In order to ensure the safe use of the new cobalt-free lithium battery, an electrochemical-thermal coupling model was established for the cobalt-free lithium battery to study its charge-discharge characteristics and its electrical and thermal characteristics at different rates and different ambient temperatures. It is found that the charge-discharge characteristics of cobalt-free lithium batteries meet the characteristics of typical ternary lithium batteries, and the high charge-discharge rate and low temperature environment increase the temperature inhomogeneity of the battery and reduce the thermal stability of the battery. Under high-rate charge and discharge, ohmic heat accounts for a large proportion, and the battery produces more heat; in the low temperature environment, the polarization effect of the battery is intensified, and the voltage drop is increased. This study has important reference value for evaluating the electrothermal characteristics of cobalt-free lithium batteries and the safe use of batteries.

Amid the global transition toward low-carbon energy systems, proton exchange membrane fuel cell combined heat and power (PEMFC-CHP) has attracted extensive attention due to its high efficiency and clean operation. To identify PEMFC-CHP system architectures that can accommodate the seasonal energy-use characteristics of Chinese households, this paper proposes two air source heat pump (ASHP) integrated PEMFC-CHP architectures: a serial architecture and a parallel architecture. Year-round performance and seasonal adaptability are evaluated under two representative residential demand patterns, namely seasonal-shift load households and steady-heatleading load households. For a fair comparison, both architectures adopt an iterative control strategy to decouple thermal and electrical energy management. The results indicate that the parallel architecture achieves superior overall performance, with more pronounced advantages for seasonal-shift load households. The total system efficiency reaches 84.58% in summer, 90.13% in mid-season, and 92.27% in winter, representing a maximum improvement of 0.60% over the serial architecture. Weekly hydrogen consumption can be reduced by up to 1.10%, while the annualized cost and carbon dioxide equivalent emissions are also lowered by approximately 0.60%. This study provides a valuable reference for the design and energy management of PEMFC-CHP system tailored to diverse residential energy demands in China.

As the operating speed of high-speed magnetic levitation trains continues to increase, ensuring their accuracy and efficiency has become a key challenge in technological development. The speed measurement and positioning system is crucial for ensuring the safety and stability of train operation. To address the issue of output signal distortion in the speed positioning system caused by train vibrations, track long stator joints, and temperature changes, this paper combines Long Short-Term Memory (LSTM) neural networks with the Gray Wolf Optimization (GWO) algorithm. This approach captures long-term dependencies in the time series data of the speed positioning system output from a test train and diagnoses faults in the redundant signals. Comparing real train data, the results demonstrate that the speed measurement and positioning system using this algorithm can effectively detect and diagnose sensor faults. It provides high-precision fault detection and localization results for predicting train position and speed.

Hybrid Vertical Take-Off and Landing Unmanned Aerial Vehicles (H-VTOL-UAV) are experiencing rapid growth in logistics, surveillance, and patrol applications, and their continued advancement is critically dependent on improvements in energy management efficiency. However, existing research on hybrid power energy management strategies (EMS) has predominantly focused on conventional fixed-wing or helicopter configurations, leaving a notable absence of dedicated solutions for H-VTOL-UAV platforms. To address this gap, this paper proposes a multi-objective Deep Reinforcement Learning (DRL)-based EMS optimization framework aimed at maximizing the overall energy efficiency of fuel cell hybrid power systems. Through the development of a comprehensive model integrating the hybrid powertrain, mission power demand profiles, and an efficiency-oriented learning algorithm, comparative experiments are conducted under six typical flight conditions against the Equivalent Consumption Minimization Strategy (ECMS). The results demonstrate that the proposed strategy achieves an approximately 19.53% improvement in overall system efficiency while simultaneously reducing fuel cell stress by about 27.6 W.

