Ionics最新文献

{"title":"A deep neural network based battery state of charge: electric vehicle application","authors":"Radhia Jebahi,&nbsp;Nadia Chaker,&nbsp;Helmi Aloui","doi":"10.1007/s11581-025-06440-9","DOIUrl":"10.1007/s11581-025-06440-9","url":null,"abstract":"<div><p>Accurate State of Charge (SoC) estimation is critical for the performance, safety, and longevity of Li-ion batteries in electric vehicles (EVs). Traditional model-based approaches, such as equivalent circuit models and Kalman filters, often suffer from computational complexity and sensitivity to parameter variations, while data-driven methods face challenges in generalization due to limited training data or suboptimal algorithm selection. To address these limitations, this study proposes an intelligent SoC estimation process based on a deep neural network, which learns an algebraic expression describing the SoC evolution directly from voltage, current, and temperature measurements. A systematic comparative study evaluates three training algorithms Levenberg–Marquardt, Bayesian Regularization, and Conjugate Gradient under varying data splits to determine the optimal balance between precision and robustness. Results demonstrate that Bayesian Regularization achieves the highest accuracy when trained on 70% of the dataset, with 15% each for validation and testing, reducing the SoC prediction error to below 2%. This outcome not only validates the effectiveness of the proposed data-driven approach but also highlights the importance of algorithm and data split selection in overcoming the generalization challenges of existing methods. The study provides a practical and reliable solution for real-time EV battery management systems.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"7969 - 7986"},"PeriodicalIF":2.6,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161627","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":"Numerical modeling and investigation of foreign cation contamination on the performance of proton exchange membrane fuel cells","authors":"Shian Li,&nbsp;Li Zhang,&nbsp;Guogang Yang,&nbsp;Qiuwan Shen","doi":"10.1007/s11581-025-06432-9","DOIUrl":"10.1007/s11581-025-06432-9","url":null,"abstract":"<div><p>Proton exchange membrane fuel cells (PEMFCs) can be used as power sources in a wide variety of application scenarios due to the attractive advantages. Foreign cations introduced by air can cause a decrease in the output performance and durability of PEMFCs. In this study, a one-dimensional mathematical model is established to investigate the effect of foreign cation (Na⁺) contamination on the performance of PEMFCs. The performance degradation is observed due to the presence of Na⁺ and it becomes more severe with decreasing cell voltage. The maximum power density of the fuel cell without Na⁺ contamination (case 0) is 0.435 W/cm², while the maximum power densities of the fuel cells with Na⁺ contamination (case 1, case 2, case 3, and case 4) are 0.385 W/cm², 0.332 W/cm², 0.275 W/cm², and 0.206 W/cm², respectively. In addition, the local transport processes are strongly affected in PEMFCs due to the cation contamination.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"8211 - 8219"},"PeriodicalIF":2.6,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142238","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":"Eco-friendly synthesis of BiVO4 nanoparticles for efficient photocatalytic degradation and electrochemical sensing","authors":"Pushpa C. S.,&nbsp;Lakshmi Ranganatha V.,&nbsp;Soundarya T. L.,&nbsp;Pramila S.,&nbsp;Sangamesha M. A.,&nbsp;Mallikarjunaswamy C.","doi":"10.1007/s11581-025-06394-y","DOIUrl":"10.1007/s11581-025-06394-y","url":null,"abstract":"<div><p>The development of high-performance photocatalysts and electrocatalysts has rapidly emerged as one of the most dynamic and cutting-edge areas of scientific research today. This study introduces a novel, simple, eco-friendly approach to synthesize Bismuth Vanadate (BiVO₄) nanoparticles (NPs) using <i>Glossocardia bosvallia</i> leaf extract as a natural fuel source for the first time. The fuel plays a crucial role in controlling the size of the NPs, with Scherrer’s analysis revealing an average Np’s (BVG3) size of 20 nm. These NPs exhibited a band gap of approximately 2.8 eV, indicating their activity under visible light. When exposed to visible light, the crystalline surface and optimal bandgap of the BiVO₄ NPs facilitated the effective degradation of methylene blue (MB). The degradation efficiency of MB dye using BiVO₄ NPs reached up to 98.2% within 180 minutes. Furthermore, recycling experiments demonstrated the excellent photostability of BiVO₄ NPs. Additionally, BiVO₄ NPs exhibited remarkable bio-analyte sensing capabilities by showing distinct oxidation and reduction peaks towards paracetamol, and they also demonstrated a good response in linear sweep voltammetry studies. Electrochemical impedance spectroscopy (EIS) provided valuable insights into the ionic conductivity of a substance and the capacitive behaviour of the BiVO₄-modified electrode.