Batteries

29 pages, 4045 KB

Open AccessArticle

Hybrid SOC Estimation for LiFePO₄ Batteries Using Observability- and Innovation–Reliability-Regulated EKF with Reliability-Scaled Residual Learning

by Junrui Wang, Wenlei Wei, Bowen Ma, Hang Pan and Guanlan Liu

Batteries 2026, 12(9), 328; https://doi.org/10.3390/batteries12090328 (registering DOI) - 27 Aug 2026

Abstract

Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LiFePO₄) batteries is challenging because the voltage feedback used for correction does not provide constant SOC-related information under different operating conditions. Conventional extended Kalman filters (EKFs) usually apply measurement correction based on predefined [...] Read more.

Accurate state-of-charge (SOC) estimation of lithium iron phosphate (LiFePO₄) batteries is challenging because the voltage feedback used for correction does not provide constant SOC-related information under different operating conditions. Conventional extended Kalman filters (EKFs) usually apply measurement correction based on predefined statistical assumptions, while overlooking variations in voltage-domain observability and innovation reliability. This paper proposes a hybrid estimation framework, termed observability- and innovation–reliability-regulated EKF with reliability-scaled residual learning (OIR-EKF-RSRL). The proposed method retains a first-order RC model and EKF as the physical estimation backbone, while regulating voltage correction according to local OCV-SOC sensitivity and normalized innovation reliability. A reliability-scaled residual learning module is further introduced after physical filtering to compensate for remaining SOC deviations rather than directly predicting SOC. The learned residual correction is modulated by a reliability-dependent scaling coefficient before fusion with the OIR-EKF estimate, after which the final SOC estimate is constrained to the physical interval [0, 1]. The framework is evaluated using the CALCE A123 LiFePO₄ dataset under a frozen temperature-disjoint train–validation–holdout protocol. On the independent holdout set, OIR-EKF-RSRL reduces the RMSE from 3.331 percentage points for the conventional EKF to 2.942 percentage points. The results demonstrate that reliability-aware measurement utilization and reliability-scaled residual compensation provide an interpretable solution for LiFePO₄ SOC estimation under varying voltage-information quality. Full article

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

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21 pages, 13337 KB

Open AccessArticle

Research on ISC Triggering Behavior of Lithium-Ion Batteries in Bionic Underwater Vehicles Under Indentation Conditions

by Xuefei Wang, Shaowei Zhang, Guang Pan, Yuli Hu, Yu Pei and Chengyi Lu

Batteries 2026, 12(9), 327; https://doi.org/10.3390/batteries12090327 - 27 Aug 2026

Abstract

Structurally integrated lithium-ion batteries (LIBs) in bionic underwater vehicles face increasingly complex internal short circuit (ISC) risks under mechanical abuse. In this study, a three-dimensional bidirectional mechanical–electrical–thermal coupling model is established to reconstruct the evolution from structural damage to ISC triggering in cylindrical [...] Read more.

Structurally integrated lithium-ion batteries (LIBs) in bionic underwater vehicles face increasingly complex internal short circuit (ISC) risks under mechanical abuse. In this study, a three-dimensional bidirectional mechanical–electrical–thermal coupling model is established to reconstruct the evolution from structural damage to ISC triggering in cylindrical LIBs under indentation conditions. A constitutive inversion method incorporating load, contact area, and volume evolution is proposed to calibrate the jellyroll stress–strain response for different indenter diameters. An ISC criterion based on separator thickness is then introduced, and local short-circuit paths are realized through dynamic topology updates of the distributed equivalent circuit model network. The calibrated model reproduced the experimental load response, voltage decay, temperature rise, and damage morphology. The systematic investigation into ISC behavior shows that indenter diameter governs competition among local shear, local bending, and global compression, while loading position determines structural constraint and boundary effects. Rather than corresponding to the minimum ISC load, the most hazardous condition (4 mm indenter diameter and 18 mm loading position) exists where local stress concentration and weakened structural constraints jointly promote rapid separator failure, shortening the ISC triggering time to 79.2 s. These findings provide guidance for battery safety assessment and structural protection design in underwater vehicles. Full article

