Ionics最新文献

{"title":"Green-synthesized multifunctional TiO2 nanoparticles for efficient dye-sensitized solar cells, photocatalysis, and asymmetric supercapacitors","authors":"A. Mohamed Musthafa","doi":"10.1007/s11581-025-06944-4","DOIUrl":"10.1007/s11581-025-06944-4","url":null,"abstract":"<div>\u0000 \u0000 <p>This study developed a green method for synthesizing titanium dioxide (TiO₂) nanostructures by employing a water-based <i>Rosmarinus officinalis</i> leaf extract as an organic reducing and stabilizing agent. The structural, morphological and elemental composition was analysed through XRD, SEM and XPS. The green-synthesized TiO₂ demonstrated a 0.611 ± 0.05 V, open-circuit voltage (V_OC), 16.9 ± 0.02 mAcm⁻² short-circuit current density (J_SC), and 0.71 ± 0.02 fill factor (FF) when used as the photoanode material in dye-sensitized solar cells (DSSCs) coupled with a platinum counter electrode. The power conversion efficiency was 8.4 ± 0.05%. Compared to the benchmark P25 TiO₂ (η = 3.3 ± 0.01%, V_OC = 0.611 ± 0.05 mV, J_SC = 10.1 ± 0.02 mA cm⁻², FF = 0.55 ± 0.05), these values are significantly better. The methylene blue (MB) degradation was found to be 99% and 65% for TiO₂ and P25, respectively. Similarly, the rate constant was found to be 0.0986 and 0.0254 min⁻¹, TiO₂ and P25, respectively. The fabricated ASC deliver a specific capacitance at 1 A g⁻¹ was 210 F g⁻¹, the cycling stability was 99.5% retention, the Coulombic efficiency was 103.2%, and the energy and power densities were 56.7 Wh kg⁻¹ and 777 W kg⁻¹, respectively. These results demonstrate, in general, that green-fabricated TiO₂ nanoparticles have several potential uses in fields such as energy harvesting, storage, and environmental cleansing.</p>\u0000 </div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 3","pages":"3295 - 3307"},"PeriodicalIF":2.6,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State of health prediction for proton exchange membrane fuel cells using multi-scale indicators","authors":"Haitao Min,&nbsp;Xiubing Liu,&nbsp;Xia Sheng,&nbsp;Weiyi Sun,&nbsp;Zhaopu Zhang,&nbsp;Yipeng Lin","doi":"10.1007/s11581-025-06945-3","DOIUrl":"10.1007/s11581-025-06945-3","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate health estimation is critical for the safe and reliable operation of proton exchange membrane fuel cells (PEMFCs). However, achieving precise diagnostics is impeded by the complex, non-linear coupling between macroscopic performance decay and microscopic degradation mechanisms. To address these limitations, this study proposes a physics-informed data-driven framework. Leveraging a validated coupled multi-physics platform, we constructed a multiscale dataset bridging external macroscopic characteristics and internal microscopic degradation parameters, thereby enabling precise internal health characterization. A transformer model was then employed to capture the long-range dependencies of these polarization features and accurately predict the internal metrics. The novel multiscale health index was then constructed using the geodesic distance to quantify any deviation of the current state from the healthy baseline. As such, quantitative mapping between the stack voltage and internal indicators was established, enabling the internal states to be inferred from voltage inputs. The model achieved a normalized root mean squared error (NRMSE) &lt; 0.020 and a coefficient of determination (R²) &gt; 0.990 for degradation-indicator prediction, and the health state assessment attained an NRMSE of 0.009 and an R² of 0.998 against voltage-based benchmarks. Overall, the proposed method provides a solution for conducting a non-invasive assessment of the degradation of internal components within PEMFC systems.