</p><h3>Graphical abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"8263 - 8280"},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142027","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":"Layered perovskite SrLaAlO4 as a proton-conducting material for intermediate-temperature solid oxide fuel cells: synthesis, hydration, and electrical properties","authors":"Danil Vasin,&nbsp;Natalia Lakiza,&nbsp;Irina Animitsa","doi":"10.1007/s11581-025-06434-7","DOIUrl":"10.1007/s11581-025-06434-7","url":null,"abstract":"<div><p>The work aims to investigate the layered perovskite SrLaAlO₄ with the Ruddlesden-Popper structure as a potential proton conductor for medium-temperature solid oxide fuel cells (SOFCs). The sample was synthesized using the glycerol-nitrate method with subsequent calcination of the sample at 1200 °C for 24 h. Electrical properties were studied in dry and humid atmospheres to confirm the presence of proton conductivity, which dominates at temperatures below 500 °C. The phase SrLaAlO₄ is stable over a wide range of partial pressures of oxygen and at high partial pressures of water vapor. The degree of hydration reaches 0.16 mol H₂O, which corresponds to the composition SrLaAlO_3.84(OH)_0.32. IR studies confirmed the presence of different types of OH⁻-groups participating in hydrogen bonds of varying strengths.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"8173 - 8183"},"PeriodicalIF":2.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145142025","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":"Joint estimation of state of charge and state of energy for lithium-ion batteries based on the iTransformer deep learning network","authors":"Shanshan Wang,&nbsp;Hao Zhang,&nbsp;Wenkang Han,&nbsp;Qicai Yin,&nbsp;Liang Zeng","doi":"10.1007/s11581-025-06424-9","DOIUrl":"10.1007/s11581-025-06424-9","url":null,"abstract":"<div><p>Lithium-ion batteries are the core energy storage devices in electric vehicles. Accurate estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is critical to ensure their safe and healthy operation. Most existing methods focus on state estimation under constant temperatures. However, during actual charging and discharging processes, battery temperature continuously fluctuates. To address the challenge of accurately estimating SOC and SOE under varying environmental temperatures (such as high and low temperatures), we propose a deep learning network model based on the Inverted Transformer (iTransformer), named iTransformer-SQL-AdamP. During training, the smoothed quadratic loss (SQL) function is incorporated to dynamically adjust the gradient, reducing noise interference, while the Adaptive Moment Estimation with Projection (AdamP) optimizer is employed to further enhance estimation accuracy. SOC and SOE estimations were conducted under different conditions at − 20 °C, − 10 °C, 0 °C, 10 °C, 25 °C, and 45 °C to validate the model’s efficacy. In the LA92 cycle at 0 °C, the lowest MAE for SOC and SOE were 0.416% and 0.395%, respectively, with <i>R</i>² coefficients approaching 1, demonstrating significant estimation accuracy. The predictive validation results show that the proposed network exhibits strong generalization ability, high estimation accuracy, and robustness. Therefore, this network provides a novel method for joint estimation of battery SOC and SOE across a wide range of temperatures, offering excellent predictive performance.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"7821 - 7836"},"PeriodicalIF":2.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171215","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":"Ru nanoclusters supported on doped carbon nanotubes for efficient alkaline hydrogen evolution: metal-support interactions modulated by different heteroatoms","authors":"Wenke Liu,&nbsp;Ziqi Meng,&nbsp;Bo Li,&nbsp;Jing Chen,&nbsp;Mengran Liu,&nbsp;Jiaxun Feng,&nbsp;Dongjin Wan,&nbsp;Xuzhuo Sun","doi":"10.1007/s11581-025-06429-4","DOIUrl":"10.1007/s11581-025-06429-4","url":null,"abstract":"<div><p>Metal-support interaction is vital of modulating electronic structure of metal nanoclusters as well as stabilizing these nanoclusters. In this study, a series of Ru-based catalysts supported on CNT doped with diverse nonmetallic elements, including O, N, and S (denoted as Ru/x-CNT), are delicately designed to investigate the relationship of metal-support interaction and HER activities. Compared with N-CNT and S-CNT, O-CNT-supported Ru NCs manifest the stronger metal-support interactions due to its high charge transfer between Ru and substrate, which exhibits a remarkable low overpotential of 10 mV at 10 mA cm⁻² and excellent stability at 100 mA cm⁻². This work confirms that enhanced metal-support interactions are benefited to improve both HER activity and stability.