Batteries

16 pages, 2398 KB

Open AccessArticle

Scalable, Electrically Insulating TPMS Silicone Cold Plates for Passive Battery Thermal Management

by Nicholas Harris, Abel Solomon, Jinhao Cao, Yi Ding and Xianglin Li

Batteries 2026, 12(8), 316; https://doi.org/10.3390/batteries12080316 - 20 Aug 2026

Viewed by 269

This study introduces a novel class of architected foam structures based on triply periodic minimal surface (TPMS) geometries for passive battery thermal management. Unlike conventional cold plates that rely on active pumping or high-conductivity solid materials, the proposed TPMS-like foams leverage convective fluid [...] Read more.

This study introduces a novel class of architected foam structures based on triply periodic minimal surface (TPMS) geometries for passive battery thermal management. Unlike conventional cold plates that rely on active pumping or high-conductivity solid materials, the proposed TPMS-like foams leverage convective fluid transport within a lightweight, electrically insulating polymer matrix. We present the design, fabrication, and experimental characterization of Schwarz Primitive TPMS structures manufactured via injection molding using silicone rubber, which has a comparable quality to additively manufactured polymer cold plates but with significantly lower manufacturing complexity and cost. The TPMS cold plates achieve passive fluid circulation without external pumps, reducing parasitic power consumption while maintaining thermal resistance values of approximately 23.5 K/W. Although its thermal resistance is higher than that of an aluminum plate of the same size (2.7 K/W), the TPMS cold plate is electrically insulating and offers additional safety benefits. Additionally, it can be fabricated from and filled with fire-retardant materials to prevent thermal propagation while maintaining a relatively low temperature gradient. Mechanical compression testing of TPMS foam samples with about 30% solid volume fraction showed a compressive strength of 86.2 kPa at 0.2 strain, equivalent to 30.5% of the compressive modulus of solid silicone (282.6 kPa). Compared to solid silicone plates (thermal resistance is 1420 K/W), the TPMS fluid-filled structures reduce thermal resistance by more than two orders of magnitude. This work establishes design rules, fabrication protocols, and performance benchmarks for TPMS-based passive cooling devices, offering a scalable pathway toward safer, lighter, and more energy-dense battery packs. Full article

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

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22 pages, 4717 KB

Open AccessArticle

Damage Analysis of Prismatic Battery Pack with Polyurea-Coated Carbon Fiber Reinforced Plastic Bottom Plate Due to Ground Impact

by Wenhong Ao, Luyang Wang, Chenghao Ma, Qing Zhou and Yong Xia

Batteries 2026, 12(8), 315; https://doi.org/10.3390/batteries12080315 - 20 Aug 2026

Viewed by 246

Abstract

A polyurea-coated carbon fiber reinforced plastic (CFRP) laminated structure is designed to enhance the impact resistance of lithium-ion batteries against ground impact. This paper presents a numerical simulation to investigate the influence of a polyurea-coated CFRP battery pack bottom plate on mitigating battery [...] Read more.

A polyurea-coated carbon fiber reinforced plastic (CFRP) laminated structure is designed to enhance the impact resistance of lithium-ion batteries against ground impact. This paper presents a numerical simulation to investigate the influence of a polyurea-coated CFRP battery pack bottom plate on mitigating battery damage under ground impact conditions. A novel three-dimensional finite element model of the polyurea-coated CFRP laminate, incorporating a hyper-viscoelastic material model for the polyurea coating and an orthotropic model for the CFRP, is established to analyze the impact response and damage behavior of the laminate. The simulated impact peak force, energy absorption, and maximum crack length of the polyurea-coated CFRP laminate are all within 5% of the experimental results. Based on this validated three-dimensional model, a new battery pack simulation model is developed. The battery module model innovatively adopts a hybrid approach that combines homogenized battery module models and detailed battery module models, enabling accurate simulation of localized cell damage and failure during collisions while significantly improving computational efficiency. The punching process after perforation of the polyurea-coated CFRP laminate, the subsequent crack propagation of the plate, and the local deformation modes of individual cells are clearly predicted by the global model. Battery shortening is recorded as an important indicator of internal short circuits and potential thermal runaway. A parametric study is carried out, and several underlying rules are revealed: the front coating method leads to a greater reduction in battery damage, and the stiffness–toughness interplay between the polyurea coating and the carbon fiber composite is identified as a critical factor governing battery damage. This study provides important insights for the design of protective structures for battery packs against ground impact. Full article

(This article belongs to the Section Electric Vehicles and Mobile Energy Storage Systems)

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26 pages, 3900 KB

Open AccessArticle

Reconciling Manufacturer Claims with Measured Degradation in Commercial Lithium-Ion Cells: A Provenance-Aware Knowledge Graph with Coverage-Gated Abstention

by Alexandru Lecu, Lezan Hawizy and Adrian Groza

Batteries 2026, 12(8), 314; https://doi.org/10.3390/batteries12080314 - 20 Aug 2026

Viewed by 310

Abstract

Manufacturer datasheets state battery cycle life under conditions that rarely match how cells are used, while public cycling datasets measure degradation under conditions datasheets do not cover. We present a knowledge-graph (KG) system that represents claims, measurements, and independent tests of commercial lithium-ion [...] Read more.

Manufacturer datasheets state battery cycle life under conditions that rarely match how cells are used, while public cycling datasets measure degradation under conditions datasheets do not cover. We present a knowledge-graph (KG) system that represents claims, measurements, and independent tests of commercial lithium-ion cells with full provenance, detects claim-versus-measured and claim-versus-claim discrepancies conditioned on the comparability of test conditions, and supports cycle-life prediction with coverage-gated abstention. On the 124-cell Severson dataset under leave-one-policy-group-out cross-validation, graph-derived neighbor features do not significantly improve point prediction over a strong early-cycle baseline (RMSE 135 vs. 141 cycles), but graph coverage provides a statistically significant abstention signal (Spearman

ρ = - 0.25

with prediction error,

p = 0.006

) that reduces retained RMSE by roughly 40% at 60% retention, where random abstention does not. Deployed zero-shot on a second cycling study of the same commercial cell, the gate abstained on all 77 cells; the counterfactual confirms every refusal (approximately 83% error had it answered), an error an ungated baseline commits silently. On a third study with commensurable features, the gate’s first partial acceptance (17 of 45 cells) is itself diagnostic: coverage acts partly as a lifetime proxy out of distribution, and five labeled cells halve retained error while leaving that proxy in place—adaptation repairs the predictor, not the selection criterion. A 70B open-weight LLM extracts datasheet claims at

