Batteries

18 pages, 1667 KB

Open AccessArticle

Multi-Source Impedance and Discharge Feature Learning for Cross-Battery State-of-Health Estimation of Lithium-Ion Batteries

by Syed Adil Sardar, Farhan Akhtar, Wajid Ali and Woo Young Kim

Batteries 2026, 12(8), 291; https://doi.org/10.3390/batteries12080291 - 6 Aug 2026

Abstract

Accurate state-of-health (SOH) estimation is essential for the reliable operation of lithium-ion batteries. However, predicting SOH for previously unseen batteries remains challenging because degradation behavior varies among cells. This study proposes a multi-source feature-learning framework that combines electrochemical impedance spectroscopy (EIS), discharge-profile, and [...] Read more.

Accurate state-of-health (SOH) estimation is essential for the reliable operation of lithium-ion batteries. However, predicting SOH for previously unseen batteries remains challenging because degradation behavior varies among cells. This study proposes a multi-source feature-learning framework that combines electrochemical impedance spectroscopy (EIS), discharge-profile, and aging-related information for cross-battery SOH estimation. EIS and discharge data from 34 batteries in the National Aeronautics and Space Administration (NASA) battery aging dataset are processed to construct 1830 matched impedance–SOH samples. A total of 101 features are extracted from raw and rectified impedance spectra, resampled impedance points, NASA-provided impedance parameters, discharge profiles, and cycle-related information. Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), Histogram Gradient Boosting (HGB), and Extreme Gradient Boosting (XGBoost) models are evaluated using random sample splitting and strict battery-wise validation. Under random validation, GB achieves the best performance, with a coefficient of determination (R²) of 0.9788. Under strict battery-wise validation, ET achieves a mean absolute error (MAE) of 4.9131 percentage points, a root mean square error (RMSE) of 7.3664 percentage points, and an R² of 0.7855. The performance difference between the two validation strategies demonstrates the importance of battery-grouped evaluation when assessing generalization to unseen cells. Overall, the results indicate that combining impedance- and discharge-derived information provides a promising basis for cross-battery SOH estimation, although further leakage-controlled validation across broader operating conditions is required. Full article

23 pages, 2772 KB

Open AccessArticle

Hybrid Elephant Herding and Golden Eagle Optimization-Based Extended Kalman Filter for State of Charge Estimation of Energy Storage Batteries

by Wei Wang, Zhenchao Ren, Junlin Wang and Lei Zhang

Batteries 2026, 12(8), 290; https://doi.org/10.3390/batteries12080290 - 6 Aug 2026

Abstract

The Extended Kalman Filter (EKF) serves as a widely utilized approach to evaluate the state of charge (SOC) of energy storage batteries. However, the conventional EKF is commonly adversely affected by ambient temperature variations, uncertain noise matrices, and inaccurate parameter estimation in practice. [...] Read more.

The Extended Kalman Filter (EKF) serves as a widely utilized approach to evaluate the state of charge (SOC) of energy storage batteries. However, the conventional EKF is commonly adversely affected by ambient temperature variations, uncertain noise matrices, and inaccurate parameter estimation in practice. Therefore, hybrid elephant herding and golden eagle optimization based EKF (HEGO) is introduced to enhance the precision and effectiveness of battery SOC estimation. The local contraction capability of elephant herding optimization is utilized to narrow the search range within a predefined search space and accurately locate the region of the optimal solution. Within the narrowed search range provided by EHO, golden eagle optimization (GEO) is then employed to accurately identify the noise matrix and equivalent circuit parameters appropriate for the current state, thereby increasing the precision and resilience of SOC estimations against environmental disturbances. Data for an 18650-battery evaluated with the Federal Urban Driving Schedule (FUDS), Dynamic Stress Test (DST), and Hybrid Pulse Power Characterization (HPPC) conditions were collected using an experimental platform, and the proposed algorithm was experimentally validated. The results demonstrate that, across different temperatures and operating conditions, the proposed algorithm consistently achieves optimal performance, with a mean absolute error below 0.7% and strong generalization, thereby providing stable and reliable technical support for battery SOC estimation. Full article

23 pages, 15413 KB

Open AccessArticle

AWGLFuser: A Global–Local Feature Fusion Network with Adaptive Wavelet Filter for Lithium-Ion Battery State-of-Health Estimation

by Ge Song, Zhihong Zhang, Yuqiao Deng and Yong Zhou

Batteries 2026, 12(8), 289; https://doi.org/10.3390/batteries12080289 - 6 Aug 2026

Abstract

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is critical to ensuring the safety and stability of energy storage and power systems. However, existing SOH evaluation methods fall short in adaptive denoising, multi-scale feature extraction, and effective fusion of global [...] Read more.

