Journal of energy storage最新文献

{"title":"A data-driven architecture fusing Kolmogorov-Arnold feature extraction and contextual-attention long short-term memory network for accurate state-of-charge estimation in lithium-ion batteries under dynamic operating conditions","authors":"Syed Abbas Ali Shah ,&nbsp;Syed Maooz Ali Shah ,&nbsp;Shunli Wang ,&nbsp;Mahrukh ,&nbsp;Shungang Ning ,&nbsp;Ziqiang Xu ,&nbsp;Mengqiang Wu","doi":"10.1016/j.est.2025.118819","DOIUrl":"10.1016/j.est.2025.118819","url":null,"abstract":"<div><div>Accurate estimation of lithium-ion battery state of charge (SOC) under dynamic conditions remains challenging. This study introduces a hybrid architecture that combines a Kolmogorov-Arnold Network (KAN) for theoretical nonlinear decomposition of battery signals, a feature-wise contextual attention (FCA) mechanism for adaptive channel weighting, and stacked long short-term memory (LSTM) layers for temporal modeling, collectively termed KALSTM framework. The KAN front-end leverages the Kolmogorov–Arnold representation theorem to factorize current, voltage, and temperature into independent univariate mappings, recombining them through a single affine mixer to achieve universal approximation with far fewer parameters than a dense input layer. FCA replaces quadratic self-attention with a context-conditioned probability distribution over latent channels at each timestep, ensuring linear complexity in sequence length when feature dimension is fixed while selectively amplifying salient sensor information. The LSTM stack captures multiscale temporal dependencies within these refined embeddings to produce a single SOC estimate. Training was conducted on a subset of a publicly available battery dataset comprising urban-to-highway drive cycles collected at 0 °C, 10 °C, and 25 °C. Validation utilized the remaining records, including both fixed-temperature cycles and continuously varying thermal conditions, along with data from a different battery chemistry to evaluate generalization. The proposed KALSTM model achieved optimal accuracy, attaining an RMSE of 0.77 % and MAE of 0.63 % under fixed temperatures, and RMSE of 1.10 % and MAE of 0.89 % during dynamic thermal variations. It consistently outperformed a parameter-matched LSTM baseline and recent literature benchmarks. These results highlight its potential as a reliable and transferable tool for advanced battery state estimation.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118819"},"PeriodicalIF":8.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flexible management of power flows in the low-voltage grid using energy storage & artificial intelligence","authors":"Bartłomiej Mroczek ,&nbsp;Paweł Pijarski","doi":"10.1016/j.est.2025.118878","DOIUrl":"10.1016/j.est.2025.118878","url":null,"abstract":"<div><div>One of the main challenges of the energy sector at the moment is to be able to absorb maximum power and electricity from RES (Renewable energy sources), without applying constraints for them on the grid at any voltage level. This paper presents the proprietary Block model of the Low Voltage (LV) grid control system enabling full control of the power flow in the LV grid using BESS (Battery Energy System Storage). The block system of LV grid control is built on the basis of four dedicated algorithms within three logic blocks, described later in this article. The first two algorithms of the four run offline for optimal power selection and BESS location and for building the training database. The other two algorithms are the procedure for starting BESS operation and maintaining its continuity. The execution device (GPU microcontroller) responsible for the current BESS control is a deep learning convolutional machine, while a statistical shallow learning regression machine (mdl) is responsible for controlling the MV/LV transformer ratio settings. The research was carried out in a real LV grid with high-RES saturation. The model was implemented in the environment: Power Word Simulator, MATLAB and SIMULINK.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118878"},"PeriodicalIF":8.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic confinement by MOF-derived carbon and MXene in silicon-based anodes enables stable lithium storage","authors":"Wuyang Xiao ,&nbsp;Lin Hu ,&nbsp;Hui Xu ,&nbsp;Yaru Pei ,&nbsp;Qinggen Li ,&nbsp;Zhong Yang","doi":"10.1016/j.est.2025.118933","DOIUrl":"10.1016/j.est.2025.118933","url":null,"abstract":"<div><div>Silicon-based anodes offer high theoretical capacity for lithium-ion batteries but suffer from severe volume expansion during cycling, leading to structural degradation and capacity fade. This study presents a novel Si/SiO_<em>x</em>@C@MXene composite to overcome these limitations through the synergistic confinement by the metal-organic-framework (MOF) derived carbon and MXene. The resulted Si/SiO_<em>x</em>@C@MXene composite features a unique architecture where MOF-derived carbon-coated Si/SiO_<em>x</em> nanospheres are embedded within or anchored onto the conductive MXene sheets, forming a robust three-dimensional sandwich-like structure. Electrochemical test reveal that Si/SiO_<em>x</em>@C@MXene delivers a high initial discharge capacity of 1158.0 mAh g⁻¹ at 100 mA g⁻¹, maintains 953.5 mAh g⁻¹ after 1000 cycles at 1000 mA g⁻¹ (84.9 % capacity retention), and exhibits excellent rate capability (543.9 mAh g⁻¹ at 5000 mA g⁻¹). This work highlights the effectiveness approach for developing high-performance, stable silicon-based anodes.