F_{1} = 0.70

with non-deterministic output even at temperature 0; a deterministic validator with three-run consensus raises this to

F_{1} = 0.78

with zero unsourced values; on a held-out datasheet, precision and the zero-unsourced-value property transfer while recall falls to 0.34, localizing the extractor’s boundary at table-structured content; row-level table grounding, implemented in response, raises held-out recall to 0.63 with zero hallucinations at a measured precision cost. Reconciling claims across document variants shows that roughly one in three cross-document specification comparisons (14 of 43, three commercial cells) yields a conflict or condition mismatch, twelve involving third-party documents and two internal to a single manufacturer’s own documents. A hand-labeled, condition-annotated gold standard of 103 claims (62 development, 41 held-out; inter-annotator

κ = 0.74

on property naming) and a staged, human-gated literature-monitoring pipeline are released with the code. Full article

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

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21 pages, 7170 KB

Open AccessArticle

Electrode-Level Diagnosis of Lithium-Ion Battery Path-Dependent Degradation Using a Half-Cell Model

by Ben Wang, Yu Gao and Yu Zhang

Batteries 2026, 12(8), 313; https://doi.org/10.3390/batteries12080313 - 19 Aug 2026

Viewed by 319

Abstract

With the widespread application of lithium-ion batteries in electric vehicles, degradation diagnosis has attracted increasing attention. In practical operating scenarios, however, path-dependent degradation induced by the alternating effects of calendar aging and cycling aging can significantly influence the diagnosis of battery degradation. Existing [...] Read more.

With the widespread application of lithium-ion batteries in electric vehicles, degradation diagnosis has attracted increasing attention. In practical operating scenarios, however, path-dependent degradation induced by the alternating effects of calendar aging and cycling aging can significantly influence the diagnosis of battery degradation. Existing methods often identify degradation under these two aging conditions in isolation, making it difficult to quantify their coupled impact. To address this issue, this study applies a physically constrained half-cell OCP reconstruction framework to quantify electrode-level degradation under coupled aging conditions. Specifically, degradation parameters are identified by fitting full-cell pseudo-open-circuit voltage (pOCV) curves with half-cell open-circuit potential (OCP) profiles, and the corresponding degradation modes are further quantified. The results show that the proposed model can reconstruct full-cell pOCV under different aging conditions with an RMSE maintained below 10.5 mV. The loss of active material in the anode is highly sensitive to continuous cycling, whereas the divergence of loss of lithium inventory under alternating aging conditions is relatively weak but shows pronounced differences under distinct single-aging conditions. This method enables electrode-level diagnosis of path-dependent degradation under alternating aging conditions and provides a foundation for reliable degradation diagnosis under complex operating scenarios. Full article

(This article belongs to the Section Lithium-Ion and Solid-State Batteries)

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27 pages, 2973 KB

Open AccessArticle

Uncertainty-Aware State of Energy Estimation for Lithium-Ion Batteries via Hybrid Kernel Sparse Gaussian Process

by Chaoyu Xiao, Haotian Shi, Lei Chen, Zhijun Cai, Yuanru Zou and Chunmei Yu

Batteries 2026, 12(8), 312; https://doi.org/10.3390/batteries12080312 - 19 Aug 2026

Viewed by 199

Abstract

This work develops a hybrid kernel sparse Gaussian process regression integrated with kernel density estimation (HCSGPR-UQ) to resolve three critical drawbacks of conventional lithium-ion battery State of Energy (SOE) estimators: degraded accuracy under dynamic loads, high computational overhead, and inadequate uncertainty quantification. A [...] Read more.

This work develops a hybrid kernel sparse Gaussian process regression integrated with kernel density estimation (HCSGPR-UQ) to resolve three critical drawbacks of conventional lithium-ion battery State of Energy (SOE) estimators: degraded accuracy under dynamic loads, high computational overhead, and inadequate uncertainty quantification. A composite covariance kernel is built by weighting the radial basis function (RBF) and Matérn 5/2 kernels to simultaneously model global smooth SOE decay trends and local nonlinear fluctuations induced by abrupt current/temperature variations. Inducing-point sparse approximation is adopted to accelerate model inference, while kernel density estimation (KDE) generates nonparametric prediction bounds for quantitative uncertainty evaluation. Validations are carried out on a 75 Ah traction lithium-ion cell across −5 °C to 35 °C under Dynamic Stress Test (DST) and Beijing Bus Dynamic Stress Test (BBDST) cycles. Experimental results reveal that the proposed method yields mean absolute errors (MAEs) of only 0.32% (DST) and 0.38% (BBDST), runs roughly 15× faster than full Gaussian process regression (GPR), and attains a 94.7% coverage probability for nominal 95% prediction intervals. Balancing estimation precision, real-time inference speed and statistical reliability, the proposed framework delivers a viable online SOE estimation solution for vehicle battery management systems (BMSs). Full article

(This article belongs to the Special Issue Second-Life Batteries: Challenges and Opportunities)

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32 pages, 5217 KB

Open AccessReview

Research Progress on Application of Supercapacitors in Grid Frequency Regulation

by Fengyun Quan, Zilong Li, Yunfei Zhang, Bin Ye, Tong Zhang, Yong Zheng, Ling Li and Xiaoxia Sun

Batteries 2026, 12(8), 311; https://doi.org/10.3390/batteries12080311 - 18 Aug 2026

Viewed by 356

Abstract

With the rapid transition of the global energy structure, large-scale clean energy integration has become a major trend in power system development. Nevertheless, the intermittency and stochastic fluctuation of renewable power generation threaten the secure operation of power systems. With high power density [...] Read more.