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is critical to ensuring the safety and stability of energy storage and power systems. However, existing SOH evaluation methods fall short in adaptive denoising, multi-scale feature extraction, and effective fusion of global and local information. To overcome these limitations, this paper proposes a global–local feature fusion network with an adaptive wavelet filter (AWGLFuser) for end-to-end SOH estimation. The proposed model consists of three modules: an adaptive wavelet filter (AWF) module to suppress high-frequency noise and highlight key information in the frequency domain; a global–local feature extraction (GLFE) module to capture both global and local temporal dependencies at multiple scales; and a bidirectional cross-attention fusion (BCAF) module to enable deep interaction between global and local features, thereby facilitating their effective fusion. Comparison experiments on the NASA and XJTU datasets demonstrate that the proposed model yields lower estimation errors than the eight competing models. The average reductions in MAE, MAPE, and RMSE are 44.158%, 44.209%, and 38.014%, respectively, and this improvement is statistically significant against every comparison model. Furthermore, ablation studies clarify what each component contributes, with the modules proving mutually reinforcing when combined. AWGLFuser also attains a compact parameter count and storage footprint with competitive inference latency, despite comparatively higher FLOPs. Full article

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24 pages, 1537 KB

Open AccessArticle

Data-Driven Modeling of Auxiliary Consumption in Utility-Scale BESS

by Aleksandar Dimovski, Matteo Spiller, Mershad Pakjoo, Giulio Cantoni, Giacomo Gorni, Luigi Piegari and Marco Merlo

Batteries 2026, 12(8), 288; https://doi.org/10.3390/batteries12080288 - 6 Aug 2026

Abstract

Accurately modeling the auxiliary power consumption of Battery Energy Storage Systems (BESSs) is increasingly important as grid-scale storage assets are becoming involved in electricity markets. In this paper, we develop a data-driven framework to characterize and forecast auxiliary consumption using operational data from [...] Read more.

Accurately modeling the auxiliary power consumption of Battery Energy Storage Systems (BESSs) is increasingly important as grid-scale storage assets are becoming involved in electricity markets. In this paper, we develop a data-driven framework to characterize and forecast auxiliary consumption using operational data from a utility-scale BESS deployed in Italy. To capture the short-term thermal inertia of the system and the delayed response of the cooling systems, a set of predictors based on moving averages of power and ambient temperature is constructed. Two novel modeling approaches are proposed: a three-dimensional look-up table (LUT) representation that provides an interpretable characterization of system behavior, as well as a Random Forest (RF) regression model capable of capturing complex non-linear relationships between parameters. These are evaluated in comparison with a two-dimensional LUT from the literature. The analysis showed a superior performance of the RF model that comes at the cost of reduced interpretability and computational efficiency, while both proposed models outperform the literature-based LUT. Moreover, the impact of the number and type of predictors on model performance is systematically assessed, to shed light on what constitutes the requirements for a reasonably accurate estimation of the auxiliary systems. Finally, the concept of forecasting uncertainty for the temperature and day-ahead forecasting applicability for the auxiliary systems is investigated by introducing a persistence-based logic and an evaluation of the impact on the results. Overall, the study highlights the importance of explicitly modeling auxiliary consumption in grid-scale BESSs, proposes well-performing models for its estimation, and provides practical guidelines for their implementation in energy management and forecasting applications. Full article

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

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48 pages, 35599 KB

Open AccessArticle

LightBAL: An AI-Based Model for EfficientActive Balancing in Electric Vehicle Battery Management Systems

by Khayri Abu Sayf, Main Hammad Nazir, Leshan Uggalla and Abdulla Rahil

Batteries 2026, 12(8), 287; https://doi.org/10.3390/batteries12080287 - 5 Aug 2026

Abstract

In this paper, we present LightBAL, an ultra-lightweight deep learning framework for real-time active cell balancing and onboard balancing control in electric vehicle (EV) battery management systems (BMSs). Although active cell balancing can improve battery utilisation and performance, applying deep learning-based balancing control [...] Read more.

In this paper, we present LightBAL, an ultra-lightweight deep learning framework for real-time active cell balancing and onboard balancing control in electric vehicle (EV) battery management systems (BMSs). Although active cell balancing can improve battery utilisation and performance, applying deep learning-based balancing control strategies remains prohibitive in typical embeddable BMS platforms because of the computational complexity and inference latency of deep models. In response to this issue, we propose an AI-physics-informed controller that forecasts the voltage difference of a single cell, the SoC variation, and the optimal balancing current based on proportional feedback closed-loop (FCLL) control. The introduced framework exploits wavelet-based adaptive denoising, multi-scale hierarchical feature learning using a cooperative Principal Component Analysis (PCA) and autoencoder feature extraction technique, and a lightweight One-Dimensional Convolutional Neural Network (Conv1D) coupled with Bidirectional Long Short-Term Memory (BiLSTM) (Conv1D-BiLSTM). The implemented lightweight network is further trained by model compression methodologies such as knowledge distillation and 8-bit quantisation-aware training, aiming for efficient deployment on edge devices. Experimental validation on the multivariate battery time-series dataset demonstrates that LightBAL achieves an F1-score of 96.64%, a balancing efficiency of 94.30%, and a Mean Absolute Error (MAE) of 0.0379, outperforming methods based on conventional ANN, LSTM, and CNN. LightBAL without compression takes only 1.26 s to conclude on a PC workstation; the inference latency of the embedded light model is as low as 28.7 ms. In addition, hardware-in-the-loop (HIL) validation on the Raspberry Pi 4 platform indicates that the framework can fulfil real-time inference requirements under normal operating conditions, taking 28.7 ms per balancing process. Simulation shows that the proposed approach significantly decreases cumulative balancing energy loss by 12.4% across several driving cycle conditions. Full article