</p>\u0000 </div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 3","pages":"2997 - 3010"},"PeriodicalIF":2.6,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CTAB-regulated porous carbon embedded with Co nanoparticles promotes the adsorption and conversion of polysulfides in lithium–sulfur batteries","authors":"Zhiyuan Sun,&nbsp;Chengshuai Chang,&nbsp;Wan Zhang,&nbsp;Yunqiang Zhang,&nbsp;Laiying Jing,&nbsp;Qiuju Zheng,&nbsp;Hong Xiao,&nbsp;Mei Li,&nbsp;Yanfei Zhang","doi":"10.1007/s11581-025-06942-6","DOIUrl":"10.1007/s11581-025-06942-6","url":null,"abstract":"<div>\u0000 \u0000 <p>Lithium-sulfur batteries (LSBs) have garnered significant attention due to their high theoretical energy density and low cost. However, the shuttle effect of polysulfides and the low conductivity of the sulfur cathode severely limit their practical application. To address these challenges, a porous carbon framework embedded with cobalt nanoparticles (Co-SPC-CTAB) is successfully prepared by calcining zeolitic imidazolate framework-67 (ZIF-67)/polypyrrole Schiff base polymer under the regulation of cetyltrimethylammonium bromide (CTAB), and it is used as the interlayer material for LSBs. The metal Co derived from ZIF-67 provides abundant adsorption-catalytic active sites, effectively facilitating the adsorption and conversion of lithium polysulfides (LiPSs). In addition, CTAB not only optimizes the pore structure of the carbon skeleton to furnish abundant channels for Li⁺ transport and electrolyte infiltration, but also helps to improve the graphitization degree of the porous carbon framework, thereby further enhancing the electrochemical energy storage kinetics of LSBs. When Co-SPC-CTAB is utilized as the interlayer material, the assembled LSBs can achieve an initial specific capacity of 1063.6 mAh g^− 1 at 1 C and still retain 659.5 mAh g^− 1 after 500 cycles, with a capacity decay rate of only 0.076% per cycle. Under low-temperature conditions (-10 °C), the battery can exhibit a high initial specific capacity of 931.6 mAh g^− 1 at 0.2 C and remain at 724.8 mAh g^− 1 after 100 cycles. This study provides a novel strategy for the rational design of transition metal-carbon composite interlayer materials, advancing the development of high-stability LSBs.</p>\u0000 </div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 3","pages":"2763 - 2773"},"PeriodicalIF":2.6,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization and prediction of power density in proton exchange membrane fuel cells for green energy using advanced machine learning models: a comparative study","authors":"Kamil Kayode Katibi,&nbsp;Arun Kumar Shukla,&nbsp;Ibrahim Garba Shitu,&nbsp;Khalid M. Alotaibi,&nbsp;Ahamad Imran,&nbsp;Mubarak Olumide Mojoyinola,&nbsp;Abdul Azeez Olayiwola Sirajudeen","doi":"10.1007/s11581-025-06923-9","DOIUrl":"10.1007/s11581-025-06923-9","url":null,"abstract":"<div><p>This study presents an advanced methodology that integrates experimental validation with machine learning (ML) models to predict and optimize power density in proton exchange membrane fuel cells (PEMFCs). The models considered include Linear Regression (LR), Stepwise Linear Regression (SLR), Tree Regression (TR), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), Neural Networks (NN), Ensemble Learning (ENS), ElasticNet (EL), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). A high-precision experimental setup, employing Nafion 112 membranes, ultra-high-purity gases, and thoroughly controlled operational parameters, generated an extensive data set for model training. Model performance was carefully evaluated using key metrics, including Root Mean Square Error (RMSE), Mean Square Error (MSE), Coefficient of Determination (R²), and Mean Absolute Error (MAE). Among the models tested, GPR and NN demonstrated superior predictive accuracy (RMSE = 32.67 mW cm⁻²; R² = 0.96), capturing nonlinear dependencies in PEMFC dynamics. Residual analysis revealed the models’ ability to predict non-linear dependencies across mid-range operational conditions, while also identifying their limitations under extreme settings, such as high pressure or low current density. Unlike most PEMFC prediction studies that consider only current density and pressure, we explicitly model clamping line load <span>(:L)</span> across a wide operating envelope (5–15 N·cm^− 1; 5–25 bar). This reveals how compression co-governs gas diffusion and proton conductivity, enabling models that generalize across regimes where flooding, dehydration, and contact resistance jointly shape performance. By integrating data-driven and physics-informed approaches, this research yields nonlinear predictors that provide actionable compression set points to sustain high power density, mitigate degradation risks, and offer indispensable guidelines for designing efficient and robust PEMFC systems, thereby advancing the development of green energy technologies.