With the rapid transition of the global energy structure, large-scale clean energy integration has become a major trend in power system development. Nevertheless, the intermittency and stochastic fluctuation of renewable power generation threaten the secure operation of power systems. With high power density and millisecond-level response capability, supercapacitors act as key technical support for frequency stabilization and grid frequency fluctuation suppression. This paper reviews research advances in the application of supercapacitors to power system frequency regulation. It presents the classification and energy storage mechanisms of supercapacitors, analyzes their technical advantages in frequency regulation, and summarizes key research progress involving control strategies, topologies and capacity optimization schemes. Three typical application scenarios are illustrated: standalone frequency regulation, coordinated thermal-storage frequency regulation, and auxiliary frequency regulation for renewable power plants. Considering future requirements for frequency regulation, potential research directions are put forward to provide references for follow-up related studies. Full article

(This article belongs to the Special Issue Advances in Supercapacitors: Materials, Devices, Models, Systems, and Applications: 2nd Edition)

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14 pages, 8029 KB

Open AccessArticle

Chemical Oxidation Synergistically Regulates Surface Chemistry and Pore Structure of Cotton Stalk-Based Hard Carbon for Enhanced Sodium Storage Performance

by Yuanzhe Wang, Hong Cui, Liang Liu, Jianyuming Zhang, Yue Tang and Jiantie Xu

Batteries 2026, 12(8), 310; https://doi.org/10.3390/batteries12080310 - 18 Aug 2026

Viewed by 326

Abstract

Biomass-derived hard carbon (HC) represents a promising anode candidate for sodium-ion batteries, owing to its disordered structure and abundant micropores. This study systematically investigates three chemical oxidation strategies (NaClO, H ₂ SO ₄ and H ₂ O ₂ +NaOH) applied to cotton stalk-derived [...] Read more.

Biomass-derived hard carbon (HC) represents a promising anode candidate for sodium-ion batteries, owing to its disordered structure and abundant micropores. This study systematically investigates three chemical oxidation strategies (NaClO, H ₂ SO ₄ and H ₂ O ₂ +NaOH) applied to cotton stalk-derived HC carbonized at 1300 °C. The NaClO-treated sample delivers the optimal overall electrochemical performance, achieving a high discharge capacity of 330.2 mAh g ⁻¹ at 0.1 C, a high initial Coulombic efficiency (ICE) of 81.7%, and a capacity retention of 81.9% after 1000 cycles at 2 C (from 226.0 to 185.1 mAh g ⁻¹ ). This superiority is attributed to the formation of a three-dimensional hierarchical pore network and optimal oxygen functional groups. The H ₂ SO ₄ treatment yields a discharge capacity of 323.2 h g ⁻¹ , an ICE of 75.3%, and a capacity retention of 77.1% after 1000 cycles (from 183.5 to 141.5 mAh g ⁻¹ ), benefiting from structural densification. The H ₂ O ₂ +NaOH treatment delivers a capacity of 269.8 mAh g ⁻¹ and an ICE of 74.2%, exhibiting a distinct activation behavior likely due to its thin pore walls and abundant open mesopores. Overall, all treated samples significantly outperformed the pristine HC, which exhibits a discharge capacity of 320.3 mAh g ⁻¹ , an ICE of 68.0%, and a retained capacity of 90.4 mAh g ⁻¹ after 1000 cycles at 2 C. Full article

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27 pages, 1208 KB

Open AccessArticle

Lithium-Ion Battery Temperature Estimation Based on Electrochemical Impedance Spectroscopy

by Timur Issayenko, Frank Opferkuch and Stephan Rinderknecht

Batteries 2026, 12(8), 309; https://doi.org/10.3390/batteries12080309 - 16 Aug 2026

Viewed by 471

Abstract

The electrification of commercial vehicles demands precise battery thermal management, but direct measurement of the cell core temperature is challenging. This paper presents an electrochemical impedance spectroscopy (EIS)-based approach for rapid indirect estimation of the mean internal temperature in 2170 NMC lithium-ion cells. [...] Read more.

The electrification of commercial vehicles demands precise battery thermal management, but direct measurement of the cell core temperature is challenging. This paper presents an electrochemical impedance spectroscopy (EIS)-based approach for rapid indirect estimation of the mean internal temperature in 2170 NMC lithium-ion cells. Three measurement approaches and various fitting methods, including Steinhart–Hart, least-squares polynomials, and nonlinear Arrhenius-based fits, are compared using experimental data. The results indicate that estimation accuracy is more strongly influenced by the selection of measurement frequency than by the choice of fitting approach. The optimal method combines a single optimized frequency with a nonlinear polynomial incorporating an Arrhenius term, achieving a maximum deviation of

0.23 K

and a mean deviation of

0.14 K

. This framework enables indirect EIS-based estimation of the cell core temperature and can be further refined through measurements on multiple cells or by combining different fitting methods. Future work should extend the proposed approach to dynamic EIS measurements, battery ageing, and integration with thermal models for early overheating warning and identification of the first thermal runaway warning stage in high-power commercial vehicles. Full article

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

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23 pages, 3937 KB

Open AccessArticle

DMS-SVDD: Dynamic Multiscale State-Space Support Vector Data Description for Lithium-Ion Battery Fault Detection from Electric Vehicle Charging Segments

by Chenjie Du, Zhoutao Hu, Junhao Hu and Silu Chen

Batteries 2026, 12(8), 308; https://doi.org/10.3390/batteries12080308 - 16 Aug 2026

Viewed by 263

Abstract

Fault detection from electric vehicle charging segments is challenging because real-world records are noisy, verified fault labels are scarce, and weak signatures may evolve gradually across time and unevenly across a vehicle’s charging history. This study proposes an unsupervised dynamic multiscale state-space support [...] Read more.

Fault detection from electric vehicle charging segments is challenging because real-world records are noisy, verified fault labels are scarce, and weak signatures may evolve gradually across time and unevenly across a vehicle’s charging history. This study proposes an unsupervised dynamic multiscale state-space support vector data description framework for vehicle-level battery fault detection. A gated diagonal state-space encoder preserves long-range charging patterns while retaining a direct input path for transient changes. A progressive cross-scale fusion module then combines short-term fluctuations with accumulated deviations in the learned hidden representation. Finally, a two-stage hypersphere optimisation strategy first estimates the normal centre and then refines the boundary around that fixed centre. This coordinated design avoids sequence reconstruction and directly scores charging segments by their distance from normal behaviour before robust vehicle-level aggregation. On the two EVBattery subsets that permit statistically reliable evaluation, the proposed framework achieved vehicle-level areas under the receiver operating characteristic curves of 0.8849 and 0.8438. These results exceed those of the best-performing baseline on the corresponding subsets by 0.0889 and 0.0417, respectively. The results show that coordinating long-range encoding, cross-scale fusion, and staged boundary learning improves threshold-independent vehicle-level fault ranking in real charging data. Full article

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

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10 pages, 8495 KB

Open AccessArticle

Lipoic Acid-Derived Interphase for Stable Lithium Metal Anodes in High-Performance Lithium Metal Batteries

by Liyuan Zhang, Chen Liang, Jiarong Xu, Chuanhui Gong and Wei Chen

Batteries 2026, 12(8), 307; https://doi.org/10.3390/batteries12080307 - 14 Aug 2026

Viewed by 251

Abstract

Lithium metal batteries are widely regarded as one of the most promising candidates for achieving energy densities beyond 500 Wh kg⁻¹. However, their practical commercialization is severely hindered by the high reactivity of lithium metal, which leads to pronounced interfacial instability. [...] Read more.