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

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17 pages, 3521 KB

Open AccessArticle

Chemically Active LiH₂PO₄ Interphase Precursor Enables Self-Limited Interface Stabilization in Sulfide-Based All-Solid-State Batteries

by Youngmin Lee, Eun Chan Heo, Yong Joon Park and Dongwook Shin

Batteries 2026, 12(9), 326; https://doi.org/10.3390/batteries12090326 - 26 Aug 2026

Abstract

The commercialization of sulfide-based all-solid-state batteries (ASSBs) is severely limited by the interfacial degradation between layered oxide cathodes and sulfide solid electrolytes, resulting in electrolyte decomposition, impedance growth, and rapid capacity fading. Herein, we propose lithium dihydrogen phosphate (LiH₂PO₄, [...] Read more.

The commercialization of sulfide-based all-solid-state batteries (ASSBs) is severely limited by the interfacial degradation between layered oxide cathodes and sulfide solid electrolytes, resulting in electrolyte decomposition, impedance growth, and rapid capacity fading. Herein, we propose lithium dihydrogen phosphate (LiH₂PO₄, LDP) as a low-cost polyanionic interphase precursor to regulate the cathode–electrolyte interface. A uniform LDP coating was deposited onto polycrystalline NCM622 particles, producing a continuous 40-nm-thick phosphate layer. Although the LDP-coated electrode exhibited a higher initial interfacial resistance than the bare cathode, electrochemical analyses revealed a capacity retention of 94.1% after 100 cycles, compared with 73.9% for the uncoated electrode. X-ray photoelectron spectroscopy demonstrated that LiH₂PO₄ is not chemically inert toward Li₆PS₅Cl but undergoes a controlled initial reaction with sulfur-deficient species to generate a phosphate-rich artificial interphase while suppressing the unstable P–[S]_n–P species. This interphase rapidly reaches chemical equilibrium, suppressing sulfur precipitation, SO_x formation, and subsequent electrolyte decomposition. Cross-sectional STEM–EDS and focused ion beam analyses revealed that the artificial phosphate interphase inhibited elemental interdiffusion, suppressed chemical mixing, and preserved mechanical contact during prolonged cycling. These findings demonstrate that the electrochemical penalty associated with the initial formation of the LDP interphase represents the necessary cost of constructing a chemically stable interface rather than a degradation process. This study introduces a new interfacial design strategy based on chemically active sacrificial interphase precursors, providing an alternative to conventional inert oxide coatings for realizing long-term stable sulfide-based all-solid-state batteries. Full article

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

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24 pages, 42874 KB

Open AccessArticle

Effect of the Activating Agent on the Pore Structure of Chimney-Soot-Derived Carbon Materials Used as Supercapacitors

by Boryana Karamanova, Ofeliya Kostadinova, Ognian Dimitrov, Adriana Gigova, Antonia Stoyanova and Toma Stankulov

Batteries 2026, 12(9), 325; https://doi.org/10.3390/batteries12090325 - 25 Aug 2026

Abstract

This study investigated physically and chemically activated chimney soot to evaluate the influence of the activation process on the textural properties and porous structure of materials intended for use in supercapacitors. The materials were characterized by Raman spectroscopy to analyze molecular structure and [...] Read more.

This study investigated physically and chemically activated chimney soot to evaluate the influence of the activation process on the textural properties and porous structure of materials intended for use in supercapacitors. The materials were characterized by Raman spectroscopy to analyze molecular structure and composition, as well as BET analysis to determine porosity and structural properties based on N₂ adsorption-desorption isotherms. Their electrochemical characteristics were evaluated using a two-electrode configuration with cyclic voltammetry, galvanostatic charge–discharge measurements, and long-term cycling tests of up to 10,000 cycles within a voltage window of 0.05–1.0 V in a 1 M KOH electrolyte. Soot-based symmetric supercapacitors activated with KOH at a ratio of 3:1 exhibit a specific capacitance of 74–78 F g⁻¹ at 0.2 A g⁻¹ and achieve an energy density of 2.3 Wh kg⁻¹ at a power density of 32 W kg⁻¹. The results demonstrate that, by carefully controlling the type and quantity of the activating agent, chemical activation is an effective method for increasing the specific surface area and improving the electrochemical characteristics of the resulting materials. Chemical activation significantly expands the microporous framework, optimizing ion transport at the electrode–electrolyte interface. The results demonstrate the successful valorization of chimney soot as functional carbon electrodes for next-generation energy storage applications. Full article

