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Table of Content

    17 October 2023, Volume 37 Issue 9 Previous Issue    Next Issue
    Specially invited articles

    Energy management research of intelligent connected hybrid electric vehicles: a review

    2023, 37 (9):  1-12. 
    Abstract ( 399 )   PDF (1711KB) ( 463 )   Save
    In response to the increasing societal emphasis on environmental performance and the growing demand for energy-efficient transportation methods, hybrid electric vehicles (HEVs) are recognized as a key enabler of future green mobility. The energy management strategy within the powertrain of HEVs is regarded as a pivotal technology to ensure high energy utilization efficiency and low carbon emissions. Especially in the context of the development of intelligence and connectivity, the integration of smart connectivity technologies with energy management strategies has become a prominent focal point in this domain. Hence, this paper undertakes an in-depth analysis and comprehensive survey of the current state and categorization of intelligent and connected vehicles (ICVs) energy management strategies. The objective is to provide insights for further research in the domain of energy management strategies for hybrid electric vehicles in intelligent traffic environments, while also offering guidance for future technological advancements. Firstly, this paper reviews a series of classic energy management strategy methods that have emerged in the field of hybrid electric vehicles in recent years. These methods encompass traditional rule-based and optimization-based strategies, as well as more advanced machine learning-based approaches. Through a comparative analysis of these methods, it is revealed that mainstream research approaches can be categorized based on their primary contributions, including: enhancement of existing algorithms or introduction of novel algorithms; integration and fusion of different algorithms; incorporation of additional environmental information to enhance adaptability to varying operating conditions. These introductions can provide an overall understanding of the research foundation to help researchers understand the evolution and trends in the field. Secondly, this paper provides a detailed overview of the system architecture and operation principles of intelligent-connected cloud control system, emphasizing their critical role in hybrid electric vehicle energy management, and analyzes the combined application of cloud control system and energy management strategy, which provides potential avenues for more intelligent energy management. Subsequently, this paper places particular emphasis on conducting in-depth research into energy management strategies for HEVs within the context of intelligent connected environments, with a specific focus on both single-vehicle and multi-vehicle scenarios, and summarizes the design methodologies found in the literature. Regarding single-vehicle strategies, the paper outlines the classifications of energy management strategies under the context of energy-efficient path planning, energy-efficient speed profile generation, and state-of-charge management. For multi-vehicle scenarios, it investigates how vehicles can cooperatively control energy management strategies to reduce overall energy consumption. This includes energy management strategies based on adaptive cruise control (ACC) in platooning scenarios and predictive cruise control (PCC) in convoy scenarios, as well as energy management strategy planning for homogeneous and heterogeneous vehicle fleets. Finally, this paper identifies some limitations in current research, including practical applications in complex traffic environments and algorithm efficiency. In light of these limitations, future research priorities are proposed to further advance the application of intelligent technologies in hybrid electric vehicles. These areas of focus encompass: adoption of efficient and adaptive optimization algorithms; development of novel solutions for intelligent traffic system deployment; exploration of new methods for multi-vehicle cooperative optimization. In summary, through a comprehensive and in-depth study of intelligent-connected hybrid electric vehicle energy management strategies, this paper provides a holistic understanding of the field. It offers valuable references and guidance for future research and development directions, aiming to achieve more efficient and environmentally-friendly transportation methods. These efforts hold the potential to contribute to the realization of sustainable green mobility in the future, thereby making significant contributions to environmental and societal sustainability.
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    “Research on State Estimation and Prediction Technology of Advanced Power Battery”Special Column

    Correlation analysis of battery pack temperature and voltage consistencies based on cloud data