Lithium metal batteries are widely regarded as one of the most promising candidates for achieving energy densities beyond 500 Wh kg⁻¹. However, their practical commercialization is severely hindered by the high reactivity of lithium metal, which leads to pronounced interfacial instability. Constructing an artificial solid electrolyte interphase (SEI) via surface pretreatment has been demonstrated to be an effective strategy for suppressing dendrite growth and mitigating parasitic side reactions. Herein, we take advantage of the rapid reaction between lipoic acid (LA) and lithium metal to pre-form a uniform artificial SEI on the anode surface. This interphase is composed of organic COO-Li species and sulfur-containing compounds. Electrochemically, the LA-modified lithium anode exhibits remarkable stability, sustaining more than 1500 h of cycling in symmetric cells at 5 mA cm⁻² and 5 mAh cm⁻². Furthermore, full-cell configurations, including lithium-sulfur and lithium-LiFePO₄ pouch cells, deliver significantly improved cycling performance compared with those employing bare lithium anodes. These results establish a practical and scalable route for fabricating stable artificial SEI layers on lithium metal, thereby providing a feasible pathway toward the realization of high-energy-density lithium metal batteries. Full article

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23 pages, 2945 KB

Open AccessPerspective

Buried Interfaces as Functional Architectures in Rechargeable Batteries: A FIB-Enabled Perspective

by Jiaqi Jia, Ke Deng, Yong Li, Yuchen Li, Zhao Ding and Maziar Ashuri

Batteries 2026, 12(8), 306; https://doi.org/10.3390/batteries12080306 - 13 Aug 2026

Viewed by 316

Abstract

Buried interfaces and interphases often govern performance loss in rechargeable batteries, although their functions are frequently inferred from spatially averaged composition, surface-sensitive measurements, or cell-level electrochemical response. In this Perspective, an interface denotes the geometrical boundary between adjacent phases, whereas an interphase denotes [...] Read more.

Buried interfaces and interphases often govern performance loss in rechargeable batteries, although their functions are frequently inferred from spatially averaged composition, surface-sensitive measurements, or cell-level electrochemical response. In this Perspective, an interface denotes the geometrical boundary between adjacent phases, whereas an interphase denotes a finite-thickness region whose composition or structure differs from those of the adjoining bulk phases. Rather than organizing the discussion by focused ion beam (FIB) modality or battery chemistry alone, we adopt an architecture-first, evidence-bounded framework and compare three classes of buried-interface architecture: engineered particle coatings; electrochemically generated solid electrolyte interphase (SEI) and cathode–electrolyte interphase (CEI) regions together with lithium-metal deposits; and solid–solid contacts in all-solid-state batteries. For each class, the formation route and required function are related to spatial descriptors, including thickness distribution, lateral continuity, pore or gap topology, chemical gradients, contact area, and contact retention. FIB-enabled cross-sectioning, tomography, and correlative spectroscopy can register morphology, chemistry, and contact geometry within a common spatial frame, but they do not directly measure ionic conductivity, electronic leakage, adhesion energy, or local reaction rate. Such functional attribution therefore requires complementary electrochemistry, spectroscopy, modeling, temporal observation, and representative sampling. Across the three classes, durable interfacial function depends on chemically selective transport pathways that remain spatially continuous and mechanically viable during processing, cycling, and storage. Full article

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

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41 pages, 3747 KB

Open AccessReview

From Flame Extinguishment to Reignition Control: Fire Suppressants, Sustained Cooling Mechanisms, and Fire-Safety Challenges in Lithium-Ion Battery Fires

by Qiqi Yang, Qingwen Lin, Ruichao Wei, Jiaxin Gao, Yihe Zhang and Shenshi Huang

Batteries 2026, 12(8), 305; https://doi.org/10.3390/batteries12080305 - 13 Aug 2026

Viewed by 404

Abstract

Lithium-ion battery fires are governed by continuous heat release during thermal runaway, flammable gas venting, and thermal coupling between adjacent cells. Even after visible flames are extinguished, post-extinguishment temperature rise, thermal runaway propagation, and reignition may still occur. This review establishes a full-process [...] Read more.

Lithium-ion battery fires are governed by continuous heat release during thermal runaway, flammable gas venting, and thermal coupling between adjacent cells. Even after visible flames are extinguished, post-extinguishment temperature rise, thermal runaway propagation, and reignition may still occur. This review establishes a full-process control framework linking flame suppression, sustained cooling, thermal runaway propagation mitigation, and reignition control. Within this framework, water-based agents, clean gaseous agents, dry powder agents, foams, cryogenic media, and hybrid suppression methods are not only compared by their flame extinguishment performance but also by their cooling capability, thermal runaway propagation/reignition control, scenario applicability, and environmental impacts. Evaluation metrics and standardization requirements are further integrated to support cross-study comparison and practical suppressant selection. Existing studies indicate that a single suppressant is generally unable to achieve both rapid flame extinguishment and post-extinguishment thermal stability. Multi-mechanism synergy, realistic scenario validation, and standardized evaluation protocols are therefore essential for improving the full-process control of lithium-ion battery fires. 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, 1717 KB

Open AccessArticle

Numerical Investigation of a Compact Air-Cooled EV Battery Thermal Management System Using Circumferential Fins

by Ahmed Saeed, Ali Alawi, Mohammad Al Janaideh, Ahmed M. R. Elbaz and Mostafa H. Sharqawy

Batteries 2026, 12(8), 304; https://doi.org/10.3390/batteries12080304 - 13 Aug 2026

Viewed by 326

Abstract

Battery thermal management systems (BTMSs) are essential for maintaining the performance, efficiency, durability, and safety of electric-vehicle battery packs. Although fin-enhanced air-cooled BTMSs offer a simple and leakage-free cooling solution, their practical implementation is often limited by increased weight, insufficient temperature uniformity, and [...] Read more.