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118933"},"PeriodicalIF":8.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145289563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of ultrasonic field on electrodeposition preparation of Zn anode and research on the battery performance","authors":"Yuting Chen ,&nbsp;Yulan Xue ,&nbsp;Ruimin Yang ,&nbsp;Rui Feng ,&nbsp;Xiaowen Wang ,&nbsp;Yaokun Pan ,&nbsp;Xinde Zhu ,&nbsp;Kebin Sun","doi":"10.1016/j.est.2025.118868","DOIUrl":"10.1016/j.est.2025.118868","url":null,"abstract":"<div><div>Due to high safety and environmental friendliness, aqueous Zn-ion batteries exhibit significant potential. However, during the electrodeposition process of Zn anode, dendrite formation and hydrogen evolution occur, which significantly deteriorate the performance of batteries. The unique cavitation effect of ultrasonic field significantly regulates the electrodeposition process of Zn²⁺ in the solution. In this paper, the surface morphology of Zn anode was observed, and the electrochemical techniques were employed to characterize the electrochemical behavior of electrodeposited Zn in a weakly acidic sulfate system under varying ultrasonic powers. The prepared Zn anode was assembled into Zn||AC full cell for tests, and the preferred orientation in Zn anode after cycling was characterized. The research shows that under the static condition, the surface presents a 3D stacked structure of hexagonal Zn flakes with disordered morphology. Under the ultrasonic field, stacking direction of hexagonal Zn flakes becomes more ordered, and the lamellae are more tightly packed. In particular, when the ultrasonic power is 70 W, Zn anode exhibits better electrochemical performance, meanwhile, the cell shows a higher Coulombic efficiency. After 1500 cycles, the Zn (002) crystal plane proportion of Zn anode prepared at 70 W is much higher.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118868"},"PeriodicalIF":8.9,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced joint estimation of lithium-ion battery state of charge and state of energy using regression tree and improved adaptive unscented particle filtering","authors":"Junhao Xu,&nbsp;Li Zhang,&nbsp;Lijun Xu,&nbsp;Qing Huang,&nbsp;Jianming Zhu,&nbsp;Wenyi Yuan","doi":"10.1016/j.est.2025.118889","DOIUrl":"10.1016/j.est.2025.118889","url":null,"abstract":"<div><div>Precise estimation of battery state of charge (SOC) and state of energy (SOE) is critical for enhancing the performance of electric vehicle battery management systems. This work presents a high-precision joint estimation method for SOC and SOE based on a regression tree (RT) model and an improved adaptive unscented particle filtering algorithm. Firstly, a first-order resistance-capacitance (RC) equivalent circuit model is employed, with model parameters identified online across a wide temperature range using variable forgetting factor recursive least squares method, addressing the insufficient performance of conventional offline parameter identification. Secondly, an RT-based open-circuit voltage (OCV)-SOC/SOE mapping approach is proposed, significantly reducing errors compared to traditional polynomial fitting. Finally, to overcome the particle degeneracy limitation of standard particle filters, an improved adaptive unscented particle filtering algorithm is introduced, substantially improving estimation accuracy and stability. Experimental validation under dynamic stress test and federal urban driving schedule profiles at 0 °C, 25 °C, and 45 °C demonstrates that the proposed method achieves root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of SOC estimation results below 0.96 %, 0.77 %, 1.61 % respectively, while those metrics corresponding to SOE estimation are less than 0.96 %, 0.84 % and 1.59 % respectively. Those results showcase the developed join estimation framework's high precision and enough robustness ability across wide-temperature range usage.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118889"},"PeriodicalIF":8.