An electrochemical-thermal coupling model based on COMSOL is developed to study the new cobalt-free lithium battery's changing electro-thermal characteristics under various charge-discharge rates and ambient temperature conditions. The simulated results are then compared to cobalt-containing lithium batteries to examine the impact of the battery's anode material on its characteristics. It is found that in the cobalt-free lithium battery, the degree of voltage drop at the start of the discharge is smaller than in the cobalt-containing lithium batteries; the charging time is shorter; the temperature change is larger; the temperature is higher than in the cobalt-containing lithium batteries under various ambient temperatures; the final temperature of the cobalt-free lithium batteries is higher than in the cobalt-containing lithium batteries by 0-5 degrees C; and the instability of the battery is found to be exacerbated by the high charge-discharge rate and high-temperature environment. Cobalt-free lithium batteries show excellent cycle stability, retaining 82 % of their capacity after 600 cycles at a 1C rate. This study discusses methods to optimize cobalt-free lithium-ion battery cathode materials, combining research on cathode material modification. This study is of great significance in further investigating the thermal management of new cobalt-free lithium batteries.

While ceramic ultrafiltration membranes are highly valued in wastewater treatment for their exceptional durability and robust resistance to harsh conditions, their application is often hindered by an inability to efficiently degrade emerging pollutants. To address this, we fabricated a novel MnTiO3-modified catalytic ceramic membrane (MTPCM) via a facile solid-phase impregnation-drawing-sintering process. The optimized MTPCM3, tightened 30 nm pore size, demonstrates a remarkable capacity to generate high concentrations of reactive oxygen species (ROS) while intensifying pollutant-ROS collisions. Evaluated using bisphenol A (BPA) as a model pollutant and peroxymonosulfate (PMS) as the oxidant, the integrated system achieved an outstanding 95.2% degradation efficiency within 30 min, maintaining robust stability across eight operational cycles. Furthermore, intermediate and toxicity assessments delineated the degradation pathways and confirmed detoxification. Mechanistically, radical quenching experiments coupled with density functional theory (DFT) calculations identified 1O2 as the dominant ROS, systematically elucidating its generation pathway. Crucially, finite element simulations confirmed that this superior catalytic performance stems from the synergistic interplay between high-density MnTiO3 active sites and a pronounced nanoscale confinement effect. Overall, MTPCM offers a simple, scalable, and highly reusable paradigm for advanced wastewater treatment, innovatively structuralizing pores to maximize ROS yield and accelerate interfacial reactions for highly efficient pollutant decontamination.

Fuel cell-based combined heat and power (CHP) systems enable cascade conversion of hydrogen chemical energy into electricity and heat, providing an effective pathway to enhance overall energy utilization efficiency. In this study, a system-level simulation model for a proton exchange membrane fuel cell CHP waste heat recovery system is developed, incorporating stack waste heat, auxiliary component heat dissipation, catalytic combustion heat, and air-source heat pump upgrading. The multi-source coupling characteristics and the effects of key operating parameters on system performance are quantitatively investigated. The results show that within the current density range of 0.2-1.2 A/cm(2), the fuel cell stack is the dominant heat source, with heat generation increasing linearly with current density. The catalytic combustion unit acts as a marginal heat source, contributing less than 2% of total heat. The performance of the heat pump system is primarily influenced by ambient temperature and compressor speed. The system energy distribution exhibits significant load dependence: as current density increases, the stack heat contribution rises from 35% to 78%, and the primary source of auxiliary power consumption shifts from the heat pump compressor to the stack air compressor. Although the heat pump COP continues to decline, the system COP first increases and then stabilizes. Sensitivity analysis indicates that ambient temperature improves CHP efficiency by 18% while increasing compressor speed enhances thermal efficiency by 51.7%, but reduces electrical efficiency by 25.2%, resulting in an overall CHP efficiency improvement of 11.0%. In contrast, cathode inlet pressure has a nearly neutral impact on system performance (<0.7% fluctuation).