Battery thermal management systems (BTMSs) are essential for maintaining the performance, efficiency, durability, and safety of electric-vehicle battery packs. Although fin-enhanced air-cooled BTMSs offer a simple and leakage-free cooling solution, their practical implementation is often limited by increased weight, insufficient temperature uniformity, and restricted heat-dissipation capability under high thermal loads. This study numerically investigates a compact air-cooled BTMS for two types of cylindrical lithium-ion batteries using aluminum and polypropylene (PP-

β

) circumferential fins in inline and staggered cell arrangements. Unlike previous fin-based air-cooling investigations, the present study combines a compact 2 × 4 battery pack with transverse and longitudinal center-to-center cell pitches of 1.2D, a direct comparison between metallic and lightweight polymer fins, and an assessment of two 18650 battery types with different capacities, thermophysical properties, and heat-generation characteristics. A three-dimensional steady-state conjugate heat-transfer model was developed in ANSYS Fluent to evaluate the effects of fin number, fin material, cell arrangement, ambient temperature, and inlet airflow velocity under discharge rates ranging from 1 C to 4 C. The results reveal that increasing the number of fins consistently reduced the maximum cell temperature but increased the pressure drop. The inline configuration generally achieved a lower maximum temperature and higher Nusselt number (Nu), whereas the staggered arrangement maintained a substantially lower pressure drop. Relative to the corresponding finless configurations, the Nu increased by 64.4–71.2% for the inline arrangement and 86.4–98.1% for the staggered arrangement. Polypropylene fins provided thermal performance close to that of aluminum fins in terms of maximum temperature while reducing the total fin mass by approximately 44.8%; however, aluminum fins maintained better temperature uniformity. These findings quantify the trade-offs among thermal performance, pressure drop, compact cell spacing, and system weight, providing design guidance for compact fin-enhanced air-cooled BTMSs. Full article

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46 pages, 17356 KB

Open AccessReview

Sodium-Ion Batteries: Linking Liquid and Solid-State Electrolytes, Electrode Compatibility, and Commercial Viability

by Maria Luís Pinto, Beatriz Moura Gomes and Maria Helena Braga

Batteries 2026, 12(8), 303; https://doi.org/10.3390/batteries12080303 - 13 Aug 2026

Viewed by 640

Abstract

Sodium-ion batteries are emerging as credible complements to lithium-ion technology for sustainable, safe, and cost-effective energy storage. This critical review links molecular-scale electrolyte solvation and interphase chemistry to electrode compatibility, full-cell engineering, manufacturing constraints, and commercial viability. Organic liquid, aqueous, ionic-liquid, concentrated, inorganic [...] Read more.

Sodium-ion batteries are emerging as credible complements to lithium-ion technology for sustainable, safe, and cost-effective energy storage. This critical review links molecular-scale electrolyte solvation and interphase chemistry to electrode compatibility, full-cell engineering, manufacturing constraints, and commercial viability. Organic liquid, aqueous, ionic-liquid, concentrated, inorganic solid, polymer, and composite electrolytes are compared using transport, stability, processing, and interface criteria. The principal cathode and anode families are then evaluated in terms of practical voltage, reversible capacity, cycling stability, raw-material exposure, manufacturability, and end-of-life implications. A distinctive contribution of this work is the explicit separation of thermodynamic predictions, laboratory measurements, prototype demonstrations, and company-reported targets, together with design rules that connect electrolyte chemistry to cell-level performance. Sodium-ion batteries are unlikely to replace lithium-ion batteries universally, but they can occupy a strategic role where cost, safety, abundance, supply-chain resilience, and circularity outweigh maximum energy density. Full article

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

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27 pages, 13533 KB

Open AccessReview

Characterization of Solid Electrolyte Interphases on Carbon-Based Negative Electrodes for Lithium-Ion Batteries: Methods, Artifacts, and Correlative Workflows

by Soon-Ki Jeong

Batteries 2026, 12(8), 302; https://doi.org/10.3390/batteries12080302 - 13 Aug 2026

Viewed by 330

Abstract

Solid electrolyte interphase (SEI) characterization is needed to interpret the performance, degradation, and lifetime of graphite and Si-containing carbon-based negative electrodes in lithium-ion batteries. However, SEI claims are often difficult to compare because measured signals, inferred assignments, sample history, and electrode architecture are [...] Read more.

Solid electrolyte interphase (SEI) characterization is needed to interpret the performance, degradation, and lifetime of graphite and Si-containing carbon-based negative electrodes in lithium-ion batteries. However, SEI claims are often difficult to compare because measured signals, inferred assignments, sample history, and electrode architecture are not always clearly separated. This review presents a claim-bounded framework for SEI characterization that distinguishes direct observables from inferred chemical, molecular, structural, morphological, and functional information. Photoelectron spectroscopy methods provide chemical-state and relative-depth-sensitivity constraints; secondary-ion mass spectrometry methods provide fragment and isotope distributions; vibrational spectroscopies support functional-group and local vibrational evidence; nuclear magnetic resonance and molecular mass spectrometry provide molecular or product-level constraints; and microscopy, tomography, and atomic force microscopy provide morphology, architecture, local thickness, topography, and mechanical response. Across these methods, rinsing, drying, sputtering, beam exposure, extraction, and limited sampling can alter the observable and therefore the defensible claim. The review emphasizes the distinction between native electrode-associated SEI features and extracted, soluble, or electrolyte-phase products, and between morphology-only evidence and chemically assigned morphology. It concludes by proposing claim-driven correlative workflows and reporting guidance for reproducible interpretation on graphite, Si/graphite, Si/C, and carbon-coated Si architectures where directly studied or present. 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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20 pages, 9085 KB

Open AccessArticle

Life Prediction of Energy Storage LFP Batteries Based on Voltage Segment Health Indicators: A Comparative Study of Data-Driven and Arrhenius-Data Fusion Models

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

Batteries 2026, 12(8), 301; https://doi.org/10.3390/batteries12080301 - 12 Aug 2026

Viewed by 313

Abstract

Large-capacity lithium iron phosphate (LFP) batteries dominate energy storage systems, but their degradation characteristics differ from small-capacity cells. Most existing life prediction methods require complete voltage–current time-series data, which is hard to obtain in practical operation. This paper proposes two life prediction methods: [...] Read more.