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 3","pages":"3123 - 3144"},"PeriodicalIF":2.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of Mo-CoNiFe-S/NF and its outstanding electrocatalytic performance in the oxygen evolution reaction","authors":"Zekun Yun,&nbsp;Zhaoping Zhong,&nbsp;Renzhi Qi,&nbsp;You Jia,&nbsp;Huanqi Chen,&nbsp;Yuxuan Yang,&nbsp;Qihang Ye,&nbsp;Bohan Gu","doi":"10.1007/s11581-025-06935-5","DOIUrl":"10.1007/s11581-025-06935-5","url":null,"abstract":"<div>\u0000 \u0000 <p>The development of non-noble metal oxygen evolution reaction (OER) catalysts that combine low overpotential and long-term cycling stability at high current densities is key to overcoming the bottleneck in water electrolysis for hydrogen production technology. This work successfully prepared a molybdenum-doped cobalt-nickel-iron based sulfide catalyst (Mo-CoNiFe-S/NF) on nickel foam (NF) through a synergistic strategy involving two-step electrodeposition combined with hydrothermal sulfurization. This catalyst demonstrates outstanding OER catalytic performance in 1 mol/L KOH solution: it requires an overpotential of only 69.7 mV to reach a current density of 10 mA cm⁻², and a low overpotential of 346.3 mV at 100 mA cm⁻², with a Tafel slope as low as 37.3 mV dec⁻¹. These metrics are significantly superior to those of the molybdenum-undoped CoNiFe-S/NF and pure NF catalysts. This catalyst exhibits excellent potential for industrial application: operating continuously at a current density of 50 mA cm⁻² for 24 h, the current density decayed only from 50 mA cm⁻² to 48.8 mA cm⁻², representing a decay rate of merely 2.4%, while achieving a high Faraday efficiency of 95.36%. The doping with trace amounts of molybdenum and the sulfurization treatment enhance catalytic performance through multiple pathways: constructing a Ni₃S₂/(Co, Ni)₃S₄/FeS multiphase composite system to leverage component synergy; forming an interconnected network-like porous structure to expand the electrochemical active surface area (C_dl reaching 26.6 mF cm⁻²); and inducing electronic reconstruction to increase the electron density of active metal sites, thereby optimizing the adsorption-desorption kinetics of oxygen intermediates.</p>\u0000 </div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 2","pages":"1607 - 1622"},"PeriodicalIF":2.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modifications strategies for ABO3 class of perovskite materials for effective photocatalytic activity","authors":"Akshima Soni,&nbsp;Praveen K. Surolia,&nbsp;Dipti Vaya","doi":"10.1007/s11581-025-06941-7","DOIUrl":"10.1007/s11581-025-06941-7","url":null,"abstract":"<div><p>Due to urban expansion and rapid industrialization, water pollution significantly increases which gives necessity of efficient water purification technologies. ABO₃ pervoskites are good for the degradation of organic pollutants because of their unique photocatalytic properties. This review explores the modification strategies of ABO₃ perovskites, which focus on enhancing their photocatalytic activity for water treatment applications. It includes the advanced fabrication techniques, Site-Specific Doping At the A, B and O and the formation of nanocomposites to improve photogenerated charge Carrier Separation. The electronic and structural properties of ABO₃ perovskites have introduced the foreign ions at A and B sites. ABO₃ systems show significant performance gains, such as &gt; 90% degradation of Rhodamine B or methylene blue within 60–120 min and up to 2–3-fold increases in rate constants compared to pristine perovskites. Co-catalysts and the advanced synthesis improve the performance of these material and also emphasizes the critical roles of heterojunction formation and nanostructure design in intrinsic limitations. The development of efficient, scalable and eco-friendly photocatalytic systems that positioning ABO₃ pervoskite for next generation water purification technologies.