(This article belongs to the Special Issue Battery Management System, Thermal Management System, Digital Twin and Model Predictive Control for Next-Generation Smart Battery Systems)

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30 pages, 7641 KB

Open AccessArticle

Power Converter-Based Impedance Spectroscopy for Supercapacitors: Theory, Simulation and Experimental Verification

by Diego Alejandro Herrera-Jaramillo, Juan David Bastidas-Rodríguez and Carlos Andrés Ramos-Paja

Batteries 2026, 12(8), 286; https://doi.org/10.3390/batteries12080286 - 5 Aug 2026

Abstract

This paper addresses the need for cost-effective and integrated impedance spectroscopy (IS) techniques for supercapacitors (SCs), particularly in applications where conventional frequency response analyzers (FRAs) are impractical due to their high cost and lack of portability. A power converter-based methodology is proposed to [...] Read more.

This paper addresses the need for cost-effective and integrated impedance spectroscopy (IS) techniques for supercapacitors (SCs), particularly in applications where conventional frequency response analyzers (FRAs) are impractical due to their high cost and lack of portability. A power converter-based methodology is proposed to perform IS using a power electronics interface, enabling in situ characterization of SCs. The approach is based on an analytical formulation that relates the amplitude of the duty-cycle perturbation introduced into the converter with the excitation frequency and the desired sinusoidal current amplitude, allowing the direct generation of frequency-dependent excitation signals using the power converter. The proposed methodology is first validated using circuital simulations, demonstrating accurate impedance estimation with a Range-Average Absolute Error (RAAE) of

0.30 %

in magnitude and

1.94 %

in phase compared with a reference simulation. Experimental validation is then conducted using a synchronous converter controlled by a digital signal processor, and those results are benchmarked against a commercial FRA, obtaining an experimental RAAE of

4.88 %

in magnitude and

2.41 %

in phase. These discrepancies are mainly attributed to limitations in the excitation and measurement stages. In addition to its accuracy, the proposed approach significantly reduces implementation cost. The converter-based setup relies on standard power electronics hardware and conventional laboratory instrumentation, with an estimated cost of approximately

$ 2000

USD, which is much cheaper than commercial FRA-based systems (up to $60,000 USD). These results demonstrate that the proposed methodology provides a practical and scalable alternative for impedance spectroscopy of supercapacitors, enabling embedded and in situ diagnostics of energy storage systems. Full article

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

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18 pages, 2724 KB

Open AccessArticle

Multi-Scale Consistency Analysis and Unsupervised Detection of Energy Storage Batteries for Asynchronous Sampling

by Guozhi Huang, Shijie Li, Chao Wang, Yujie Wang, Ming Jin, Peng Guo, Kun Jia, Huangwang Mai, Yong Zang, Yingmeng Zhang, Gongsheng Song, Guobin Zhong, He Zhao and Qianqian Hu

Batteries 2026, 12(8), 285; https://doi.org/10.3390/batteries12080285 - 4 Aug 2026

Abstract

Consistency monitoring is essential for safe battery energy storage system operation, yet practical data often exhibit asynchronous sampling. This study proposes a tick-driven multi-scale consistency analysis and unsupervised detection method for energy storage batteries. Actual cell-voltage update instants are used as analysis ticks, [...] Read more.

Consistency monitoring is essential for safe battery energy storage system operation, yet practical data often exhibit asynchronous sampling. This study proposes a tick-driven multi-scale consistency analysis and unsupervised detection method for energy storage batteries. Actual cell-voltage update instants are used as analysis ticks, while high-frequency string-level variables are aggregated over adjacent tick intervals to describe operating conditions. Voltage-dispersion features are then used for anomaly scoring and cell-level localization. The method was evaluated using two-day station data and a controlled 20 Ah 16-series module experiment. In the station dataset, 190 effective ticks were extracted, and a transient consistency deterioration at 24,023 s showed a voltage range of 0.049 V and a standard deviation of 0.0057 V. In the controlled experiment, 185 ticks were obtained from 166,797 voltage samples after 900 s batch resampling, reducing cell-level evaluation instances by over 99%; cell #13 was identified as the dominant high-response cell. The method provides an interpretable framework for consistency monitoring and abnormal-cell localization under asynchronous sampling. Full article

(This article belongs to the Special Issue Advanced Architecture and Intelligent Thermal Management of Battery Systems)

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16 pages, 1979 KB

Open AccessArticle

Retrieval-Augmented Degradation Priors for Data-Efficient Early Lithium-Ion Battery Lifetime Prediction

by Yuelin Zou, Yajun Zhang and Ke Lv

Batteries 2026, 12(8), 284; https://doi.org/10.3390/batteries12080284 - 4 Aug 2026

Abstract

Early lithium-ion battery lifetime prediction is difficult because only a small fraction of a cell’s lifetime is observed when a prediction is needed. We evaluate a transparent, validation-tuned combination of a supervised early-cycle predictor and a nearest-neighbor retrieval estimate built from similar historical [...] Read more.