Large-capacity lithium iron phosphate (LFP) batteries dominate energy storage systems, but their degradation characteristics differ from small-capacity cells. Most existing life prediction methods require complete voltage–current time-series data, which is hard to obtain in practical operation. This paper proposes two life prediction methods: a data-driven method and an empirical-data hybrid method. The data-driven method adopts Summed Voltage Falloff (SVF) extracted from partial voltage segments as the health indicator, which removes the dependence on full charge–discharge waveform data and enhances engineering practicability. It uses a unified numerical fitting framework with Gaussian process regression (GPR) residual correction, with tailored fitting strategies for 320 Ah and 298 Ah battery datasets. The hybrid method integrates the Arrhenius model with Kalman filtering for closed-loop online prediction correction. Both methods are validated using 281 cycles of 320 Ah battery data, and the data-driven method is further verified with 150 cycles of 298 Ah battery data. Results show that the data-driven method performs better with limited data, suitable for offline one-time inspection scenarios; the hybrid model achieves higher accuracy with sufficient data, applicable to long-term online remaining useful life monitoring. The data-driven method yields a worst-case cycle life of 3304 cycles at 2σ confidence level, and the hybrid model maintains error below 5% when forecasting 1000 cycles with 200 cycles of training data. Full article

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

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42 pages, 17332 KB

Open AccessReview

Hybrid Energy Storage Systems: A Review of Topology Classification, Energy Management Strategies, Applications and Future Challenges

by Ahmet Yimenicioğlu and Yunus Yalman

Batteries 2026, 12(8), 300; https://doi.org/10.3390/batteries12080300 - 11 Aug 2026

Viewed by 537

Abstract

Energy storage systems (ESSs) play a crucial role in mitigating the intermittency and variability of renewable energy sources (RESs) and enhancing the stability and reliability of modern power systems. However, the inherent limitations of individual storage technologies, particularly the trade-off between energy density [...] Read more.

Energy storage systems (ESSs) play a crucial role in mitigating the intermittency and variability of renewable energy sources (RESs) and enhancing the stability and reliability of modern power systems. However, the inherent limitations of individual storage technologies, particularly the trade-off between energy density and power density, restrict their ability to satisfy diverse operational requirements. In this context, hybrid energy storage systems (HESSs), which combine complementary storage technologies, such as batteries, supercapacitors, and flywheels, have emerged as an effective solution capable of simultaneously delivering high-energy and high-power performance. This paper presents a comprehensive review of HESS architectures, converter topologies, energy management strategies (EMSs), and applications. The EMS taxonomy is organized into classical and intelligent control. Classical EMS approaches are categorized into filtration-based, rule-based, deadbeat, droop, sliding mode, and fuzzy logic control, whereas intelligent EMS approaches encompass optimization-based methods, including model predictive control, as well as learning-based techniques such as supervised and reinforcement learning. Moreover, HESS applications are examined across grid-scale systems, microgrids, renewable energy systems, transportation, power quality improvement, frequency regulation, peak shaving, and uninterruptible power supply systems. Representative implementations are also reviewed to identify current technological trends, operational challenges, and performance trade-offs. Finally, future research directions are outlined, with emphasis on digital twins, privacy-preserving and explainable learning frameworks, cyber–physical security, and adaptive and scalable EMSs. Full article

(This article belongs to the Special Issue Innovations in Battery Management: Advanced Modeling, Control and Diagnostic Algorithms for Batteries and Hybrid Energy Storage Systems)

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14 pages, 5812 KB

Open AccessArticle

Impact of Using Flame-Retardant Electrolyte Additives in Li-Ion Batteries: A Comprehensive Evaluation of Ethoxy (Pentafluoro) Cyclotriphosphazene (PFPN)

by Afaque Alam, Samarpan Farmer, Mohammad Behzadnia, Xuefeng Jiao, Brad VanDerWege, Andrew Getsoian, Claudia Iyer, Benjamin Petersen, James Yi, Likun Zhu and Li Qiao

Batteries 2026, 12(8), 299; https://doi.org/10.3390/batteries12080299 - 11 Aug 2026

Viewed by 433

Abstract

Li-ion batteries (LIBs) are seeing increasingly widespread adoption across consumer electronics, electric vehicles, and grid-scale energy storage systems, yet their susceptibility to thermal runaway remains a concern. This study evaluates ethoxy (pentafluoro) cyclotriphosphazene (PFPN) as an electrolyte additive to reduce electrolyte flammability and [...] Read more.

Li-ion batteries (LIBs) are seeing increasingly widespread adoption across consumer electronics, electric vehicles, and grid-scale energy storage systems, yet their susceptibility to thermal runaway remains a concern. This study evaluates ethoxy (pentafluoro) cyclotriphosphazene (PFPN) as an electrolyte additive to reduce electrolyte flammability and thermal stability without significantly compromising electrochemical performance. Electrolyte flammability was quantified using self-extinguishing time (SET) measurements, which revealed that PFPN significantly suppresses combustion. At 4 wt% PFPN, 60% of electrolyte samples failed to ignite despite extended ignition exposure, and the average SET decreased from 51.15 s g⁻¹ to 34.60 s g⁻¹. Differential scanning calorimetry (DSC) further demonstrated improved thermal stability, with the onset of solvent decomposition delayed by ~30 °C at 4 wt% PFPN. Ionic conductivity modestly decreases (14%, from 8.13 to 6.97 mS cm⁻¹ at 4 wt% PFPN). Electrochemical testing showed negligible impact on battery performance. Graphite||Li and NMC811||Li half-cells containing PFPN exhibited comparable capacity retention to baseline cells. NMC811||graphite pouch cells were used to further evaluate extended cycling and rate capability; PFPN-containing cells demonstrated similar capacities even after prolonged cycling and high-rate operation. Overall, PFPN provides effective flame retardance at 4 wt% while maintaining electrochemical compatibility, making it a promising additive for enhancing thermal stability of LIB electrolytes. Full article

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

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11 pages, 3131 KB

Open AccessArticle

Promoting Polysulfide Conversion via a Lithium-Ion Reservoir Based on La-Doped MoO₃ Nanorods for Li-S Batteries

by Guoping Xiang, Jing Liu, Jinshan Ai, Tong Liu, Ziheng Wang, Hao Zhang and Peng Zeng

Batteries 2026, 12(8), 298; https://doi.org/10.3390/batteries12080298 - 11 Aug 2026

Viewed by 295

Abstract

The practical application of Li-S batteries is seriously plagued by the severe shuttle effect and sluggish conversion kinetics of polysulfides. To circumvent these obstacles, we herein construct La-doped MoO₃ nanorods (La-MoO₃) as a functional lithium-ion reservoir for sulfur hosts. By [...] Read more.