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 3","pages":"2585 - 2610"},"PeriodicalIF":2.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147727311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Battery SOH estimation via an optimized CNN–BiLSTM–Attention network using ICA-Based ageing features","authors":"Zhiqiang Lyu,&nbsp;Hao Wang,&nbsp;Wenwu Shi,&nbsp;Xingzi Qiang,&nbsp;Longxing Wu","doi":"10.1007/s11581-025-06933-7","DOIUrl":"10.1007/s11581-025-06933-7","url":null,"abstract":"<div><p>Accurate estimation of lithium-ion battery State of Health (SOH) remains challenging because most existing methods rely on full charging cycles, are sensitive to noise and capacity regeneration, or require manual hyperparameter tuning that limits generalization across cells and datasets. To address these issues, this study proposes a hybrid CNN–BiLSTM–Attention framework optimized by a Genetic Grey Wolf Optimizer (GGWO) for SOH estimation using only two informative features extracted from partial charging data via Incremental Capacity Analysis. The CNN extracts local degradation patterns, the BiLSTM captures long-range temporal dependencies, and the attention mechanism adaptively emphasizes salient temporal information, while the GGWO automatically searches for optimal hyperparameters to improve robustness and accuracy. Extensive experiments on both public CALCE datasets and a private multi-cell LBP dataset demonstrate that the proposed model achieves superior estimation performance across varying temperatures and loading conditions. The GGWO-optimized model attains a minimum MAE of 0.42% and RMSE of 0.51%, consistently outperforming conventional machine learning baselines as well as the non-optimized CNN–BiLSTM–Attention model. These results confirm the model’s strong generalization capability and its suitability for real-time implementation in battery management systems.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 2","pages":"1771 - 1787"},"PeriodicalIF":2.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preparation of high-density lithium iron phosphate with Nb, Ti, V co-doping and non-uniform particle distribution","authors":"Hao Yang,&nbsp;Jianling Guo,&nbsp;Juanjuan Xue,&nbsp;Jingpeng Zhang,&nbsp;Guangchuan Liang,&nbsp;Yong Wang","doi":"10.1007/s11581-025-06915-9","DOIUrl":"10.1007/s11581-025-06915-9","url":null,"abstract":"<div><p>This study successfully prepared high-density LiFePO₄/C composite materials using a ternary co-doping strategy of Nb₂O₅, TiO₂, and V₂O₅, combined with particle size optimization and control techniques. The substitution of Nb⁵⁺ for Li⁺ positions can widen the diffusion channels for Li⁺ and enhance its diffusion kinetics. The replacement of Fe²⁺ by Ti⁴⁺ can stabilize the crystal structure, reduce volume changes during charge-discharge processes, and improve cycling stability. The substitution of Fe²⁺ by V^4⁺ and the introduction of electron defects can increase electronic conductivity. The synergistic co-doping of Nb⁵⁺, Ti⁴⁺, and V^4⁺ increased the electrical conductivity of the LFP material from 2.0 × 10^− 1 to 2.6 × 10^− 1 S cm^− 1 and the Li⁺ diffusion coefficient from 7.65 × 10^− 13 to 2.39 × 10^− 12 cm²s^− 1. Moreover, by optimizing the particle size distribution and adopting a non-uniform particle grading strategy, small particles can efficiently fill the gaps between large particles, reducing porosity and further increasing the compaction density. The final Li₀.₉₉₀Nb₀.₀₁₀Fe₀.₉₉₀Ti₀.₀₀₅V₀.₀₀₅PO₄ material achieved an industry-leading compaction density of 2.72 g cm^− 3 under a pressure of 226 MPa. Electrochemical tests showed that the discharge capacities at 0.2 C and 5 C rates were 166.2 mAh g^− 1 and 142.4 mAh g^− 1, respectively, and the capacity retention rate after 1000 cycles at 5 C was 91.34%. The assembled 14,500 cylindrical battery exhibited excellent volumetric energy density (0.2 C: 1166.27 Wh L^− 1, 1 C: 1109.62 Wh L^− 1). This research provides an effective material design strategy for the development of high-energy-density lithium iron phosphate batteries.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 2","pages":"1849 - 1862"},"PeriodicalIF":2.