(This article belongs to the Special Issue High-Performance Supercapacitors: Preparation and Application—2nd Edition)

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27 pages, 16648 KB

Open AccessArticle

Impact of Busbar Resistance and Series–Parallel Topology on Current Inhomogeneity and Safety Limits in Battery Packs

by Xiaoxuan Chen, Dmitri L. Danilov, Tim-Andy Benning, Luc H. J. Raijmakers and Rüdiger-A. Eichel

Batteries 2026, 12(9), 324; https://doi.org/10.3390/batteries12090324 - 25 Aug 2026

Abstract

Current distribution in serial–parallel battery packs is commonly assumed to be uniform in the absence of cell-to-cell variations. However, in practical systems, the electrical topology and finite resistance of current-collecting busbars can introduce significant inhomogeneities even when all cells are identical. In this [...] Read more.

Current distribution in serial–parallel battery packs is commonly assumed to be uniform in the absence of cell-to-cell variations. However, in practical systems, the electrical topology and finite resistance of current-collecting busbars can introduce significant inhomogeneities even when all cells are identical. In this work, a matrix-based modeling framework is developed to analyze the current and voltage distribution in large battery packs with arbitrary serial–parallel configurations. The results reveal that the resistance of current-supplying busbars plays a dominant role in shaping current distribution, leading to pronounced current imbalance that increases with both resistance and operating C-rate. To quantify this effect, a current non-uniformity factor is introduced and used to define an illustrative criterion for acceptable operation. Based on this metric, together with a maximum-cell-voltage constraint, design maps are constructed to identify operating regions that are acceptable or critical with respect to current overload and localized overvoltage as a function of busbar resistance and charging rate. The analysis further demonstrates that topology-induced current inhomogeneity can lead to cell-level voltage divergence and localized overcharge under high-current operation. Such local effects may remain hidden when only the pack voltage or the voltage of a series-connected cell group is monitored, because conventional battery management systems (BMSs) typically do not resolve individual cell currents or local voltage drops within parallel-connected cell groups. The proposed approach enables the derivation of design-oriented constraints linking electrical performance to physical parameters such as busbar resistance and cell spacing. The resulting design maps provide a practical tool for battery pack engineering, enabling the determination of the maximum allowable busbar resistance or operating current to ensure safe, homogeneous pack operation. Full article

(This article belongs to the Special Issue Battery-Based Energy Storage Systems: Latest Results on Design, Safety and Performance)

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28 pages, 3389 KB

Open AccessArticle

Physics-Informed Attention-Enhanced Reinforcement Learning for Safe and Explainable Fast Charging of Lithium-Ion Batteries

by Marran Al Qwaid, Gobbi Ramasamy and Md Sabbir Hossen

Batteries 2026, 12(9), 323; https://doi.org/10.3390/batteries12090323 - 24 Aug 2026

Abstract

Fast charging of lithium-ion batteries requires balancing charging efficiency with electrochemical safety to minimize degradation and lithium plating. Conventional charging strategies and existing reinforcement learning approaches often lack physical consistency and model interpretability, limiting their applicability in safety-critical battery management systems. This paper [...] Read more.