The practical application of Li-S batteries is seriously plagued by the severe shuttle effect and sluggish conversion kinetics of polysulfides. To circumvent these obstacles, we herein construct La-doped MoO₃ nanorods (La-MoO₃) as a functional lithium-ion reservoir for sulfur hosts. By virtue of its enhanced lithium intercalation kinetics, the La-MoO₃ actively accumulates Li⁺ ions during electrochemical cycling, which may promote the chemical conversion of soluble long-chain polysulfides to short-chain species. This behavior helps restrain the shuttle effect and expedite the sulfur redox process via a probable lithium-reservoir-related catalytic effect. The La-MoO₃/S cathode achieves a high reversible capacity of 1376 mAh g⁻¹ at 0.1 C, along with excellent long-term cyclability featuring a low decay of 0.08% per cycle over 160 cycles at 0.5 C. Even at a high rate of 1 C, it retains remarkable durability with an ultralow fading rate of 0.068% per cycle over 450 cycles. This work demonstrates the potential of La-doping to build an efficient lithium-ion reservoir and provides insight into the correlation between lithium-ion storage and accelerated polysulfide conversion, which may guide the development of high-performance Li-S cathodes. Full article

(This article belongs to the Special Issue Interfacial Engineering Strategy for High-Performance Lithium-Sulfur Batteries)

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23 pages, 2212 KB

Open AccessArticle

Recycling Strategies for New Energy Vehicle Power Batteries with Consideration of Pricing Mechanism

by Yanyan Kong, Jianling Chen and Honglin Zhang

Batteries 2026, 12(8), 297; https://doi.org/10.3390/batteries12080297 - 10 Aug 2026

Viewed by 241

Abstract

There is a large and rapidly growing stock of retired power batteries from new energy vehicles in China. Unregulated informal recycling and improper disposal of these waste batteries trigger serious environmental hazards. Though a batch of regulatory policies on battery recycling have been [...] Read more.

There is a large and rapidly growing stock of retired power batteries from new energy vehicles in China. Unregulated informal recycling and improper disposal of these waste batteries trigger serious environmental hazards. Though a batch of regulatory policies on battery recycling have been released in recent years, the power battery recycling sector still faces prominent governance bottlenecks, especially ambiguous responsibility division and poor implementability under the entrusted recycling mode. To fill the existing research gap regarding tripartite interest conflicts and pricing mechanisms in entrusted recycling, this paper constructs a three-party evolutionary game model covering power battery producers, recyclers and government regulators. Two pricing models are further developed to distinguish producer self-operated recycling and third-party entrusted recycling channels. Numerical simulation is adopted to investigate multi-stakeholder interest contradictions, dynamic evolutionary trajectories and equilibrium stability of the recycling system, and the influences of subsidy intensity, supervision intensity and recycling cost on participants’ strategic choices are quantitatively analyzed. The research results demonstrate that inadequate government supervision and insufficient economic returns for formal recyclers serve as the primary obstacles hindering the effective deployment of entrusted recycling. An inherent and reasonable price gap exists between self-operated and entrusted recycling modes. Essentially, the price differential of standardized entrusted recycling represents the profit margin conceded by producers to recyclers instead of direct financial subsidies. To solve existing industry problems, this study proposes targeted recommendations for tripartite collaboration. The government should refine the regulatory framework of Extended Producer Responsibility and adopt differentiated reward and penalty mechanisms. Producers are expected to standardize entrusted recycling management and formulate a scientific pricing range for retired batteries. Recyclers ought to advance recycling technologies and maintain standardized operations. Collective efforts from all stakeholders can facilitate the long-term sustainability of the closed-loop recycling system for retired power batteries. Full article

(This article belongs to the Special Issue Second-Life Batteries: Challenges and Opportunities)

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23 pages, 5879 KB

Open AccessArticle

BATWO: Bayesian Adaptive Time Window Optimization for Feature Extraction in SOH Estimation of Li-Ion Batteries Under Dynamic Operating Conditions

by Sijia Yang, Jingjing Zhang, Jichao Hong, Zhaolin Yuan, Lifan Wang, Shanshan Guo and Shihan Ge

Batteries 2026, 12(8), 296; https://doi.org/10.3390/batteries12080296 - 8 Aug 2026

Viewed by 266

Abstract

Accurate state of health (SOH) estimation of lithium-ion batteries is critical to the reliability of electric vehicles. However, under dynamic operating conditions, conventional feature extraction based on fixed time window often exhibits poor generalization, as it fails to account for the multi-timescale parameter [...] Read more.

Accurate state of health (SOH) estimation of lithium-ion batteries is critical to the reliability of electric vehicles. However, under dynamic operating conditions, conventional feature extraction based on fixed time window often exhibits poor generalization, as it fails to account for the multi-timescale parameter couplings inherent in the non-stationary voltage responses. To address this issue, this paper proposes a Bayesian Adaptive Time Window Optimization (BATWO) framework for feature extraction in battery SOH estimation. Within this framework, the time window length is treated as a learnable structural parameter and is adaptively optimized via Bayesian optimization to identify the most informative observation timescale for extracting degradation-sensitive statistical features under given operating conditions. Evaluations on a cycle-aging dataset containing 69 lithium-ion battery samples subjected to distinct dynamic operating profiles show that the optimal time window lengths vary significantly, ranging from 500 s to 27,630 s. The BATWO framework achieves an average root-mean-square error (RMSE) of 2.07% and a mean absolute error (MAE) of 1.45%, outperforming the best fixed time window strategy by reducing the RMSE and MAE by 2.35% and 2.68%, respectively. Moreover, compared with LSTM- and Transformer-based models without feature extraction, the BATWO framework reduces training time by over 97%. These results highlight the superior generalization capability and computational efficiency of the BATWO framework, demonstrating its great potential for practical deployment in battery management system. Full article

(This article belongs to the Special Issue Advanced Intelligent Management Technologies of New Energy Batteries)

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23 pages, 6534 KB

Open AccessArticle

State of Health Estimation of Large-Capacity Energy Storage Batteries Based on Mechanical–Electrical–Thermal Multi-Modal Features

by Rong He, Jiang He, Lu Wang, Meng Wei and Sijia Yang

Batteries 2026, 12(8), 295; https://doi.org/10.3390/batteries12080295 - 8 Aug 2026

Viewed by 640

Abstract

This paper proposes an SOH estimation method that fuses mechanical–electrical–thermal multi-modal features by introducing expansion force monitoring. Aging tests on 16 prismatic 530 Ah LiFePO₄ batteries from two brands are conducted at 25 and 45 °C. Each full cycle is divided into [...] Read more.