Early lithium-ion battery lifetime prediction is difficult because only a small fraction of a cell’s lifetime is observed when a prediction is needed. We evaluate a transparent, validation-tuned combination of a supervised early-cycle predictor and a nearest-neighbor retrieval estimate built from similar historical cells. The method is assessed against supervised-only and retrieval-only baselines over 20 repeated cell-wise splits. On the Severson/Toyota Research Institute/Massachusetts Institute of Technology (Severson/TRI/MIT) dataset (124 lithium iron phosphate/graphite cells), fusion reduces the mean absolute error (MAE) of end-of-life (EOL) prediction by 10.08 cycles at 10 observed cycles and 11.90 cycles at 20 observed cycles relative to the validation-selected supervised baseline; the confidence intervals for these gains exclude zero, whereas gains at 50 and 100 cycles are unsupported. Retrieval-only prediction is competitive in the shortest windows. In a targeted 40-split external confirmation on BatteryLife-XJTU (Xi’an Jiaotong University) at 50 observed cycles, fusion reduces mean MAE from 34.11 to 28.02 cycles (paired bootstrap 95% confidence interval for the gain: 0.47–11.41 cycles; one-sided Wilcoxon

p = 0.007

). The results support retrieval as an interpretable complement to supervised prediction in compatible weak-signal regimes, not as a universal replacement for supervised models. Full article

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16 pages, 7397 KB

Open AccessArticle

Three-Dimensional Physics-Based Circuit Modeling of Battery Incipient Faults in Microgrid Applications

by Ziqi Wang, Junya Shao and Mo-Yuen Chow

Batteries 2026, 12(8), 283; https://doi.org/10.3390/batteries12080283 - 3 Aug 2026

Abstract

Battery condition directly affects the reliability, resilience, and safety of microgrids, making physically interpretable battery information an important input to situation awareness. In practical microgrid operation, battery monitoring commonly relies on terminal signals available to the supervisory energy-management layer. These signals do not [...] Read more.

Battery condition directly affects the reliability, resilience, and safety of microgrids, making physically interpretable battery information an important input to situation awareness. In practical microgrid operation, battery monitoring commonly relies on terminal signals available to the supervisory energy-management layer. These signals do not directly reveal early internal degradation. This paper develops a three-dimensional physics-based circuit modeling framework for battery incipient fault information generation in microgrid situation awareness. A high-fidelity electrochemical model was employed offline as a virtual battery to generate controllable reference data that are difficult to obtain comprehensively through physical experiments. This virtual reference serves as the benchmark for validating the proposed circuit model. The three-dimensional physics-based circuit model then acts as the real-time modeling core, representing distributed electrochemical and thermal behavior at substantially lower computational cost. Across the evaluated discharge cases, the circuit model voltage outputs show that mean relative errors remain below 1.3% with respect to the virtual battery reference while achieving a tenfold reduction in mean solver time per step. The proposed model therefore provides an online-oriented foundation for extending battery-related situation awareness in microgrids from terminal-signal monitoring toward the model-informed representation of internal degradation. Full article

(This article belongs to the Special Issue Advancements in Battery Fault Modeling, Detection, Diagnosis, and Digital Twin Technologies)

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41 pages, 1319 KB

Open AccessReview

Mechanical Intrusion of Aged Lithium-Ion Batteries: Boundary-Condition Continuity, Trigger Definitions, and the Limits of Cross-Study Comparability

by Richard Polzer, Carlos Antônio Rufino Júnior, Sergej Diel and Hans-Georg Schweiger

Batteries 2026, 12(8), 282; https://doi.org/10.3390/batteries12080282 - 1 Aug 2026

Abstract

Indentation and crush tests are widely used as crash-relevant surrogates for mechanical intrusion in Lithium-Ion Batteries (LIBs), yet their interpretation still relies on macroscopic, binary pass/fail criteria established at the Beginning of Life (BoL). This neglects how calendar and cyclic degradation alter a [...] Read more.

