Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is a critical task in battery management systems. To address the nonlinearity and nonstationarity of raw capacity-fading data, as well as the fluctuation disturbances induced by localized capacity regeneration, this paper proposes a hybrid prediction method that combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a bidirectional long short-term memory neural network (Bi-LSTM). The proposed method first decomposes the battery capacity sequence using CEEMDAN and then separates the resulting modal components into high-frequency and low-frequency parts according to the zero-crossing rate (ZCR) criterion. Pearson and Spearman correlation analyses are further performed to verify that the low-frequency (LF) component is representative of the overall degradation trend. Subsequently, under the offline prediction setting, baseline models including LSTM, Bi-RNN, and Bi-LSTM are compared to evaluate the effectiveness of the CEEMDAN–Bi-LSTM strategy. For the online prediction setting, a multi-input multi-output (MIMO) rolling multi-step ahead forecasting strategy is adopted, where the low-frequency component (Bi-LSTM-1) is used as the primary prediction target to enhance rolling stability and improve cycle estimation near the failure threshold. Finally, extended experiments are conducted on the NASA 18650 dataset (B05/B06) to validate the applicability of the proposed method across different battery samples. Experimental results show that the CEEMDAN–Bi-LSTM approach can track the capacity-fading trend more stably under different prediction starting points in online prediction, and achieves favorable performance in both offline and online tasks.

Sodium-ion batteries (SIBs), owing to their abundant resources, low cost, and superior low-temperature performance, show great potential for applications in energy storage systems and electric vehicles. Accurate state of charge (SOC) estimation, a critical function of battery management systems (BMS), is essential for ensuring operational safety and optimizing efficiency. However, existing SOC estimation methods for SIBs often face challenges including limited accuracy, poor wide-temperature adaptability, high computational demands, or insufficient mechanistic clarity. To overcome these issues, this study proposes a hybrid SOC estimation method integrating an Improved Gas-Liquid Dynamic (IGLD) model with the Cubature Kalman Filter (CKF). The IGLD model refines the original framework by introducing a temperature-dependent correction mechanism that dynamically maps battery capacity and internal resistance to temperature variations, thereby enhancing characterization across wide temperature ranges. Comprehensive validation was conducted under multiple temperatures (-20 degrees C to 45 degrees C) and dynamic driving cycles (DST, FUDS, UDDS, CLTC). Results indicate that the IGLD model reduces root mean square error (RMSE) and mean absolute error (MAE) to within 1.8 %, outperforming the original model. Further integration with CKF improves robustness: the IGLD-CKF method achieves RMSE and MAE below 1 %, converges within 35 iterations even with 100 % initial SOC error, and maintains errors under 1 % under strong noise interference (+10 mV voltage, +100 mA current). These results confirm the method's high accuracy, strong robustness, and excellent temperature adaptability, offering a reliable technical solution for the safe and efficient deployment of SIBs in real-world dynamic scenarios.

Accurate estimation of the state of charge (SOC) is a critical technological component for ensuring the performance and safe operation of power batteries. In practical applications, individual cells are configured into parallel battery module (PBM), series battery module (SBM), and series-parallel battery module (SPBM) through various topological arrangements such as parallel, series, and series-parallel connections. Owing to the inconsistencies among individual cells, conventional single-cell SOC estimation methods exhibit limited applicability when extended to battery modules. This paper introduces a gated recurrent unit based on physical loss integrated with unscented Kalman filter (PLGRU-UKF), aiming to achieve robust and accurate estimation of the extremal SOC values within battery modules. To validate the effectiveness of the proposed method, a comprehensive experimental framework is established, comprising both single-cell and battery module testing platforms. These platforms are utilized to select appropriate cells for module assembly and to construct multi-condition datasets for PBM, SBM and SPBM. Subsequent analyses evaluate cell inconsistency and the estimation accuracy under different connection topologies and varying numbers of cells. The results show that the proposed method maintains RMSE within 1.3 % across all datasets. In addition, the method demonstrates strong robustness to measurement noise, confirming its effectiveness and reliability in practical applications.