Fast charging of lithium-ion batteries requires balancing charging efficiency with electrochemical safety to minimize degradation and lithium plating. Conventional charging strategies and existing reinforcement learning approaches often lack physical consistency and model interpretability, limiting their applicability in safety-critical battery management systems. This paper proposes a physics-informed Attention-Proximal Policy Optimization (Attention-PPO) framework for intelligent battery fast charging by integrating the Single Particle Model with Electrolyte (SPMe) with a transformer-based attention mechanism. SPMe provides physically meaningful battery state transitions, while the attention-enhanced PPO dynamically learns informative electrochemical representations for charging control. To improve transparency, a monotonic XGBoost surrogate model is employed for lithium-plating risk estimation, and the learned policy is further distilled into an interpretable decision tree. Experimental results demonstrate that the proposed Attention-PPO achieves substantially faster and more stable policy convergence than the baseline PPO, reaching convergence at episode 37 compared with episode 82 for PPO, corresponding to a 54.9% reduction in training episodes. The reward standard deviation is also reduced from 0.132 to 0.041, indicating 68.9% lower reward variability. In terms of electrochemical safety, Attention-PPO achieves a mean plating overpotential of +0.023 V compared with −0.015 V for PPO, providing a 38 mV improvement and a positive safety margin against lithium plating. Compared with conventional CC-CV and CC-COP controllers, the proposed framework requires a longer charging duration because it prioritizes electrochemical safety; however, it consistently maintains positive plating overpotential while achieving reliable charging performance. Compared with conventional CC-CV, CC-COP, and standard PPO controllers, the proposed framework provides a larger electrochemical safety margin while preserving reliable charging performance. Transformer attention analysis and policy distillation provide interpretable representations of the learned charging policy, while SHAP analysis characterizes feature contributions within the auxiliary plating-risk estimator. The proposed framework provides an effective and explainable physics-informed reinforcement learning solution for safe lithium-ion battery fast charging, offering a promising approach for next-generation intelligent battery management systems. Full article

(This article belongs to the Topic Electric Vehicles Smart Charging: Strategies, Technologies, and Challenges-2nd Edition)

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25 pages, 12928 KB

Open AccessArticle

Mission-Phase Feature Learning for eVTOL Li-Ion Battery Prognostics: A Leakage-Safe Cell-Held-Out Benchmark for SOC, SOH, and RUL

by Su Yan and Musa Wiston

Batteries 2026, 12(9), 322; https://doi.org/10.3390/batteries12090322 - 24 Aug 2026

Abstract

Reliable battery prognostics for electric vertical take-off and landing (eVTOL) aircraft require models that preserve phase-dependent electrothermal information while generalizing to cells absent from training. This study reconstructs the public CMU eVTOL battery dataset and establishes a leakage-safe benchmark for state of charge [...] Read more.

Reliable battery prognostics for electric vertical take-off and landing (eVTOL) aircraft require models that preserve phase-dependent electrothermal information while generalizing to cells absent from training. This study reconstructs the public CMU eVTOL battery dataset and establishes a leakage-safe benchmark for state of charge (SOC), five-mission-ahead state of health (SOH), and threshold-based remaining useful life (RUL). A protocol-based screen identified 441 valid C/5 reference-performance-test anchors across 22 cells; three cells with non-monotone diagnostic-capacity trajectories were excluded from the primary health benchmark, leaving 19 cells. Predictors were restricted to telemetry-derived phase and mission features, with cell identity and target- or future-derived quantities excluded. All health models were evaluated using outer leave-one-cell-out validation with matched 20-mission histories. Random Forest achieved the lowest SOH MAE of 0.620 percentage points, compared with 1.252 for the mission-phase Transformer and 1.471 for Attention-LSTM-MoE. The best RUL MAEs were 24.067, 46.527, and 122.198 missions at the 90%, 85%, and 80% SOH thresholds. Cell-bootstrap uncertainty showed that model differences were threshold-dependent. Row-random splitting produced substantially more optimistic errors than cell-held-out evaluation. These results show that rigorous target construction and leakage-safe validation are critical and that increased sequence-model complexity does not guarantee superior unseen-cell generalization. Full article

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23 pages, 9957 KB

Open AccessArticle

Cross-Condition State-of-Health Estimation of Lithium-Ion Batteries via Degradation-Feature Constraints and Domain-Difference Gating

by Zhanyu Li, Qingwen Lin, Songfeng Liang, Jiaxin Gao, Heran Song and Ruichao Wei

Batteries 2026, 12(9), 321; https://doi.org/10.3390/batteries12090321 - 24 Aug 2026

Abstract

To address the limited generalization of lithium-ion battery state-of-health (SOH) estimation under unseen aging conditions, this study proposes a degradation-feature-constrained domain-difference gated method (DFC-DGGate). Cycle-level features are constructed from capacity, voltage, local statistics, and first-order degradation variations. Three branches, namely Local ET, Trend [...] Read more.