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"8235 - 8243"},"PeriodicalIF":2.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145749","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":"Enhancing lithium-ion battery performance through novel spinel/layered heterostructured cathode design: a systematic investigation of xLi₄Mn₅O₁₂·(1-x)Li₁.₂Mn₀.₅Ni₀.₂Co₀.₁O₂","authors":"Amer Abdulabbas Sakran,&nbsp;Hadi Arabi,&nbsp;Shaban Reza Ghorbani,&nbsp;Nasrin Azad","doi":"10.1007/s11581-025-06381-3","DOIUrl":"10.1007/s11581-025-06381-3","url":null,"abstract":"<div><p>This study presents the development and characterization of a novel spinel/layered heterostructured cathode material designed for enhanced energy storage capacity of lithium-ion batteries. The investigation began with the synthesis of Li-rich layered (Li_1.2Mn_0.5Ni_0.2Co_0.1O₂) and spinel (Li₄Mn₅O₁₂₎ cathode materials via a modified sol–gel method. These materials were then integrated to create heterostructured cathodes with compositions of xLi₄Mn₅O₁₂·(1-x)Li_1.2Mn_0.5Ni_0.2Co_0.1O₂ (x = 0.01, 0.03, 0.05, and 0.07). Comprehensive structural characterization using X-ray diffraction (XRD), Raman spectroscopy, and high-resolution transmission electron microscopy (HR-TEM) confirmed the successful formation of the spinel/layered heterostructure. X-ray photoelectron spectroscopy validated the presence of the Li₄Mn₅O₁₂ spinel structure within the heterostructure matrix and verified the valence states of transition metal ions. The electrochemical performance of the Li-rich layered and heterostructure cathode materials was assessed through various measurements, including galvanostatic charge–discharge at different C rates, cyclic voltammetry, differential capacity, and electrochemical impedance spectroscopy. Electrochemical performance evaluation revealed that the optimized composition (x = 0.01) exhibited superior performance metrics, delivering specific capacities of 299.38, 231.48, 205.03, 174.93, and 115.67 mAh g⁻¹ at rates of 0.1C, 0.5C, 1C, 2C, and 5C, respectively. Notably, this composition maintained a stable capacity of 175.3 mAh g⁻¹ after 100 cycles at 1C rate, representing a 76.89% capacity retention. The enhanced performance is attributed to the synergistic effect of the ultrathin spinel layer, which facilitates Li-ion diffusion kinetics while protecting the layered structure from electrolyte degradation.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"7785 - 7802"},"PeriodicalIF":2.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171216","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":"Electrosynthesis/chemical in situ growth of the LDH/MOFs nanocomposite at the surface of carbon ceramic electrode for methanol oxidation reaction","authors":"Biuck Habibi,&nbsp;Ali Pashazadeh,&nbsp;Sara Pashazadeh","doi":"10.1007/s11581-025-06391-1","DOIUrl":"10.1007/s11581-025-06391-1","url":null,"abstract":"<div><p>In this study, we developed a nanocomposite of Ni/Zn/Al-layered double hydroxide (LDH) and metal–organic frameworks (MOFs) through the electrochemical deposition of the Ni/Al LDH at the surface of the carbon ceramic electrode (CCE) and chemical in situ growth of Ni/Zn/Al LDH-MOF nanocomposite under mild conditions. The Ni/Zn/Al LDH-MOF nanocomposite modified CCE was analyzed using a range of instrumental methods. Then, the Ni/Zn/Al LDH-MOFs/CCE was employed for the electrocatalytic oxidation of methanol in alkaline solution. The Ni/Zn/Al LDH-MOFs/CCE shows enhanced electrocatalytic activity in 0.1 M sodium hydroxide toward the oxidation of methanol, with anodic peak current density proportional to methanol concentration. In continuation of electrochemical studies, the electron transfer coefficient (<i>α</i>) and the catalytic rate constant (<i>k</i>_cat) for the methanol oxidation reaction at the Ni/Zn/Al LDH-MOF/CCE were measured: <i>α</i> = 0.35 and <i>k</i>_cat = 6.77 × 10⁴ cm³ mol⁻¹ s⁻¹. The pseudo steady-state polarization method was employed to investigate the electrocatalytic oxidation of methanol at the Ni/Zn/Al LDH-MOFs/CCE, as well as to determine some electrooxidation reaction parameters. The obtained values include the following: <i>k</i>₂Г^* = 1.87 × 10⁻⁹ cm s⁻¹ and the ratio of <i>k</i>⁰₋₁/<i>k</i>⁰₁ = 1.8 × 10⁷. These findings indicated that the incorporation of the Zn ions and MOFs into the Ni/Al LDH and the construction of the nanocomposite significantly enhanced the electrocatalytic performance of the resulting electrocatalyst, paving the way for the development of a highly efficient anodic material for direct methanol fuel cell applications.