The boost chopper (HS) is a core electrical component of the 440 V grid in high-speed maglev trains, providing reliable power for battery charging and auxiliary systems. Fault diagnosis of the HS is crucial for identifying operational faults and ensuring stable train operation. However, HS faults exhibit both long-period fluctuations and transient characteristics, which are difficult for a single network to capture synchronously. This paper proposes a multi-scale fault diagnosis method based on a TimesNet-CNN dual-branch architecture, constructing a parallel and complementary feature extraction mechanism. The TimesNet branch uses Fast Fourier Transform (FFT) to adaptively identify dominant periods, reshaping the 1D sequence into a 2D structure to explicitly model the global evolution of intra-period fluctuations and inter-period trends via Inception convolution. Meanwhile, the CNN branch employs stacked small convolutional kernels and hierarchical downsampling to extract local high-frequency anomaly features. After feature fusion, the method achieves synergistic discrimination of global periodicity and local transiency. Finally, experiments were conducted on a real-world dataset containing 11 system states (10 fault types and 1 normal state). Experimental results show that the proposed method outperforms TimesNet, CNN, ResNet and Informer models in precision, recall and F1-score. This validates the effectiveness of the dual-branch feature fusion mechanism in capturing multi-scale fault features, achieving high-precision identification of HS faults.

This paper presents a virtual calibration method for parameters in two coupled control strategy of a complex hybrid electric vehicle. Firstly, the sampling inputs are determined through the optimal Latin hypercube design, and the response are obtained by employing a validated physics-based model, forming the training dataset. Based on this dataset, the deep neural network model is employed to establish a surrogate model connecting control parameters and response, achieving a mean square error of 7.47 x 10-7. Using the deep neural network model, sensitivity analysis of calibration parameters is carried out through the Sobol indices method. The results of this analysis reveal calibration paths of key parameters and combinations, effectively reducing the search region. Finally, the double distance sorting genetic particle swarm optimization algorithm is employed to solve the multi-objective optimization problem. The optimization results indicate a 2.09 % reduction in the mean square error of speed, a 24.28 % reduction in equivalent fuel consumption, and a 14.71 % reduction in battery capacity loss compared to the initial values. These outcomes clearly confirm the effectiveness of the proposed calibration method in enhancing calibration efficiency and provide valuable theoretical guidance for actual calibration.

Reasonably configuring the concentration distribution of the mixture to achieve partially premixed combustion has been proven to be an effective method for improving energy utilization efficiency. However, due to the significant influence of concentration non-uniformity and flow field disturbances, the combustion behavior and mechanisms of partially premixed combustion have not been fully understood or systematically analyzed. In this study, the partially premixed combustion characteristics of methane–hydrogen–air mixtures in a confined space were investigated, focusing on the combustion behavior and key parameter variation patterns under different equivalence ratios (0.5, 0.7, 0.9) and hydrogen contents (10%, 20%, 30%, 40%). The global equivalence ratio and degree of partial premixing of the mixture were controlled by adjusting the fuel injection pulse width and ignition timing, thereby regulating the concentration field and flow field distribution within the combustion chamber. The constant-pressure method was used to calculate the burning velocity. Results show that as the mixture formation time decreases, the degree of partial premixing increases, accelerating the heat release process, increasing burning velocity, and shortening the combustion duration. It exhibits rapid combustion characteristics, particularly during the initial combustion phase, where flame propagation speed and heat release rate increase significantly. The burning velocity demonstrates a distinct single-peak profile, with the peak burning velocity increasing and its occurrence advancing as the degree of partial premixing increases. Additionally, hydrogen’s preferential diffusion effect is enhanced with increasing mixture partial premixing, making the combustion process more efficient and concentrated. This effect is particularly pronounced under low-equivalence-ratio (lean burn) conditions, where the combustion reaction rate improves more significantly, leading to greater combustion stability. The peak of the partially premixed burning velocity occurs almost simultaneously with the peak of the second-order derivative of the combustion pressure. This phenomenon highlights the strong correlation between the combustion reaction rate and the dynamic variations in pressure.