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-morphological carbon cross-linked composite enhances the high-rate performance and ultra-long cycling stability of Na3Fe2(PO4)(P2O7) cathode","authors":"Hang Song,&nbsp;Kaihua Liu,&nbsp;Yinghan Liu,&nbsp;Chuanlong Ji,&nbsp;Yuhao He,&nbsp;Keyan Bao,&nbsp;Wutao Mao","doi":"10.1007/s11581-025-06938-2","DOIUrl":"10.1007/s11581-025-06938-2","url":null,"abstract":"<div>\u0000 \u0000 <p>Sodium-ion batteries (SIBs) have emerged as a promising complement to lithium-ion counterparts, owing to their advantages of abundant resources, low cost, and high safety. Among the polyanion-type cathode materials, Na₃Fe₂(PO₄)(P₂O₇) (NFPP) has garnered significant attention due to its stable three-dimensional framework and environmentally friendly characteristics. However, its inherent low electronic conductivity has hindered practical application. This work presents a modification strategy to address this limitation. By constructing a three-dimensional continuous conductive network within the NFPP composite material (NFPP@C/CNT-rGO) through the integration of carbon nanotubes (CNT), reduced graphene oxide (rGO), and amorphous carbon coating, the electronic conductivity is significantly enhanced, and sodium-ion diffusion sites are optimized. Electrochemical evaluation demonstrates that the NFPP@C/CNT-rGO half-cell delivers a reversible capacity of 113.3 mAh g⁻¹ at 0.1 C, with a capacity retention rate of 70.7% after 6000 cycles at a high rate of 10 C, while maintaining a stable Coulombic efficiency close to 100%. Notably, at 20 C, the capacity reaches 70.2 mAh g⁻¹, far surpassing that of NFPP@C. Furthermore, the assembled NFPP@C/CNT-rGO || HC full cell exhibits a capacity retention rate of 78.4% after 300 cycles at 1 C, validating the material’s practical application potential. This study introduces a novel approach to enhance the performance of iron-based polyanion cathodes in sodium-ion batteries by constructing carbon conductive networks with multi-morphological structures, thereby paving the way for the practical application of sodium-ion batteries.</p>\u0000 </div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 2","pages":"1935 - 1947"},"PeriodicalIF":2.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"State evaluation of lithium-ion batteries in energy storage stations based on adaptive noise updating AEKF algorithm","authors":"Mingwan Zhuang,&nbsp;Jianzhong Tang,&nbsp;Junwei Ma,&nbsp;Guanhui Yin,&nbsp;Weirong Yang","doi":"10.1007/s11581-025-06902-0","DOIUrl":"10.1007/s11581-025-06902-0","url":null,"abstract":"<div><p> With the expansion of Energy Storage Power Stations (ESPS), the state assessment of Lithium-ion Batteries (LIBs) is crucial for system safety and efficiency. This study proposes a fusion algorithm combining adaptive extended Kalman filtering and particle swarm optimization to address traditional methods’ limitations in adapting to battery dynamic characteristics and reducing estimation errors. This algorithm dynamically adjusts the noise covariance matrix through an adaptive noise update mechanism, enhances the global search capability of particle swarm optimization, and makes the estimation results more accurate and reliable. Experiments showed the method’s loss values decreased to 0.1 and 0.06 across two datasets, with mean absolute errors in SOC estimation of only 0.98% and 0.62%. The identification error rapidly decreased with iterations, remaining between 0.2% and 0.3%. In practical applications, the method maintained battery SOC at 80%-90% under high-frequency low-power pulse conditions and long-term high-power continuous conditions with 4A current and approximately 1-second transient response. The designed state evaluation model effectively alleviates energy storage system pressure, reduces energy loss, and extends battery life, providing a new direction for LIBs state evaluation in ESPS and contributing to improved operational efficiency and safety.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"32 2","pages":"1789 - 1801"},"PeriodicalIF":2.6,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147338342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}