To address the limited generalization of lithium-ion battery state-of-health (SOH) estimation under unseen aging conditions, this study proposes a degradation-feature-constrained domain-difference gated method (DFC-DGGate). Cycle-level features are constructed from capacity, voltage, local statistics, and first-order degradation variations. Three branches, namely Local ET, Trend Ridge, and Robust Huber, are used to characterize local nonlinear mapping, global degradation trends, and robust estimation, respectively. Their outputs are fused by a condition-aware domain-difference gate and further smoothed to obtain continuous SOH estimates. Cell-wise and condition-wise validations are conducted on the XJTU dataset, and external testing is performed on the NASA dataset. Under XJTU condition-wise validation, DFC-DGGate achieves an RMSE of 14.3895%, while the best-performing baseline, SVR, achieves 5.7623%. The proposed framework remains more accurate than the global Trend Ridge branch (22.3915%) but does not outperform the strongest nonlinear baselines under the substantial Sim_satellite shift. In XJTU-to-NASA validation, the anchor-corrected external extension achieves RMSEs of 20.45% and 19.10% on NASA core and NASA clean, respectively, slightly outperforming ExtraTrees on both subsets. Full article

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

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24 pages, 19539 KB

Open AccessArticle

Early Prediction of Lithium-Ion Battery Remaining Useful Life Using a GWO-Optimized CNN–Transformer–BiGRU Network

by Chongyang Wei, Xinfu Pang, Jingran Sheng, Hongxia Yu, Zedong Zheng and Pengwei Yu

Batteries 2026, 12(9), 320; https://doi.org/10.3390/batteries12090320 - 24 Aug 2026

Abstract

Lithium-ion batteries are widely used in various energy sectors, and accurately predicting their early remaining useful life (RUL) is crucial for shortening battery evaluation time and accelerating battery commercialization. However, information on degradation during the early cycling stages of batteries is limited, and [...] Read more.

Lithium-ion batteries are widely used in various energy sectors, and accurately predicting their early remaining useful life (RUL) is crucial for shortening battery evaluation time and accelerating battery commercialization. However, information on degradation during the early cycling stages of batteries is limited, and it is difficult to fully characterize their lifespan. This study proposes a CNN–Transformer–BiGRU-based method for predicting the early RUL of lithium-ion batteries using Grey Wolf Optimization (GWO). First, using only the first 100 cycles of each battery in the MIT dataset, early degradation features are extracted from the dimensions of capacity and internal resistance, and then standardized. Second, a CNN is employed to extract local degradation features, while the Transformer’s self-attention mechanism is used to capture global correlations, and BiGRU is utilized to further extract bidirectional temporal dependency information. Building on this foundation, GWO is introduced to perform joint optimization of the model’s key hyperparameters to obtain optimal network parameters. Finally, the effectiveness of the proposed method is validated through ablation and comparison experiments. The experimental results show that the proposed model achieved an R² of 0.9633, with RMSE, MAE, and MAPE values of 80.5608 cycles, 63.2524 cycles, and 7.29%, respectively, demonstrating overall prediction performance superior to that of the comparison models. This method can effectively mine degradation information related to battery life from limited early-cycle data, providing an effective approach for the accurate prediction of the early RUL of lithium-ion batteries. Full article