    2023, 37 (9):  13-22. 
    Abstract ( 161 )   PDF (4092KB) ( 246 )   Save
    Due to the initial difference in manufacturing process and the dynamic difference in application of a battery pack, there exists battery inconsistency of the battery pack, which has a negative impact on the overall performance of the battery pack and cause safety risks of the electric vehicles. This paper takes the battery pack of an electric vehicle as the object to study the correlation of battery pack temperature consistency and voltage consistency. Firstly, the raw data is pre-processed to solve the problems of inconsistent raw data field format, data missing, and bad points. Moreover, according to the discharge and charge state of the battery pack, effective charge and discharge segments has been divided. All the raw data are divided into 1 157 discharge and charge segments. Based on these effective segments, the characteristics of the total voltage and current under typical discharge conditions and the single battery cell voltage and temperature at different positions under the same discharge segment are then analyzed. It is found that multiple battery cells are detected with the same voltage at the same time, making it difficult to determine the single battery cell corresponding to the highest or lowest voltage. In addition, there are significant differences in the temperature at the different positions of the battery pack, and there is a certain correlation between temperature difference and discharge time. Thirdly, a method based on hierarchical clustering to analysis battery pack temperature inconsistency and voltage inconsistency under discharge condition is proposed. In the method, Euclidean distance of different temperature and voltage sampling points is used for the class division, and the average distance is used to calculate the center distance of each clustering. Fourthly, in order to quantitatively characterize the temperature consistency and voltage consistency of the battery pack, the dispersion of the whole battery pack is measured by the clustering center distance. The maximum and minimum clustering center distance are calculated, and the range of clustering distance is used as the inconsistency evaluation index to analyze the trend the temperature consistency and voltage consistency of the battery pack. It is found that seasonal changes in environmental temperature can affect the temperature distribution within the battery pack, and temperature inconsistency is inversely proportional to the average environmental temperature. When the average environmental temperature is high, the temperature consistency is better; When the environmental temperature decreases, the temperature inconsistency increases. Finally, the correlation analysis of battery temperature consistency and voltage consistency is carried out. It is found that the temperature inconsistency of No.1 module is the largest, and the temperature consistency of No.2 temperature sensor in No.1 module is the worst. The voltage consistency of the battery cell corresponding to the position with poor temperature consistency is also poor, that is, No.4 and No.69 battery cells. The voltage inconsistency is greatly affected by temperature inconsistency, and the voltage inconsistency rises in steps, which is unrecoverable.
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    SOC estimation of lithium-ion battery by Extended Kalman Filtering algorithm based on fractional order battery model

    2023, 37 (9):  23-30. 
    Abstract ( 139 )   PDF (2034KB) ( 148 )   Save
    Since the SOC (state of charge) of a lithium-ion battery cannot be directly measured, and can only be estimated by the external output characteristics of the battery. Taking lithium-ion phosphate lithium battery as the research object and considering the various complex nonlinear characteristics of the battery, the electrochemical impedance characteristics of the battery were analyzed. To improve the traditional equivalent circuit model, the fractional order equivalent circuit model was established using the constant phase element (CPE) combined Genetic Algorithm and hybrid impulse dynamic test to identify the parameters of the fractional equivalent circuit model offline; Based on the Extended Kalman Filter algorithm, the lithium battery SOC estimation model was built by a Fractional Order Extended Kalman Filter (FEKF) algorithm; According to the DST (dynamic stress test) conditions, charging and discharging plan for the lithium battery were designed, and the battery current and voltage data were collected in real time at an ambient temperature of 25 ℃, which was input into the model established by Matlab to estimate the SOC of the battery. Compared with the simulated SOC of the traditional second-order Thevenin circuit model, the SOC estimated based on the FEKF algorithm has higher accuracy and smaller volatility, and the error is less than 0.72% and the RMSE is only 0.24%.
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    SOH estimation of lithium-ion batteries based on frequent item statistics