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated learning empowered microgrids: Lithium battery state-of-health prediction and multi-node co-optimisation","authors":"Xiaofen Fang,&nbsp;Weidong Chen,&nbsp;Tao Hu,&nbsp;Zijie Chen,&nbsp;Qingqiang Zeng,&nbsp;Jianqing Li","doi":"10.1016/j.est.2025.118892","DOIUrl":"10.1016/j.est.2025.118892","url":null,"abstract":"<div><div>With the wide application of lithium-ion batteries in microgrids, accurately predicting the State of Health (SOH) of the batteries and realising multi-node co-optimisation has become a key challenge to ensure system stability and prolong battery lifetime. In this paper, we propose a novel federated learning-driven SOH prediction and multi-node optimisation model for Li-ion batteries, named FedOptSOH. The framework is based on National Aeronautics and Space Administration (NASA)’s publicly available Li-ion battery dataset, and utilises heterogeneous data distributed across nodes to achieve highly accurate SOH prediction through local training and central server aggregation, while effectively protecting data privacy. FedOptSOH integrates advanced federated optimisation algorithms, Federated Average of Momentum (FedAvgM) and Adaptive Federated Optimisation (FedOpt), which significantly improves the training stability and convergence speed of the model. Based on the predicted SOH results, FedOptSOH further employs a multi-objective co-optimisation approach to achieve dynamic adjustment of charging and discharging strategies at each node in the microgrid, maximising energy efficiency and delaying battery degradation. Experimental results show that FedOptSOH achieves an Mean Absolute Error (MAE) of 4.17%, a Root Mean Square Error (RMSE) of 5.03%, and a coefficient of determination (R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of 0.978 in SOH prediction, which are significantly better than the traditional methods; multi-node co-optimisation effectively reduces the energy loss of the system by 12%, and extends the battery life by 15%. FedOptSOH framework combines high accuracy, strong privacy protection and real-time co-optimisation, providing a solid theoretical foundation and technical support for lithium battery management in smart microgrids.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118892"},"PeriodicalIF":8.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising supercritical CO2 saturation and reservoir conditions for geological energy storage with transcritical carbon dioxide systems","authors":"Dounya Behnous ,&nbsp;Julio Carneiro ,&nbsp;Andrés Carro ,&nbsp;Paula Canteli ,&nbsp;Ricardo Chacartegui ,&nbsp;Jesus G. Crespo ,&nbsp;Pavlos Tyrologou ,&nbsp;Nikolaos Koukouzas","doi":"10.1016/j.est.2025.118924","DOIUrl":"10.1016/j.est.2025.118924","url":null,"abstract":"<div><div>The CO₂-based Electrothermal Energy and Geological Storage (CEEGS) system integrates energy storage with CO₂ sequestration by storing excess renewable energy as supercritical CO₂, which is back-produced for power generation. This study investigates reservoir (porosity, permeability, relative permeability, heterogeneity, anisotropy) and operational (injection rates, shut-in periods) parameters to maximise CO₂ saturation near the wellbore and minimise water co-production, critical for the energy storage capacity and operation of the surface transcritical CO₂ power cycles. Using CMG-STARS and CMOST-AI, we conducted a sensitivity analysis across injection rates (5–100 kg/s), porosity (0.05–0.25), permeability (10–1000 mD), and heterogeneity (C.V. 0.1–1.5). Results show that injection rates of 30–40 kg/s, porosity of 0.05–0.15, and low heterogeneity (C.V. &lt;0.25) achieve gas saturation up o 76 % with water production below 0.1 kg/s. Shut-in periods should not exceed 3 months to limit saturation losses. These findings provide a robust framework for optimising the CEEGS site selection and operation definition, ensuring supercritical CO₂ back production with adequate characteristics for efficient energy storage and operation.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118924"},"PeriodicalIF":8.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rice husk derived porous carbon/β-PbO₂ composites as positive additives for lead‑carbon batteries","authors":"Yunfeng Ji ,&nbsp;Jian Tu ,&nbsp;Yapeng He ,&nbsp;Hongbiao Wang ,&nbsp;Hao Zhao ,&nbsp;Jianfeng Zhou ,&nbsp;Hui Huang ,&nbsp;Zhongcheng Guo","doi":"10.1016/j.est.2025.118930","DOIUrl":"10.1016/j.est.2025.118930","url":null,"abstract":"<div><div>The continuous improvement of negative electrode in lead‑carbon batteries (LCBs) renders the positive electrode a considerable obstacle in overall performances of LCBs. Rice husk derived porous carbon/β-PbO₂ (RHC/β-PbO₂) composite was synthesized and incorporated as positive additive to improve the utilization efficiency of positive active substances (PASs). The effects of RHC/β-PbO₂ additives on the substance conversion, crystalline size, and utilization efficiency of positive plates were investigated during the curing, formation, and cycling stages. The remarkable conductivity of porous RHC/β-PbO₂ composite provides an efficient conductive network, while the hydrophilicity regulates the local ion transfer environment, significantly accelerating electron/ion transfer within PASs. The highest initial discharge capacity of 3.31 Ah at 0.05C was achieved at 1.2 wt% RHC/β-PbO₂, representing 19.9 % promotion over the blank battery. Meanwhile, the LCBs could maintain a discharge capacity of 1.65 Ah on 1C and deliver a capacity retention ratio of 86.8 % after 200 cycles, demonstrating exceptional rate capability and cyclic durability. The comprehensive work proposes an alternative approach for developing powerful positive additives and promoting the substance transformation in LCBs.