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

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30 pages, 15758 KB

Open AccessArticle

A Multi-Channel DC-Bias-Tolerant Electrochemical Impedance Spectroscopy Device for Lithium-Ion Battery Diagnostics

by Chunjing Yue, Shupeng Zhao, Xiaokang Shi, Hui Yang, Rui Zhu, Fengwei Liang and Yulong Zhang

Batteries 2026, 12(9), 319; https://doi.org/10.3390/batteries12090319 - 23 Aug 2026

Abstract

Electrochemical impedance spectroscopy (EIS) resolves the internal physicochemical processes of lithium-ion batteries across timescales—from ohmic conduction through charge-transfer kinetics to solid-state diffusion. Despite this analytical power, EIS deployment remains largely confined to laboratory electrochemical workstations that are bulky, expensive, and incapable of online [...] Read more.

Electrochemical impedance spectroscopy (EIS) resolves the internal physicochemical processes of lithium-ion batteries across timescales—from ohmic conduction through charge-transfer kinetics to solid-state diffusion. Despite this analytical power, EIS deployment remains largely confined to laboratory electrochemical workstations that are bulky, expensive, and incapable of online multi-cell operation under dynamic DC bias conditions. This study presents a multi-channel EIS measurement device that simultaneously addresses three requirements for practical battery diagnostics: workstation-grade measurement accuracy, multi-cell synchronous acquisition, and tolerance to the DC bias voltage present across battery terminals during operation. The device employs a master–slave distributed architecture: each slave unit is built around the DNB1101 battery-dedicated impedance measurement chip with a Kelvin four-wire sensing configuration, while the STM32F407-based master controller coordinates measurement scheduling and data communication under FreeRTOS. A four-channel slave board with a differential daisy-chain communication topology and hardware broadcast trigger mechanism supports multi-cell synchronous acquisition. The device operates over a frequency range of 0.01 Hz to 5620 Hz with logarithmic spacing, and a C#-based host application provides real-time Nyquist and Bode visualization along with MATLAB R2024a-based post-processing for outlier rejection and data smoothing. Validation was conducted using Panasonic NCR18650 ternary (NCA) and LiFePO₄ (LFP) 18650 cells, benchmarked against a CorrTest CS350 electrochemical workstation at SOC = 40% and 25 °C. The device achieves a maximum impedance magnitude error of 1.55% and a maximum phase error of 1.22%. Equivalent circuit model fitting via ZSimpWin yields parameter differences below 1% between the device and the reference workstation. Under online conditions with a 3.6 V DC bias, the impedance measurement deviation of a 20 mΩ precision resistor remains below 0.69% across the full frequency range. Multi-channel synchronous measurements across four cells demonstrate inter-channel amplitude variance below 2.13%. Cross-chemistry validation with LiFePO₄ cells yields magnitude and phase errors below 0.92%. These results demonstrate that the proposed device provides laboratory-grade EIS accuracy with multi-channel, online, and cross-chemistry capabilities, offering a practical platform for integrating EIS-based diagnostics into next-generation battery management systems. Full article

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

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28 pages, 2232 KB

Open AccessArticle

Cradle-to-Gate Sustainability Assessment of Composite and Metallic Battery Housings for Transport and Stationary Energy Storage Applications

by Aikaterini Fragiadaki, Christina Vogiantzi and Konstantinos Tserpes

Batteries 2026, 12(9), 318; https://doi.org/10.3390/batteries12090318 - 23 Aug 2026

Abstract

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic [...] Read more.