The planetary gear train is an essential part of the automatic transmission drive system, and its tooth surface morphological characteristics affect the dynamic characteristics of the planetary gear train through mesh stiffness and backlash. This paper introduces a method for analyzing the effect of tooth surface morphological characteristics on the dynamic characteristics of a two-row planetary gear train. The torsional dynamics model of a two-row planetary gear train is established by considering a variety of nonlinear factors, in which a time-varying meshing stiffness model considering fractal features is constructed using the potential energy method and fractal contact theory, and a backlash model considering fractal features is constructed using fractal theory and the involute tooth profile equation. The influence of tooth surface morphology on time-varying meshing stiffness and backlash is examined, and the influence of tooth surface morphology on the dynamic characteristics of the planetary gear train is analyzed based on bifurcation diagrams under different operating conditions. The findings reveal that: an increase in fractal dimension D or a decrease in characteristic scale factor G results in a smoother tooth surface, leading to enhanced meshing stiffness and reduced backlash; as D decreases and G increases, the relative displacement amplitude of meshing pairs of two rows of planetary gear train increases, and the most stable operating speed range of planetary gear train gradually moves to the direction of the low rotational speed; the vibration response of two rows of planetary gear train at Ra = 1.25 has a big difference, the relative displacement of the first row is smaller than that of the second row in the low and medium speed zones, but the opposite is true in the high speed zone.

The structural integrity and internal consistency of lithium-ion batteries are pivotal for their durability and safety. Conventional detection and evaluation methods often rely on expensive instrumentation and manual expertise, which hinder scalability and efficiency. This study presents an intelligent ultrasound-based approach for the automated identification of battery regions and defects, facilitating precise assessment of internal consistency and common defect types. Initially, sample cells-comprising normal cells and those with three typical defects-are prepared, and their ultrasound signals are analyzed. Multidimensional ultrasound features are then extracted, and a random forest-based model is developed for the automated classification of battery regions and defects. Experimental validation demonstrates that: (1) the proposed method can automatically classify eight battery regions, such as the infiltration area, bubble area, and tape area, with an overall accuracy exceeding 97.2 %; (2) the method accurately identifies three typical defects-aluminum foil insertion, electrode folding, and negative electrode scraping-with recognition rates of 90.7 %, 94.7 %, and 96 %, respectively, while providing three-dimensional defect localization. This study introduces a novel monitoring approach for battery production and application, thereby enhancing battery safety.

In practical combustion systems, the fuel-air mixture often exists in a partially premixed state, where the flame propagation characteristics critically influence the heat release behavior and energy conversion efficiency. Due to the combined effects of the concentration stratification, turbulent disturbances, and inherent flame instabilities, the propagation dynamics of partially premixed flames exhibit strong nonlinearity and complexity. To gain deeper insights into these mechanisms, this study employs a self-developed constant-volume combustion bomb platform to experimentally investigate CH4/H2/air mixtures under varying equivalence ratios (0.5, 0.7, 0.9), hydrogen fractions (10 %-40 %), and levels of partial premixing (mixture formation times from 10 ms to 3 min), focusing on flame propagation speed, structure, stretch sensitivity, and instability behavior. The results reveal that increasing the degree of partial premixing, hydrogen content, and equivalence ratio all significantly accelerates flame propagation. The effect of partial premixing is more pronounced for fuel-lean, hydrogen-rich flames. The relative thermal expansion ratio is introduced as an effective indicator of the stratification intensity and promotes flame acceleration by enhancing the local equivalence ratio, back-support effect, and hydrodynamic instability. The flame propagation speed increases nonlinearly with flame radius, indicating a pronounced acceleration behavior. Hydrogen addition increases the sensitivity of the flame acceleration index to the Markstein number. Partial premixing amplifies the roles of hydrodynamic and diffusional-thermal instabilities in driving flame acceleration. A consistent correlation is observed between flame wrinkling and propagation speed: greater degree of partial premixing leads to more pronounced flame front wrinkling and faster propagation, and the relationship was insensitive to the equivalence ratio, but the slope increased with increasing hydrogen content. This work advances the fundamental understanding of partially premixed flame propagation dynamics and provides theoretical guidance for optimizing the energy conversion of efficient and clean combustion systems.