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118930"},"PeriodicalIF":8.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing MXene electrochemistry through spatial control of O and F termination distributions","authors":"Saheb Bera,&nbsp;Sidhant Kumar Barik,&nbsp;Hemant Kumar","doi":"10.1016/j.est.2025.118594","DOIUrl":"10.1016/j.est.2025.118594","url":null,"abstract":"<div><div>MXenes have attracted considerable attention as potential electrode materials due to their adjustable chemical properties while retaining excellent metallic conductivity. Mixed surface functionalization, exemplified by O and F terminations on Ti<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>C<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>2</mn><mrow><mo>(</mo><mn>1</mn><mo>−</mo><mi>x</mi><mo>)</mo></mrow></mrow></msub></math></span>F<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>x</mi></mrow></msub></math></span> MXenes, dictates electrochemical performance, yet the specific role of their spatial arrangement is poorly understood. This study utilizes a synergistic combination of density functional theory (DFT), kinetic Monte Carlo (KMC), and <em>ab initio</em> molecular dynamics (AIMD) to investigate how the nanoscale distribution of surface terminations governs Li transport and storage. DFT calculations identify distinct local neighbourhood environments characterized by unique Li diffusion barriers, challenging the simplification inherent in weighted-average compositional models. KMC simulations over 100 random O/F configurations show a 147-fold variation in Li diffusivity at room temperature, despite identical compositions. Furthermore, a composition-dependent analysis indicates that F-rich mixed-terminated surfaces facilitate faster Li diffusion compared to O-rich counterparts. The accuracy of the KMC model itself is validated by AIMD results, ensuring reliable dynamic predictions. Additionally, we show that the theoretical Li storage capacity is sensitive to these local termination environments. These findings reveal that, even at constant composition, the spatial configuration of surface terminations critically impacts electrochemical behaviour. This underscores the limitations of averaging approaches and highlights local termination structure as a key design parameter. Nanoscale control of termination patterns can significantly enhance MXene performance, enabling a new approach to modelling and optimizing 2D materials.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118594"},"PeriodicalIF":8.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of unit pressure distribution and roll stress analysis during lithium-ion battery electrode calendering","authors":"Guodong Wang ,&nbsp;Dongcheng Wang ,&nbsp;Bowei Duan ,&nbsp;Hongmin Liu","doi":"10.1016/j.est.2025.118698","DOIUrl":"10.1016/j.est.2025.118698","url":null,"abstract":"<div><div>Calendering is a critical manufacturing step for lithium-ion batteries, where precise control of process parameters significantly influences the electrode structure and battery performance. However, existing studies lack a well-established theoretical model that can accurately predict the calendering force and unit pressure distribution. To fill this gap, a mechanistic model was developed based on the Kuhn yield criterion to predict the unit pressure distribution, which was further validated through experimental measurements. Building upon this model, the characteristics of unit pressure distribution within the roll gap were analyzed. The effects of the compression rate and roll diameter on the unit width calendering force and unit pressure distribution were also investigated. Furthermore, the obtained unit pressure was used as a boundary condition to analyze roll stress. The study focused on the roll stress distribution characteristics during the electrode calendering, and the influences of compression rate and roll diameter on the maximum von Mises stress within the roll were evaluated. The proposed model and corresponding findings provide useful insights for optimizing the calendering process of battery electrodes and offer guidance for the design of related manufacturing equipment.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"139 ","pages":"Article 118698"},"PeriodicalIF":8.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}