Indentation and crush tests are widely used as crash-relevant surrogates for mechanical intrusion in Lithium-Ion Batteries (LIBs), yet their interpretation still relies on macroscopic, binary pass/fail criteria established at the Beginning of Life (BoL). This neglects how calendar and cyclic degradation alter a cell’s mechanical, thermal, and structural state before abuse, leaving the literature on aged cells apparently contradictory. This review establishes an aging-aware interpretive framework that separates three levels commonly conflated: the boundary conditions governing deformation, the study-specific criteria and evidence strength behind a declared electrical event, and the post-trigger hazard progression captured within the available monitoring window. A descriptive synthesis of 22 normalized fresh-to-aged comparisons from 13 primary studies shows no universal aging-driven shift of the reported comparison event toward earlier or later force–displacement states, with force and displacement changing in the same direction in only about half of the cases. An apparent format-associated displacement pattern is substantially attenuated when the pouch-cell displacement ratios are normalized by the respective initial cell thicknesses: the pouch-cell median displacement ratio decreases from 1.07 to approximately 1.00, and the remaining variation differs across aging routes and states of charge, although their individual contributions cannot be separated in the available dataset. Many apparent contradictions likewise stem from non-equivalent mechanical boundary conditions and trigger definitions, and continuity of confinement—especially for pouch cells aged under stack pressure and tested in a relaxed state—is identified as a frequently overlooked interpretive variable, although no controlled aged-cell study has yet isolated its contribution. An earlier-reported trigger does not necessarily imply a more severe outcome. We distill these requirements into a tier-resolved reporting checklist, providing a more rigorous and reusable basis for abuse-test interpretation, model development, and safety qualification of batteries intended for long service life and second-life deployment. Full article

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

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Graphical abstract

17 pages, 5448 KB

Open AccessArticle

W-Doped LiMn_0.6Fe_0.4PO₄/C as a High-Performance Cathode

by Sha Li, Yizhou Cao, Xinyi Wang, Junhao Zhao, Wenbin Li, Hongxu Li, Fangkun Li and Suqin Liu

Batteries 2026, 12(8), 281; https://doi.org/10.3390/batteries12080281 - 1 Aug 2026

Abstract

The inferior electronic conductivity, sluggish bulk Li⁺ transport, and Jahn–Teller distortion intrinsic to Mn³⁺ collectively impede the practical application of LiMn_0.6Fe_0.4PO₄ (LMFP) as a high-performance cathode for lithium-ion batteries. Herein, a series of W-doped Li(Mn_0.6 [...] Read more.

The inferior electronic conductivity, sluggish bulk Li⁺ transport, and Jahn–Teller distortion intrinsic to Mn³⁺ collectively impede the practical application of LiMn_0.6Fe_0.4PO₄ (LMFP) as a high-performance cathode for lithium-ion batteries. Herein, a series of W-doped Li(Mn_0.6Fe_0.4)_1−xW_xPO₄/C (x = 0, 0.005, 0.010, 0.015) cathode materials were synthesized via spray-drying combined with carbothermal reduction. Rietveld refinement indicated decreases in the fitted lattice parameters and unit-cell volume with increasing nominal W content, and no crystalline secondary phases were detected within the laboratory XRD detection limit. Although the structural evolution is consistent with W incorporation, direct determination of the occupation site requires further local structural characterization. X-ray photoelectron spectroscopy indicated that the detectable near-surface W species are predominantly present as W⁶⁺, and the semi-quantitative Mn 2p peak-area fitting showed that the fitted relative Mn³⁺ contribution decreased from 70.4% in LMFP-0 to 58.3% in LMFP-2. The optimal composition (LMFP-2, x = 0.010) delivers an initial discharge capacity of 160.2 mAh g⁻¹ at 0.1 C, retains 98.1% capacity after 100 cycles at 1 C, and achieves 126.3 mAh g⁻¹ at 5 C. Electrochemical impedance spectroscopy reveals that LMFP-2 possesses the lowest charge-transfer resistance (195.4 Ω) and the highest Li⁺ diffusion coefficient (5.3 × 10⁻¹⁵ cm² s⁻¹). These improvements may be attributed to the synergistic effects of enhanced bulk electronic conductivity, accelerated Li⁺ diffusion kinetics, and improved structural stability induced by moderate W incorporation. This work establishes W doping as a viable compositional engineering strategy for olivine-based cathode materials. Full article

(This article belongs to the Section Electrolyte and Interfacial Engineering)

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27 pages, 3100 KB

Open AccessArticle

Internal Short Circuit Detection in Lithium-Ion Batteries Under Shipboard Vibration: A Unified Model-Based and Data-Driven Benchmark with NPU-Accelerated Inference

by Jaehee Hong and Taeho Im

Batteries 2026, 12(8), 280; https://doi.org/10.3390/batteries12080280 - 31 Jul 2026

Abstract

Shipboard lithium-ion battery systems experience continuous mechanical vibration, yet model-based and data-driven internal short-circuit (ISC) detectors have not been compared under such conditions. We present, to our knowledge, the first unified vibration-aware ISC benchmark: a model-based Extended Kalman Filter (EKF)

Δ SOC

rule [...] Read more.