A simulation study was conducted on the hydrogen leakage diffusion process and influencing factors of fuel cell vehicles in enclosed spaces. The results indicate that when hydrogen leakage flows towards the rear of the vehicle, it mainly flows along the rear wall of the space and diffuses to the surrounding areas. Setting ventilation openings of different areas on the top of the carriage did not significantly improve the spatial diffusion speed of the leaked hydrogen, and the impact on the concentration of leaked hydrogen was limited to the vicinity of the ventilation openings. The ventilation opening at the rear can accelerate the diffusion of hydrogen gas to the external environment, significantly reducing the concentration of hydrogen and rate of gas rise. When the leaked hydrogen gas flows towards the front of the vehicle and above the space, the concentration of hydrogen mainly increases along the height direction of the space. The research results have significant safety implications for the use of fuel cell semi-trailer trucks.

Effective extraction and analysis of proton exchange membrane fuel cell (PEMFC) ageing characteristics is a prerequisite for long-term prediction of PEMFC ageing. To address the problem that it is difficult to comprehensively and effectively extract the reversible and irreversible ageing features data of PEMFC under dynamic operating conditions. In this paper, the equivalent inductor module is introduced on the basis of the classical equivalent circuit model to realize the accurate simulation of PEMFC dynamic behaviors. By further summarizing the activation polarization and ohmic polarization ageing laws, a PEMFC ageing behavior dynamic model is established, and the effective extraction of PEMFC reversible and irreversible ageing voltage component data under dynamic operating conditions is realized. Single-input recurrent convolutional neural networks cause the problem of prediction error accumulation due to the single dimension of training data. By building a multi-input recurrent convolutional neural network algorithm, the error accumulation of the prediction algorithm is reduced and the long-term ageing prediction accuracy of PEMFC is improved.

With the economic and social development, the demand for energy is increasing globally, and the environmental problems are becoming more serious, and the massive use of fossil energy such as oil can cause greater environmental pollution problems, in order to reduce environmental pollution and the greenhouse effect, the development of electric vehicles and power batteries are getting more and more attention from the society. Based on the above background, in order to improve the economic and social benefits of electric vehicles, researchers have conducted a large number of related studies on the performance of power batteries. This paper carries out a simulation and analysis study on the performance change of lithium battery under typical working conditions under thermoelectric multi-coupling factors. In this paper, three-dimensional modeling is carried out based on the working characteristics of lithium batteries, which electrochemical model will be used P2D (pseudo two dimensional, P2D) model theory. The main research content of this paper and the results to be obtained are as follows: the use of COMSOL Multiphysics software platform for lithium-ion batteries to establish a three-dimensional electrochemical thermal coupling model; on this basis, through the relevant experimental data, to verify the authenticity and feasibility of the simulation model; the use of the simulation model for the simulation and analysis of the battery performance degradation, to obtain the multi-factorial coupling of heat, electricity, the performance of lithium battery degradation data and patterns. The simulation model is used to simulate and analyze the battery performance degradation to obtain the performance degradation data and law of lithium battery under multi-factor coupling of heat and electricity. Based on the above background, in order to improve the economic and social benefits of electric vehicles, researchers have conducted a large number of related studies on the performance of power batteries. This paper focuses on the influence of different charging and discharging rates on battery performance under normal temperature conditions. It is found that the battery decay rate is the lowest at 0.2C charging rate, and the battery decay rate is the highest at 2C charging rate, which significantly accelerates the battery decay. The charging ratio of 1C is a more optimized choice.