Iridium oxide (IrO2) is widely regarded as the most active and stable oxygen evolution reaction (OER) catalyst applied in proton exchange membrane water electrolysis (PEMWE). However, the scarcity and high cost substantially hinder its large-scale deployment. In contrast, RuO2 exhibits superior catalytic activity but suffers from insufficient stability under commercially relevant operating conditions. This review provides a critical overview of recent advances in RuO2-based catalysts and their degradation behavior in PEM electrolyzers. Degradation mechanisms are analyzed across multiple length scales, ranging from atomic-scale processes to macroscopic degradation phenomena, including the lattice oxygen oxidation mechanism (LOM), electrochemical dissolution, catalyst layer detachment, and porous transport layer deterioration. In addition, we assess emerging strategies to improve catalyst durability, with particular emphasis on approaches that mitigate bubble-induced catalyst detachment at high current densities and performance loss during dynamic load cycling. Ultimately, this work seeks to bridge fundamental mechanistic understanding and practical design principles for the development of robust Ru-based electrocatalysts.

Sodium superionic conductor structure (NASICON) structured phosphates have attracted much attention due to their stable three-dimensional open framework structure, excellent thermal stability, and outstanding ion conductivity. Traditional NASICON structured phosphates such as Na3V2(PO4)3 generally face the problem of low energy density and high cost. In this paper, low-cost Mn and Ti are introduced into Na3V2(PO4)3 in equal proportions to achieve a novel NASICON structured Na3VMn0.5Ti0.5(PO4)3 with high operating voltage. Organic phosphoric acid and citric acid are used to construct in-situ carbon coating to enhance the electronic conductivity of the material. Na3VMn0.5Ti0.5(PO4)3/C particles are uniformly embedded in the carbon chain network. Thanks to the synergistic effect of various ions and multi-level carbon modification, Na3VMn0.5Ti0.5(PO4)3/C prepared at 650 °C shows excellent electrochemical performance. It delivers a high specific capacity of 121.0 mA h g−1 at 1C rate, and retains 87.7 mA h g-1 after 1000 cycles. The high-rate charge-discharge at 10C did not cause irreversible capacity degradation. This new NASICON structured phosphate holds great potential for the development of sodium-ion batteries.

The excellent performance of lithium-ion batteries has led to their widespread commercial application across various industries. However, the continuous depletion of lithium resources poses significant limitations for their use in large-scale energy storage. Due to the low cost and high natural abundance of sodium, sodium-ion batteries have emerged as a promising alternative to lithium-ion batteries. Consequently, the development of novel electrode materials with high specific capacity and robust cycling stability has become a major research focus for sodium-ion batteries. In this work, we select three-dimensional (3D) graphene and its composites with iron phosphide as the target materials to develop high-performance anode materials for sodium-ion batteries. Specifically, we construct a dual-carbon (graphene and carbon nanotubes) modified FeP composite. The threedimensional graphene framework accelerates electron and sodium-ion transport while enhancing the structural stability of the material during charge-discharge cycling. A three-dimensional composite structure of FeP with dual-carbon materials (graphene and carbon nanotubes) was successfully fabricated. The resulting architecture effectively suppresses the volume expansion of FeP during charge-discharge cycles, and enhances its structural stability and electrical conductivity. At a current density of 0.05 A g-1, the composite exhibits a high reversible discharge specific capacity of 466.2 mAh g-1. Furthermore, after 1000 cycles at a high current of 1 A g-1, a specific capacity of 325.6 mAh g-1 is retained, demonstrating outstanding cycling stability and rate capability.

Herein, a versatile amorphous-to-crystalline transformation (ACT) strategy is described, to furnish various metals (Cu, Co, Ni, Cs, and Y) based atomically dispersed catalysts (ADCs) on thin holey 2D Al2O3. This approach, which involves adjusting reactant stoichiometry, enables the continuous modulation of particle size of ADCs at an atomical level, giving the ultrafine products as single atom, cluster, or nanoparticle catalysts. This synthesis method allows a straightforward analysis and comparison of the reactivity of the ADCs catalysts based on the dispersion of the active metal. In the case of Cu, the cluster catalyst 0.2-Cu/Al2O3 outperforms the single atom catalyst 0.1-Cu/Al2O3 and the nanoparticle catalyst 0.3-Cu/Al2O3 in the oxidation of refractory organic molecules, thanks to its superior electron transport and surface adsorption properties. These findings are well supported by the Density functional theory (DFT) calculations. Additionally, this method facilitates the preparation of atomic clusters with a very small size of 0.5-1 nm, composed of just a few atoms, as exemplified by Ni-based ADCs. The developed synthesis enriches the library of ADCs and demonstrates the potential of amorphous-to-crystalline transformation in creating advanced ultrasmall functional materials.

Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. This method optimizes the BP parameters using the improved HHO algorithm. At first, the circle chaotic mapping method is utilized to solve the problem of the initial value. Considering the problem of local convergence, Gaussian mutation is introduced to improve the search ability of the algorithm. Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. The model is validated using aging data from commercial lithium iron phosphate (LiFePO4) batteries. Finally, the model is thoroughly verified under an aging test. Experimental validation using training sets comprising 50%, 60%, and 70% of the cycle data demonstrates superior predictive performance, with mean absolute error (MAE) values below 0.012, root mean square error (RMSE) values below 0.017 and mean absolute percentage error (MAPE) within 0.95%. The results indicate that the model significantly improves prediction accuracy, robustness and searchability.

This paper proposes a state of charge/state of power (SOC/SOP) estimation method based on an improved gas liquid dynamic (GLD) model. The study enhances the GLD model from the perspective of battery polarization reaction, which improves the accuracy of the cell model compared to the original model. Additionally, a dual unscented Kalman filter (DUKF) algorithm is introduced to simultaneously achieve online parameters identification and SOC estimation. Based on the obtained SOC estimates, an SOP estimation method constrained by SOC, open circuit voltage (OCV), and current is proposed. Finally, the DUKF algorithm was validated under three driving cycles using a 3 Ah ternary lithium-ion cell. The maximum error in SOC estimation was <3 %, demonstrating the strong robustness of the algorithm. The proposed SOP estimation method was verified under dynamic stress test (DST) cycle, proving its effectiveness in preventing overcharging and overdischarging of the battery.

Activating the lattice oxygen of catalysts can accelerate the oxygen evolution reaction. However, a fundamental understanding of the lattice oxygen dynamics remains insufficient, which ultimately impairs catalyst development. Herein, we show that a CO32–containing electrolyte can substantially alter the reactivity and redox stability of lattice oxygens. In particular, for CoOOH and NiCoOOH, which feature high lattice oxygen reactivity, higher degrees of CO32- intercalation deactivate lattice oxygen, shifting the reaction pathway from the lattice oxygen mechanism to the adsorbate evolution mechanism. Operando spectroscopic and spectrometric analyses coupled with 18O isotopic labeling corroborate the decreased metal‒oxygen bond covalency and hindered lattice oxygen release caused by the intercalation of CO32-. Importantly, the catalysts with a fine-tuned degree of CO32- intercalation maintain high activity and stability owing to the dynamic equilibrium between lattice oxygen release and refilling, demonstrating negligible degradation in an alkaline water electrolyzer after 5000 h of operation at 0.5 A cm-2. This work reveals the intricacy of lattice oxygen dynamics, offering opportunities for designing high-performance electrocatalysts for real-life applications. The dynamics of lattice oxygen in oxyhydroxide catalysts is crucial for water oxidation but are not fully understood. Here, the authors report that carbonate ions in the electrolyte can substantially alter the dynamics, leading to robust long-term stability with sustained lattice oxygen reactivity.

Lithium metal batteries, with high theoretical specific capacity, hold promise as next-generation high-energy-density batteries; however, challenges like lithium dendrite formation and volume expansion adversely affects their cycle life and safety. To address the problems of lithium dendrite growth and interface instability in lithium metal batteries, a novel Ni@Ni2P-Li composite anode was proposed, which was prepared by physical compression and sodium hypophosphite-induced phosphating of nickel foam. The in-situ formed Ni2P coating significantly enhanced the lithium affinity of the nickel foam substrate, making lithium deposition uniform and effectively inhibiting the formation of dendrites. Compared with the conventional Ni-Li anode, the Ni@Ni2P-Li composite exhibited excellent electrochemical stability, achieving ultra-low overpotential (<29.9 mV) and stable cycling for more than 3000 h in symmetric cells. Full cell tests of NCM622 cathode further demonstrated the excellent rate capability and cycling durability. The hierarchical three-dimensional porous structure and the lithophilic Ni2P interface synergistically reduced the local current density and mitigated the volume fluctuation. This study provides new ideas and methods for the further development of lithium metal anodes.

Accurate estimation of the State of Charge (SOC) is fundamental to ensure the safe and reliable operation of lithium-ion batteries, and developing a high-performance battery model is crucial for achieving precise SOC estimation. However, current lithium-ion battery models struggle to balance accuracy and complexity. Against this background, on the basis of further investigating battery polarization phenomena and taking temperature effects into account, this paper constructs an improved gas-liquid dynamics model with temperature parameters and proposes an online SOC estimation method based on this model. The model offers good accuracy, and its voltage equation is only first-order, resulting in low computational cost. This model can more perfectly simulate the voltage rebound characteristics exhibited by the battery as a result of polarization reactions and takes into account the influence of temperature on SOC estimation. Compared with the original gas-liquid dynamics model, this enhanced model improves SOC estimation accuracy. Additionally, the unscented Kalman filter algorithm is introduced to achieve online SOC estimation. Finally, a 3Ah ternary lithium battery is tested under three driving cycles to validate the proposed model and algorithm. The results show that the maximum SOC estimation error is less than 2 % in all cases. When facing initial SOC errors and initial temperature errors, the method rapidly eliminates the impact of initial errors, and the influence of initial errors on subsequent SOC estimation results is minimal, demonstrating strong robustness of the proposed algorithm.