Shipboard lithium-ion battery systems experience continuous mechanical vibration, yet model-based and data-driven internal short-circuit (ISC) detectors have not been compared under such conditions. We present, to our knowledge, the first unified vibration-aware ISC benchmark: a model-based Extended Kalman Filter (EKF)

Δ SOC

rule and three convolutional detectors—ModernTCN, LITE, and NPU-Conv2D—are evaluated on a simulated NCR18650PF module under quiescent, MIL-STD-810H-derived, and head-sea vibration, with vibration coupled to cell resistance through a phenomenological assumption

Δ R = k_{R} | a |

. The EKF observes the terminal voltage alone, whereas the data-driven detectors additionally observe cell temperature, so the comparison couples detector class with input observability. Under this assumed envelope and the swept coupling range, the dual-channel data-driven configurations pass

135 / 135

deadline-scored outcomes against

129 / 135

for the EKF, with zero pre-onset false alarms versus 15, and their advantage lies in detection-delay dispersion rather than in mean latency. Deployed on an STM32N6 microcontroller, the INT8 NPU-Conv2D completes one inference in

0.752

ms,

179 \times

lower latency than the EKF firmware. Calibration-free robustness emerges as the practical advantage of the evaluated dual-channel detectors. Full article

(This article belongs to the Section Emerging Battery Systems)

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13 pages, 15004 KB

Open AccessArticle

Extraction and Reconstruction of Carbon Components from Coal Gasification Fine Slag for Li-O₂ Battery Cathode Catalysts

by Chunlei Gao, Shuxuan Ma, Yongbing Liu, Yu Zhang, Fenghua Cai, Meng Li, Yunbo Wang, Zhihui Sun and Wei Jiang

Batteries 2026, 12(8), 279; https://doi.org/10.3390/batteries12080279 - 29 Jul 2026

Abstract

The ever-increasing output of coal gasification slag from coal gasification technology has created an urgent need for its efficient disposal. In this study, the fine-slag fraction is uti-lized as a precursor to prepare a carbon electrocatalyst for Li-O₂ batteries. Residual carbon was [...] Read more.

The ever-increasing output of coal gasification slag from coal gasification technology has created an urgent need for its efficient disposal. In this study, the fine-slag fraction is uti-lized as a precursor to prepare a carbon electrocatalyst for Li-O₂ batteries. Residual carbon was extracted from coal gasification fine slag via an integrated alkali-acid co-activation process. Subsequently, hydrothermal modification reconstructed the carbon microstructure and strengthened its coupling with intrinsic Ca-containing species. The as-prepared slag-derived catalyst improved oxygen-reaction kinetics at the battery cathode and effectively reduced polarization. The Li-O₂ cells delivered a low charge–discharge voltage gap of 1.02 V, a high discharge specific capacity of 9480 mAh g⁻¹, and stable long-term cycling for more than 870 h. The enhanced electrochemical performance arises from the synergistic interplay among the reconstructed carbon framework, surface defects, oxygen-containing functional groups, and Ca-containing species, with the latter serving as a major source of active sites for the oxygen electrocatalytic reactions. This work establishes a resource-utilization pathway from coal gasification slag to high-end energy-storage applications, aligning with green development goals to address both mining-area energy demands and solid-waste pollution. Full article

(This article belongs to the Topic Advanced Battery Materials and Technologies)

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14 pages, 3008 KB

Open AccessArticle

Gravure-Printed High-Energy Cathodes for Lithium-Ion Batteries Based on NMC 111 Active Material: The Challenge of Ink Formulation

by Maria Montanino, Claudia Paoletti, Anna De Girolamo Del Mauro and Giuliano Sico

Batteries 2026, 12(8), 278; https://doi.org/10.3390/batteries12080278 - 29 Jul 2026

Abstract

In the context of an increasing demand for electricity, batteries increasingly appear as one of the main sources of power supply. In particular, research on batteries is mainly focused on new and high-performance materials, and innovative and more sustainable production processes. This work [...] Read more.

In the context of an increasing demand for electricity, batteries increasingly appear as one of the main sources of power supply. In particular, research on batteries is mainly focused on new and high-performance materials, and innovative and more sustainable production processes. This work addresses both aspects by developing a gravure-printed cathode based on lithium nickel manganese cobalt oxide (NMC 111, LiNi_0.33Mn_0.33Co_0.33O₂). To this end, the formulation of a gravure-printable ink was investigated in order to meet both the printing and functional requirements. The formulation of a multicomponent dispersion able to produce a cathodic layer was particularly challenging, since such a system was found to be highly sensitive to the composition and specific interactions among the active material and the other components involved in ink preparation. Although a methodology based on Ca, aimed at obtaining layers with high macroscopic printing quality, was adopted, only a few printable inks having similar characteristics were obtained. From a microscopic point of view, printing allowed the identification of the best ink formulation able to produce the greatest layer homogeneity, thus yielding the best possible performance (150 mAh g⁻¹ at C/20). Full article

(This article belongs to the Special Issue Synthesis, Modification and Characterization of Advanced Cathode Materials for Li-Ion Batteries and Beyond)

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33 pages, 54619 KB

Open AccessReview

Water-Soluble Binders for Graphite Anode in Lithium-Ion Batteries

by Chaker Ikhlef, Jitendrasingh Rajpurohit, Anil Kumar Madikere Raghunatha Reddy and Karim Zaghib

Batteries 2026, 12(8), 277; https://doi.org/10.3390/batteries12080277 - 28 Jul 2026

Abstract

Lithium-ion batteries (LIBs) are widely used for their high energy density, long cycle life, and broad applications in portable electronics and electric vehicles. However, they still face important challenges related to cost and environmental impact, which has increased the interest in polymeric binders [...] Read more.