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"8245 - 8261"},"PeriodicalIF":2.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145145750","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":"Estimation of state of charge for a lithium-ion battery pack using nonlinear Kalman filters","authors":"Shivanshu Kumar,&nbsp;Saikat Mondal,&nbsp;Amalendu Bikash Choudhury,&nbsp;Himadri Sekhar Bhattacharyya,&nbsp;Chandan Kumar Chanda","doi":"10.1007/s11581-025-06420-z","DOIUrl":"10.1007/s11581-025-06420-z","url":null,"abstract":"<div><p>Estimating the state of charge (SOC) of a lithium-ion battery (LiB) pack is challenging due to the inherent variability across individual battery cells. This study uses a hardware configuration comprising a 13s10p battery pack, a switched-mode power supply (SMPS), a brushless direct current motor (BLDC) as a load, and a charger to charge and discharge the battery pack for gathering the real-time data. The data is subsequently fed into the simulation model, which estimate the SOC for a 2 RC model at temperatures 288 K, 298 K, and 318 K. Several nonlinear Kalman filter (KF) techniques, such as the extended Kalman filter method (EKF), the unscented Kalman filter method (UKF), extended Kalman-Bucy filter method (EKBF), and the unscented Kalman-Bucy filter method (UKBF), are utilized in estimating SOC. The UKBF and EKBF provide the most accurate estimation for SOC, with an overall root mean square error (RMSE) of less than 1% and 1.5%, respectively, while the mean absolute percentage error (MAPE) is below 1.5% and 3% for the 2 RC model across all temperatures.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"7953 - 7968"},"PeriodicalIF":2.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171214","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":"Co-estimation of state of charge and state of health of sodium-ion batteries based on fractional-order model and improved double unscented Kalman filter","authors":"Jialian Chen,&nbsp;Zhipei Xu,&nbsp;Xu Qin,&nbsp;Fumin Zou,&nbsp;Xinjian Cai","doi":"10.1007/s11581-025-06425-8","DOIUrl":"10.1007/s11581-025-06425-8","url":null,"abstract":"<div><p>Sodium-ion batteries (SIBs) are projected to become a commercially viable alternative to lithium-ion batteries in the future because of their abundant reserves, high energy density, and enhanced safety. Accurate estimation of the state of charge (SOC) and state of health (SOH) is crucial for ensuring safe battery operation, prolonging lifespan, and optimizing energy management in SIBs. This study proposes a collaborative estimation method for battery SOC and SOH based on the FO-MISVDRUKF-UKF algorithm. First, a fractional-order model (FOM) is adopted to characterize the complex ion dynamics in SIBs, achieving terminal voltage prediction errors within 0.08 V. Secondly, to address the limitations of conventional unscented Kalman filter (UKF) algorithms—including low precision, computational complexity, and weak robustness—three key enhancements are implemented: (1) Replacing Cholesky decomposition with singular value decomposition (SVD) ensures algorithm stability when the covariance matrix <i>P</i> lacks positive semi-definiteness; (2) Integration of H-infinity filtering effectively suppresses unknown noise interference; (3) Multi-innovation (MI) theory leverages historical data to further improve estimation accuracy. Furthermore, real-time parameter updating and SOH monitoring are achieved through recursive UKF adaptation, mitigating model parameter drift effects on SOC estimation. Experimental validation under varying temperatures and dynamic load conditions demonstrates the superior performance of the proposed algorithm. At temperatures of 25 °C, 45 °C, and 60 °C, SOC estimation errors remain below 0.34% (mean) and 0.75% (maximum), while SOH errors are constrained within 0.29% (mean) and 0.58% (maximum)—significantly outperforming conventional methods. These results confirm the high accuracy and robust performance of the proposed framework. </p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 8","pages":"7987 - 8003"},"PeriodicalIF":2.6,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145171217","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}