The hybrid power system with dual motors and multiple clutches experiences significant torque fluctuation during mode switching process due to the different torque response characteristics of the motor and engine. To address this issue, this paper focuses on the estimation of clutch friction torque and the development of dynamic coordinated control strategies for the components. Firstly, based on the dynamic model of the novel dual-motor hybrid electric vehicle, a torque observer based on the Kalman filter algorithm is developed to predict the friction torque generated in the clutch sliding friction stage. Secondly, the control strategies are developed for the mode switching process from single-motor to dual-motor and from dual-motor to parallel drive on a co-simulation platform. Thirdly, a power level Hardware-In-the-Loop test platform is built, and the performance of the designed control strategies is verified by the HIL platform. The results show that for the mode switching process from dual-motor to parallel drive, compared with the control strategy using the engine target speed, the control strategy based on engine idle speed proposed in this paper reduces the clutch sliding friction work and the maximum longitudinal jerk of the vehicle by 42.5% and 25.4%, respectively.

Fuel cell hybrid vehicles (FCHVs) are significant for achieving zero carbon emissions. Connected FCHVs can leverage traffic information to collaboratively optimize cruise and power allocation control, enhancing various performance aspects. For urban driving scenarios, this paper introduces a multi-strategy series control architecture for longitudinal cruise and power allocation control in connected FCHVs. However, particle swarm optimization (PSO) algorithms face challenges in high-dimensional decision and objective spaces when optimizing multiple strategies. Additionally, manually preset PSO parameters hinder particle evolution from dynamically adapting to unknown multi-objective spaces, thereby limiting the development of multiple performance metrics. To address this issue, this paper proposes a Q-learning multi-objective PSO (QMOPSO) algorithm. This algorithm tackles high-dimensional optimization challenges by improving population initialization distribution and subpopulation division, and enables particles to dynamically adjust exploration strategies, thereby maximizing multiple objective performances. The results indicate that compared to a control scheme optimized with PSO under predefined driving conditions, the multi-strategy series control framework optimized with the QMOPSO algorithm improves tracking stability by 50.20%, driving comfort by 1.77%, fuel economy by 6.10%, and reduces power source degradation by 2.04% in urban driving scenarios. Compared to PSO and multiobjective PSO algorithms, the QMOPSO algorithm demonstrates superior trade-offs. This research provides a collaborative optimization solution for FCHVs in connected environments.

Starting from the requirements of medium and low speed maglev vehicles for traction system and the design of traction performance, the characteristics of linear motor are determined through size requirements and power analysis. Through the configuration of typical medium and low speed maglev vehicle traction system, the traction system design of medium and low speed maglev train is gradually developed. The traction characteristics, motor size, motor top-level design parameters and motor performance of the motor are analyzed respectively. Based on the demand of traction characteristics, space size and material cost, a linear induction motor suitable for the maglev train of Qingyuan line in Guangdong is designed. Based on this linear motor, the simulation test is carried out. The experiment shows that this design can meet the requirements of medium and low speed maglev traction.

In this study, a concentration overvoltage model that focuses on describing variable-temperature operating condition properties for PEMFCs is established. Sensitivity analysis and a quantification study of oxygen transport resistance are carried out based on the oxygen transport resistance model and measurement data. By analyzing the influence of temperature on cathode oxygen transport resistance, the key structural parameters of the cathode oxygen transport resistance models are estimated, and the parameter modification method of fuel cell limiting current density under variable temperatures is proposed. Based on the polarization curve test experiments under variable-temperature conditions, it is demonstrated that the newly developed concentration overvoltage model reduces the relative error of simulation for a low Pt loading fuel cell in the high current region by 2.97% and 10.06% at 60 °C and 80 °C, respectively. The newly established concentration overvoltage model of a PEMFC solves the problem that the parameter of limiting current density is set without considering the influence of fuel cell temperature fluctuation, which leads to the poor simulation accuracy of the concentration overvoltage model in the high current region.