Lithium-ion batteries (LIBs) are widely used for their high energy density, long cycle life, and broad applications in portable electronics and electric vehicles. However, they still face important challenges related to cost and environmental impact, which has increased the interest in polymeric binders for composite electrodes. Binders play a key role in maintaining electrode integrity, improving adhesion, controlling slurry properties, and influencing electrochemical performance. This review focuses on binders used in graphite anodes, with a particular emphasis on water-soluble binders such as CMC, SBR, PTFE, alginate, chitin, and some polysaccharide alternatives to conventional binders like PVDF and PNA. Special attention is given to their functions and mechanisms, their main properties (e.g., tensile strength, elasticity and flexibility, adhesion, electrical conductivity, thermal and chemical stability), and their roles in electrode stability. The review also discusses the limitations of conventional binder systems and highlights major challenges. Overall, water-soluble binders appear as promising candidates for developing safer, greener, and high-performance negative electrodes for next-generation lithium-ion batteries. Full article

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18 pages, 3658 KB

Open AccessArticle

Optimizing Parasitic Pumping Power in Proton Exchange Membrane Fuel Cells via Bio-Inspired Cooling Channels Guided by Constructal Theory and Murray’s Law

by Jiale Wang, Qiurui Xin, Wenbo Hao, Chuanyu Sun, Ivan Tolj, Xuan Meng and Jian Mei

Batteries 2026, 12(8), 276; https://doi.org/10.3390/batteries12080276 - 28 Jul 2026

Abstract

Efficient thermal management is critical for proton exchange membrane fuel cells (PEMFCs). This study develops a bio-inspired active liquid cooling architecture to overcome the limitations of conventional flow channels, where convective heat transfer augmentation significantly increases pressure drop. The proposed configuration adheres to [...] Read more.

Efficient thermal management is critical for proton exchange membrane fuel cells (PEMFCs). This study develops a bio-inspired active liquid cooling architecture to overcome the limitations of conventional flow channels, where convective heat transfer augmentation significantly increases pressure drop. The proposed configuration adheres to constructal theory and Murray’s law. A three-dimensional conjugate heat transfer model was formulated to evaluate the thermo-hydrodynamic performance against parallel and serpentine flow channels. Under identical conditions, the proposed configuration exhibits superior thermal uniformity and hydrodynamic behavior, alongside minimized parasitic pumping power. At an inlet Reynolds number (Re) of 400, this configuration stabilizes the average bipolar plate temperature at 349.63 K. It reduces the index of uniform temperature (IUT) to 1.49 K, representing a 52.8% thermal uniformity improvement over the parallel flow channel. Furthermore, at an inlet Re of 600, the overall pressure drop is restricted to 68.44 Pa, reducing the single-plate parasitic pumping power to 1.27 × 10⁻⁴ W, which represents reductions of 93.5% and 12.5% relative to the serpentine and parallel flow channels, respectively. This study provides an alternative architectural scheme for the design of active liquid cooling flow channels in PEMFCs. Full article

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82 pages, 9271 KB

Open AccessReview

A Review of Coupled Degradation Mechanisms in PEMFCs: Thermal Management, Hydrogen Crossover and Mitigation Strategies

by Wenxin Luo, Ankang Feng, Haobin Xie, Yaming Wei, Jixuan Lu, Pugalenthiyar Thondaiman and Qianqian Wang

Batteries 2026, 12(8), 275; https://doi.org/10.3390/batteries12080275 - 28 Jul 2026

Abstract

Proton exchange membrane fuel cells (PEMFCs) offer high efficiency and zero emissions, but durability remains a major barrier to commercialization. Degradation arises from coupled thermal, mechanical, and chemical stresses, yet these processes are often studied in isolation, reducing the accuracy of lifetime prediction. [...] Read more.

Proton exchange membrane fuel cells (PEMFCs) offer high efficiency and zero emissions, but durability remains a major barrier to commercialization. Degradation arises from coupled thermal, mechanical, and chemical stresses, yet these processes are often studied in isolation, reducing the accuracy of lifetime prediction. This paper systematically reviews PEMFC degradation, focusing on the coupling between thermal management failure and hydrogen crossover. Most importantly, it identifies a self-reinforcing thermal–hydrogen feedback in which localized overheating and permeated-hydrogen oxidation mutually amplify membrane defect propagation. Mitigation strategies spanning temperature control, hydrogen crossover suppression, intelligent prediction, and current challenges are then discussed. Mechanistically, thermal mismanagement promotes localized hot spots, while elevated temperature increases hydrogen diffusivity and accelerates defect growth, thereby sharply increasing crossover flux. Permeated hydrogen can be catalytically oxidized near defect sites, releasing additional heat and intensifying local membrane damage. Because the two stressors are coupled rather than independent, single-factor mitigation is insufficient; temperature control and hydrogen crossover suppression must be co-optimized. Future progress requires multiscale frameworks across scales, in situ multiphysics characterization, and artificial intelligence (AI)-driven control, moving from mechanistic understanding to engineering application. Full article

