

The widespread adoption of lithium-ion battery-powered electric vehicles has raised increasing concerns regarding battery safety under mechanical abuse conditions. However, mechanical abuse scenarios, such as battery extrusion, are highly diverse, making it impractical to conduct extensive destructive experiments and independent modeling for each [...] Read more.

The widespread adoption of lithium-ion battery-powered electric vehicles has raised increasing concerns regarding battery safety under mechanical abuse conditions. However, mechanical abuse scenarios, such as battery extrusion, are highly diverse, making it impractical to conduct extensive destructive experiments and independent modeling for each specific scenario. In this work, a cross-scenario mechanical safety modeling framework for lithium-ion batteries is proposed based on transfer learning. Three quasi-static mechanical abuse tests, including flat-plate, rigid-rod, and hemispherical compression, are conducted on 18650 lithium-ion batteries. An equivalent mechanical model with a spring–damper parallel structure is employed to characterize the mechanical response and generate simulation data. Based on data from a single mechanical abuse scenario, a backpropagation neural network (BPNN)-based safety model is established to predict the maximum stress in the battery. The learned knowledge is then transferred to other mechanical abuse scenarios using a transfer learning strategy. The results demonstrate that, under limited target-domain data, the transferred models achieve stable prediction performance, with the average relative error controlled within 3.6%, outperforming models trained from scratch under the same conditions. Compared with existing studies that focus on single-scenario modeling, this work explicitly investigates cross-scenario transferability and demonstrates the effectiveness of transfer learning in reducing experimental and modeling effort for battery mechanical safety analysis. Full article

(This article belongs to the Topic Battery Design and Management, 2nd Edition)

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2 pages, 132 KB

Open AccessCorrection

Correction: Zeng et al. Sustainable Synthesis of a Carbon-Supported Magnetite Nanocomposite Anode Material for Lithium-Ion Batteries. Batteries 2024, 10, 357

by Hui Zeng, Jiahui Li, Haoyu Yin, Ruixin Jia, Longbiao Yu, Hongliang Li and Binghui Xu

Batteries 2026, 12(2), 38; https://doi.org/10.3390/batteries12020038 - 23 Jan 2026

Abstract

The authors would like to make the following corrections to their published paper [...] Full article

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

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

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

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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