Sodium-ion batteries (SIBs), characterized by abundant raw material reserves and low material costs, have emerged as a promising alternative for energy-storage applications. Precise estimation of the state of charge (SOC) is a critical prerequisite for an effective battery management system (BMS). However, the distinct electrochemical reaction kinetics of SIBs preclude direct application of SOC estimation models originally designed for lithium-ion batteries. To address this challenge, a novel SOC estimation strategy is proposed, combining a gated recurrent unit (GRU) with an adaptive unscented Kalman filter (AUKF). Experimental data collected under CLTC, DST and FUDS three conditions across -20 degrees C, -10 degrees C, 0 degrees C, 10 degrees C, 25 degrees C and 40 degrees C six temperature settings validate the method's effectiveness. The mean absolute error (MAE) and root mean square error (RMSE) of the estimation results at 25 degrees C are limited to 0.61 % and 0.74 %, respectively. Notably, even at -20 degrees C and -10 degrees C, the MAE and RMSE remain within 1.39 % and 1.86 %, demonstrating the model maintains acceptable accuracy despite harsh low-temperature environments. Furthermore, initial error and sampling noise are added to demonstrate the robustness of the model. The excellent convergence speed and interference resistance of the model show the potential of practical engineering application.

The study of battery health is closely related to battery safety. Accurately describing the status changes inside the battery can effectively prevent battery safety problems. In this paper, a novel method based on internal battery stress is proposed to improve the accuracy of health status assessment of lithium batteries. The dynamic equilibrium relationship of microscopic stress in the battery is obtained through the material transfer relationship and particle diffusion equation inside the battery material particles. Based on the mapping relationship between fatigue strength and stress of battery materials, the evaluation and prediction of battery health status are completed. The validity of the model is verified by the measured lithium battery data. The results show that the maximum stresses are 9.5/12/16 MPa respectively in 5 %/50 %/100 % DOD cycles. When the battery is in a static state, the initial stress is - 0.5Mpa caused by structural differences in different areas. The model can track the cycle life of the battery well, and the overall prediction error is kept within 3.5 %.The method demonstrates high performance.

Phase change materials, as a thermal management cooling method for batteries, have the problem of hightemperature heat dissipation failure under high rate operating conditions. This paper proposes a thermal management structure for battery modules that combines composite phase change materials (PCM) with liquid cooling to solve the heat dissipation limitations of passive cooling systems under extreme conditions. The specific structural design is analyzed and determined, and the system control strategy is optimized based on this structure. Firstly, the parameters of the liquid cooling system structure for the battery module are studied, which helps define the thermal management structure of the module. Then, the intervention conditions of the liquid cooling system are optimized to improve the utilization of the PCM. Finally, the heat dissipation performance of the thermal management structure under various configurations is evaluated. The results show that the setup of liquid cooling pipes and coolant can effectively reduce the temperature rise of the battery module. Compared to a system using only PCM, the temperature rise is reduced by 7 K. By adjusting the intervention time of the liquid cooling system, the utilization of PCM in the composite system is improved, with the liquid phase ratio of the PCM increasing by 35 %. In the optimized system, the module temperature can be controlled below 45 degrees C under 2C conditions, and the maximum liquid phase ratio of the PCM increases to 60 %. Under 3C conditions, the module temperature can be maintained within an appropriate operating range while ensuring the effective use of PCM.

The design of liquid cooling plate structure is an important research direction for the liquid cooling system of lithium-ion batteries. In recent years, structural optimization techniques have been increasingly applied to the design of liquid cooling plates. Among them, topology optimization is a systematic method for obtaining the optimal material distribution, which can automatically form optimized cooling channels based on the objective function. This article uses a density model-based topology optimization method to calculate the channel structure of liquid cooling plates, analyzes the influence of initial model parameters, and then establishes a computational fluid dynamics simulation model to compare and analyze the performance differences between topological structures and rectangular liquid cooling plates in temperature distribution, velocity distribution, and inlet and outlet pressure drop. Finally, the accuracy of numerical calculation results is verified through an experimental platform. The comprehensive results show that the topology structure with initial values of q = 0.01,theta 0 = 0.5 andRe = 400 has better performance. Compared to traditional rectangular structures, the maximum temperature of topological structures has decreased by 1.62 K, reducing the proportion by about 0.53 %, and the temperature difference has decreased by 0.55 K, reducing the proportion by about 11.55 %. The maximum discrepancy between simulation and experimental measurements remains below 2.4 K, with a relative error under 1 %, both falling within acceptable tolerance thresholds. These findings validate the precision and reliability of the numerical simulation model outcomes.