(This article belongs to the Special Issue Next-Generation Proton Exchange Membrane Fuel Cells (PEMFCs))

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55 pages, 14728 KB

Open AccessArticle

Physics-Informed Cross-Domain Deep Learning for Laboratory-to-Field Battery Remaining Useful Life Estimation Under Operational Shifts and Target-Label Scarcity

by Kumbirayi Nyachionjeka, Emad Abd-Elrady and Ehab H. E. Bayoumi

Batteries 2026, 12(8), 274; https://doi.org/10.3390/batteries12080274 - 27 Jul 2026

Abstract

Reliable remaining useful life (RUL) estimation is important for the safe and efficient use of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and energy-storage systems. Most data-driven RUL models are trained under controlled laboratory conditions, but their performance can weaken during field operation, [...] Read more.

Reliable remaining useful life (RUL) estimation is important for the safe and efficient use of lithium-ion (Li-ion) batteries in electric vehicles (EVs) and energy-storage systems. Most data-driven RUL models are trained under controlled laboratory conditions, but their performance can weaken during field operation, where usage, sensing quality, and degradation paths are less predictable. This study proposes a physics-informed laboratory-to-field (L2F) deep learning framework for battery RUL prediction under limited or unavailable target labels. The framework combines three components: a six-channel laboratory cycle representation comprising voltage, current, temperature, cumulative charge throughput, cumulative energy throughput, and voltage derivative; a gated Transformer–Temporal Convolutional Network (TCN) Fusion backbone for modeling long-range and local degradation patterns; and a staged adaptation policy based on paired-view consistency and covariance alignment. The Fusion backbone achieved the lowest held-out XJTU laboratory root mean square error (RMSE) of 46.67, compared with 48.27 for TCN and 48.85 for the Transformer. In the Tsinghua University (Tsinghua) deployment experiment, measured target RUL labels were unavailable after preprocessing and window construction. Therefore, the direct field-side mean absolute error (MAE), RMSE, and coefficient of determination

R^{2}

were not computed. The Tsinghua results are interpreted as an unlabeled deployment-credibility and trajectory-regularity assessment, showing operational continuity, finite vehicle-specific predicted trajectories, and reduced local trajectory volatility after staged adaptation. The S2a + S2b policy reduced Fusion RUL volatility from 8.376 to 0.632. In the XJTU laboratory source representation, Integrated Gradients showed that physics-aware channels contributed 30.98% of the attribution mass, increasing from 19.50% in early-life windows to 31.86% in late-life windows. These attributions explain the laboratory six-channel waveform model and are not used as direct evidence of Tsinghua field-feature importance. Full article

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

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28 pages, 10457 KB

Open AccessArticle

Circulating-Current-Impedance-Based Adaptive Assessment of Inconsistency in Energy Storage Systems

by Bowen Zheng, Zhiwei Xu, Nengwang Xie and Yajie Liu

Batteries 2026, 12(8), 273; https://doi.org/10.3390/batteries12080273 - 27 Jul 2026

Abstract

Cell inconsistency constrains the usable capacity, operational safety, and service life of large-scale battery energy storage systems. Most online methods infer inconsistency from voltage, current, or temperature; in parallel branches, however, self-balancing currents redistribute the operating current and can obscure its external signatures. [...] Read more.

Cell inconsistency constrains the usable capacity, operational safety, and service life of large-scale battery energy storage systems. Most online methods infer inconsistency from voltage, current, or temperature; in parallel branches, however, self-balancing currents redistribute the operating current and can obscure its external signatures. This paper proposes a mode-adaptive assessment method based on circulating-current impedance. Circuit analysis reveals how open-circuit-voltage and resistance differences reverse the circulating-current direction among discharge, idle, and charge. The signed branch response is combined with each cell voltage to form a topology-conditioned cell-level quasi-impedance, which is mapped to a 0–100 consistency score by mode-specific rules. In controlled 6P3S experiments, the predefined resistance-disturbed cells received the two lowest scores in both same-cluster and cross-cluster scenarios; on the same 18-cell records, a baseline-corrected peak-temperature-rise feature did not place either disturbed pair among the two highest-ranked cells. Update times remained at the millisecond level for both 18- and 1200-cell configurations. A field application produced 17.328 million valid cell–window evaluations and persistent cluster-level screening patterns. These results support a physically interpretable and computationally lightweight approach to topology-conditioned cell inconsistency screening. Because independent fault labels were unavailable, the field evidence establishes operational scalability rather than diagnostic accuracy. Full article

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