This paper proposes an SOH estimation method that fuses mechanical–electrical–thermal multi-modal features by introducing expansion force monitoring. Aging tests on 16 prismatic 530 Ah LiFePO₄ batteries from two brands are conducted at 25 and 45 °C. Each full cycle is divided into charge, post-charge rest, discharge, and post-discharge rest, with SOH defined by the capacity ratio. From cycle-level data, 37 candidate features are extracted and cleaned using local median and median absolute deviation. Using only training cells, Spearman correlation eliminates highly redundant features, and 12 key features are retained via internal validation. Under 4-fold cross-validation with complete battery grouping, Random Forest, XGBoost, LightGBM, and LSTM are compared. LightGBM achieves the best performance with an average MAE of 0.0032, RMSE of 0.0037, and R² of 91.36%. Ablation shows multi-modal fusion outperforms single-type features; five-category fused features reduce RMSE by ~75.57% versus electrical-only features. Removing expansion force features increases RMSE to 0.0064 and drops R² to 75.66%. These findings confirm that expansion force supplies critical mechanical degradation information, significantly improving SOH estimation for large-capacity energy storage batteries. Full article

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

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22 pages, 4712 KB

Open AccessArticle

SOH Estimation of Lithium-Ion Batteries Using a Residual Multilayer Perceptron-Based, Physics-Informed Neural Network for the Battery Management System

by Radhika G R and Kanthalakshmi Srinivasan

Batteries 2026, 12(8), 294; https://doi.org/10.3390/batteries12080294 - 8 Aug 2026

Viewed by 457

Abstract

Precise estimation of lithium-ion battery State of Health (SOH) is highly demanded for reliable battery management systems, lifetime prediction, and safety assurance in electric vehicle and energy storage applications. Traditional data-driven approaches such as multilayer perceptron (MLP) often suffer from poor generalization and [...] Read more.

Precise estimation of lithium-ion battery State of Health (SOH) is highly demanded for reliable battery management systems, lifetime prediction, and safety assurance in electric vehicle and energy storage applications. Traditional data-driven approaches such as multilayer perceptron (MLP) often suffer from poor generalization and may produce non-physical degradation trends due to the absence of domain knowledge constraints. To address these limitations, this work proposes a monotonic Physics-Informed Residual MLP neural network framework for SOH estimation using the NASA battery dataset (B0005, B0006, B0007, and B0018). The proposed model incorporates a physics-based monotonic degradation constraint by penalizing positive gradients of SOH with respect to cycle index, thereby enforcing physically consistent capacity fade behavior. A loss function is employed to improve robustness and enhance late-cycle learning. Experimental results demonstrate that the proposed approach achieves an RMSE of 0.0287, MAE of 0.0181, and MAPE of 2.69%, indicating accurate and stable SOH prediction across multiple degradation patterns. The use of physics-informed constraints markedly enhances deterioration consistency and diminishes overfitting relative to solely data-driven models. The proposed structure offers a faithful solution for State of Health estimation in practical battery management systems. Full article

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19 pages, 2146 KB

Open AccessArticle

A Threshold-Adaptive Framework for Quantitative Diagnosis of Internal Short Circuits in LiFePO₄ Batteries Using Multi-Feature Incremental Capacity Curves

by Rui Xiong, Lizi Qu, Jing V. Wang, Zhichao Gong, Qian Wang and Jianqiang Kang

Batteries 2026, 12(8), 293; https://doi.org/10.3390/batteries12080293 - 7 Aug 2026

Viewed by 349

Abstract

Although incremental capacity (IC) curve analysis is promising for early internal short circuit (ISC) detection, its diagnostic accuracy degrades significantly across a wide resistance range, especially for low-resistance events dominated by leakage currents. To overcome this limitation, we propose a threshold-adaptive ISC diagnostic [...] Read more.

Although incremental capacity (IC) curve analysis is promising for early internal short circuit (ISC) detection, its diagnostic accuracy degrades significantly across a wide resistance range, especially for low-resistance events dominated by leakage currents. To overcome this limitation, we propose a threshold-adaptive ISC diagnostic framework that dynamically integrates quantitative resistance calculation (for high resistance, R ≥ 100 Ω) with Gaussian process regression (GPR)-based leakage current analysis (for low resistance, R < 100 Ω). Validated on 20 Ah LiFePO₄ batteries, this approach achieves <6% error for 100–300 Ω and <8% error for <100 Ω (after GPR correction), demonstrating robust, implementation-ready solutions for real-world battery safety monitoring. Full article

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33 pages, 13142 KB

Open AccessArticle

Battery SOC Estimation Based on IAFFRLS-IGWO-AEKF Method

by Hui Luan, Meng Xu, Xinyue Piao, Song Zhang, Benxin Wu and Baofeng Tian

Batteries 2026, 12(8), 292; https://doi.org/10.3390/batteries12080292 - 6 Aug 2026

Viewed by 246

Abstract

To ensure the safe and stable operation of energy storage systems (ESS) during peak power supply periods, this study proposes an enhanced state of charge (SOC) estimation framework for lithium-ion batteries. By integrating an Improved Adaptive Forgetting Factor Recursive Least Squares (IAFFRLS) method [...] Read more.

To ensure the safe and stable operation of energy storage systems (ESS) during peak power supply periods, this study proposes an enhanced state of charge (SOC) estimation framework for lithium-ion batteries. By integrating an Improved Adaptive Forgetting Factor Recursive Least Squares (IAFFRLS) method with an adaptive extended Kalman filter (AEKF) optimized by an Improved Gray Wolf Optimizer (IGWO), the proposed method achieves superior dynamic adaptability. Specifically, the IAFFRLS employs a sliding-window root-mean-square error to dynamically adjust the forgetting factor, effectively mitigating the impact of single-point disturbances. Comparative results indicate that the average voltage estimation error is reduced by 58.62% compared to the conventional AFFRLS method. Four dynamic condition tests demonstrate that the proposed IAFFRLS–IGWO–AEKF method achieves average absolute errors of 0.186–0.209% in SOC estimation and 0.069–0.088% in voltage estimation, significantly outperforming two benchmark algorithms and enabling efficient, accurate, and stable SOC estimation for lithium-ion batteries in energy storage systems. Full article

(This article belongs to the Topic Solar and Wind Power and Energy Forecasting, 2nd Edition)

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18 pages, 2222 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

Viewed by 312

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

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23 pages, 10195 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

Viewed by 220

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

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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

Viewed by 627

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

Viewed by 327

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

Viewed by 402

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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Batteries, Volume 12, Issue 8 (August 2026) – 49 articles

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