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic impacts and supply chain vulnerabilities. This study presents a comprehensive cradle-to-gate environmental life cycle assessment (LCA), life cycle costing (LCC), and semi-quantitative social assessment of alternative battery housing materials and battery cell architectures. To achieve a functionally accurate comparison, alternative materials, including a novel recyclable thermoplastic acrylic sheet molding compound (SMC), commercial thermoset SMCs, aluminum (AlMg₃), and stainless steel, are evaluated using an analytical stiffness- and strength-equivalent methodology across three real-world geometric demonstrators. Simultaneously, lithium iron phosphate (LFP) liquid electrolyte prismatic cells and solid-state polymer pouch cells are assessed. Material-level results indicate that, while aluminum minimizes the structural mass, primary aluminum manufacturing exhibits the highest global warming potential and processing costs. Conversely, Polytec SMC and Elium SMC achieve the lowest environmental impacts alongside competitive total production costs. At the cell level, prismatic LFP architectures display superior environmental performance compared to solid-state pouch cells, which suffer from energy-intensive processing and lower volumetric capacity normalization. Demonstrator-level aggregation reveals that the electrochemical cells heavily dominate the environmental and economic footprint of the complete assembly, with the housing accounting for less than 5% of the total global warming potential (GWP) and 1% of the total costs. The social assessment reveals moderate and comparable performance across all systems, with slight advantages for thermoplastic composite-based configurations in terms of circularity potential and innovation perception. Overall, the study highlights the critical importance of the cell architecture and manufacturing processes in determining battery system sustainability, while demonstrating the relevance of lightweight composite housings in reducing the structural mass with a minimal environmental penalty. Full article

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23 pages, 4457 KB

Open AccessArticle

Design, Fabrication, and In-Flight Demonstration of a 24S NCM Battery System for an eVTOL Aircraft

by SuHo Yu, Yu-Jin Jung, Bum-Dong Cho and Gee-Soo Lee

Batteries 2026, 12(9), 317; https://doi.org/10.3390/batteries12090317 - 22 Aug 2026

Abstract

Reliable pack-level battery systems capable of safely handling instantaneous high-C-rate discharge above 10C during take-off, climb, and hovering are required for the commercialization of urban air mobility (UAM) aircraft. However, pack-level studies on wide-range C-rate characteristics of battery systems for UAM applications remain [...] Read more.

Reliable pack-level battery systems capable of safely handling instantaneous high-C-rate discharge above 10C during take-off, climb, and hovering are required for the commercialization of urban air mobility (UAM) aircraft. However, pack-level studies on wide-range C-rate characteristics of battery systems for UAM applications remain very limited, and most previous studies have been restricted to single-cell experiments or battery-pack simulations. In this study, a 24S1P test battery pack using nickel–cobalt–manganese (NCM) pouch cells, with a nominal voltage of 88.8 V and a capacity of 22 Ah, was designed and fabricated. A two-level battery management system (BMS) based on the LTC6803G-4 was also developed. To evaluate the charge–discharge characteristics of the battery system, constant-current discharge tests were conducted under five conditions ranging from 0.2C (4.4 A) to 10.68C (235 A), and charging tests were performed over the range of 0.2C–2C. The discharge test results showed that the capacity retention remained within 97.5–100.0% in the 1C–5C range, confirming excellent power capability. Continuous discharge operation was confirmed at 10.68C, the maximum discharge condition considered for vertical take-off and climb. Under this condition, the capacity decreased to 16.26 Ah, corresponding to 74.2% of the rated capacity, owing to internal-resistance-induced voltage drop, electrochemical polarization, and early attainment of the cut-off voltage. The Peukert exponent was estimated to be 1.113. An apparent pack-level direct-current internal resistance (DCIR) of approximately 40.3 mΩ was estimated from the initial voltage-drop analysis under different discharge-current conditions. In addition, the maximum temperature during 10.68C discharge was measured as 55.1 °C, providing a thermal margin of 4.9 °C relative to the operational temperature limit of 60 °C adopted in this study. Finally, a 24S4P battery system with a capacity of 88 Ah, consisting of four 24S1P battery packs connected in parallel, was installed in the VS-210, a 210 kg-class maximum take-off weight (MTOW) eVTOL aircraft. An in-flight test was conducted by repeating six take-off–hovering–landing cycles during a total test session of 15 min 20 s, and a stable propulsion power supply was maintained throughout all flight cycles. This study provides experimental baseline data for the design and preliminary safety assessment of high-power battery systems for UAM applications by presenting both the electrical and thermal characteristics of a 24S NCM battery pack over a wide discharge-rate range of 0.2C–10.68C and in-flight eVTOL data. Full article

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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

Abstract

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

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

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

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

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

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

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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