Lithium metal anode (LMA), with ultrahigh theoretical specific capacity (3860 mAh g-1) and low redox potential (-3.04 V vs. SHE), is regarded as the "holy grail" for next-generation high-energy-density batteries. However, uncontrollable lithium dendrite growth leads to poor cycling stability, low Coulombic efficiency, and safety risks, hindering its commercialization. This study proposes a novel Ag-C3N4.5 composite nanoribbon-coated separator (Ag-C3N4.5@PP) to synergistically regulate lithium deposition through material design and interface engineering. The high lithiophilicity of Ag nanoparticles induces uniform nucleation, while the layered C3N4.5 structure suppresses dendrite tip effects. The nanoribbon morphology endows the separator with superior electrolyte wettability. Experiments demonstrate that the Ag-C3N4.5@PP separator enables stable cycling for 1200 h (3000 cycles) at 5 mA cm-2 with a low overpotential of 35 mV in symmetric cells. The introduced Ag significantly reduces internal resistance and energy loss. Both symmetric cells and Li//Cu asymmetric cells exhibit exceptional performance, validating the feasibility of lithiophilic metal ion loading strategies.

High-capacity metal sulfides have been extensively investigated as cathode materials for secondary batteries. Despite their high capacity, the redox potential of metal sulfides is limited to 2.7 V. Therefore, researchers explored its application as a negative electrode material and discovered its exceptional electrochemical performance. However, the conventional methods for synthesizing transition metal sulfides (TMS) inevitably require an external source of sulfur for secondary sulfidation, resulting in energy dissipation. In this study, L-cystine was adopted as a polydentate ligand for the synthesis of MxSy@N, SC metal-organic frameworks, which can confine the desired carbon, nitrogen, and sulfur sources within the framework. This approach facilitates one-step sintering to obtain nitrogen-doped transition metal sulfides. The structure composition and electrochemical reaction mechanism of FeS@N,S-C as the negative electrode in Na-ion batteries were studied through scanning electron microscopy (SEM)/transmission electron microscopy (TEM) and X-ray diffraction (XRD)/X-ray photoelectron spectroscopy (XPS) analysis. The electrochemical properties of FeS@N,S-C materials were test as an example. The FeS@N, SC composite exhibited a reversible Na+ storage capacity of 724.4 mAh g(-1) at a current density of 200 mA g(-1), and still maintained a reversible capacity of 600.7 mAh g(-1) at a current density of 2000 mA g(-1), demonstrating excellent rate performance. After 200 cycles, the material was still able to retain 97.3 % of the discharge specific capacity, demonstrating fine cycle performance. It shows that the FeS@N, SC composite has rapid electron conduction ability and high sodium ion diffusion coefficient. At the same time, a rapidly charge-discharge process can be achieved with a large capacitive contribution, indicating the great potential of this material as a sodium-ion battery.

Realizing high -rate capability and high -efficiency utilization of polyanionic cathode materials is of great importance for practical sodium -ion batteries (SIBs) since they usually suffer from extremely low electronic conductivity and limited ionic diffusion kinetics. Herein, taking Na3.5V1.5Mn0.5(PO4)3 (NVMP) as an example, a reinforced concrete -like hierarchical and porous hybrid (NVMP@C@3DPG) built from 3D graphene ("rebar") frameworks and in situ generated carbon coated NVMP ("concrete") has been developed by a facile polymer assisted self -assembly and subsequent solid-state method. Such hybrids deliver superior rate capability (73.9 mAh/g up to 20 C) and excellent cycling stability in a wide temperature range with a high specific capacity of 88.4 mAh/g after 50 0 0 cycles at 15 C at room temperature, and a high capacity retention of 97.1% after 500 cycles at 1 C (-20 degrees C), and maintaining a high reversible capacity of 110.3 mAh/g in full cell. This work offers a facile and efficient strategy to develop advanced polyanionic cathodes with high -efficiency utilization and 3D electron/ion transport systems. (c) 2024 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.