    2023, 37 (9):  31-39. 
    Abstract ( 130 )   PDF (4477KB) ( 103 )   Save
     Rapid and accurate estimation of battery state of health (SOH) is the basis for ensuring the safety of power battery systems. During the operation of pure electric vehicles, traditional estimation methods are difficult to use the limited computing resources of the vehicle to construct accurate SOH estimation models online. To solve this problem, an online feature extraction method using short-term monitoring data to construct battery health indicators (HI) is proposed. In this method, the accumulated electricity in different voltage ranges is regarded as frequency items, and the Lossy Counting algorithm is used to construct a summary data structure to perform statistic on the distribution of frequency items, and the battery health status is characterized based on the change of the distribution law of the frequency items. Simulation and experimental results show that the proposed method can use on-board computing resources to extract battery health status indicators with small time and space complexity.
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    A review of fault diagnosis algorithms for lithium ion batteries

    2023, 37 (9):  49-61. 
    Abstract ( 624 )   PDF (1111KB) ( 825 )   Save
    Lithium-ion battery has a broad application prospect in the field of new energy vehicles, safety is an important factor affecting the promotion of new energy vehicles. The development of safe and reliable lithium-ion battery fault diagnosis technology has become a consensus in the industry. Fault diagnosis algorithm is the basis of lithium-ion battery fault diagnosis technology, so it is very important to study the lithium-ion battery fault diagnosis algorithm. This paper firstly classified the fault diagnosis algorithms of lithium ion battery, summarized the research status of the fault diagnosis algorithms based on knowledge, model and data driven in recent years, then the advantages and disadvantages of different algorithms are expounded, finally summarized and prospected the fault diagnosis algorithms of lithium ion battery, aiming to provide the direction for the future scholars
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    “Research on Energy Management Technology of New Energy Vehicles” Special Column

    Research on energy recovery strategy of P1P3 PHEV dual motor cooperative braking

    2023, 37 (9):  62-70. 
    Abstract ( 170 )   PDF (4436KB) ( 163 )   Save
    A dual-motor plug-in hybrid electric vehicle (plug-in hybrid electric vehicle, PHEV) with P1 and P3 is chosen as the research platform. In the energy recovery process, the P1 and P3 motors can be performed simultaneously, providing a more significant energy recovery advantage than other configurations. During braking, when the P1 motor is more efficient than the P3 motor, the P1 motor is used in preference for braking and the P3 motor provides the remaining required braking force. However, when the P3 motor is more efficient than or equal to the P1 motor, the P3 motor is used in preference for braking and the P1 motor makes up for the remaining required braking force. The experiment is carried out on a dual-motor plug-in hybrid vehicle equipped with an AMT (electronically controlled mechanical brake transmission).In order to recover as much energy as possible while ensuring braking safety, a hybrid vehicle based on dual motors is proposed. Based on the distribution of dual-motor energy recovery for front and rear axle, electromechanical and dual-motor braking force, a multi-stage braking power distribution strategy is proposed. The whole vehicle model is established and analyzed by using Matlab/Simulink. The simulation results show that the braking energy recovery strategy proposed in this paper can reach 66.56%, and the recovery effect is good.
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    Fuzzy energy management strategy for fuel cell vehicles with self-adaptive operating conditions

    2023, 37 (9):  71-78. 
    Abstract ( 119 )   PDF (4056KB) ( 243 )   Save
    Aiming at the problem of poor adaptability of fuel cell vehicle energy management strategy in complex operating conditions, an adaptive fuzzy energy management strategy based on back propagation (BP) neural network was proposed. The Chinese passenger vehicle driving cycle (CLTC-P) was selected as the sample driving cycle, and the maximum speed, average speed and idle time ratio were used as the characteristic parameters to establish the BP neural network driving cycle recognition model. The fuzzy energy management strategies under three typical working conditions were developed, the parameters of the fuzzy strategy were optimized offline by using genetic algorithm, and the working conditions were identified online by using BP neural network and the appropriate strategy parameters were selected. The simulation results show that compared with the rule-based strategy and the fuzzy control strategy with condition recognition, the adaptive fuzzy energy management strategy can reduce the equivalent hydrogen consumption of the vehicle by 6.87% and 3.41%, respectively, which indicates that the proposed strategy can effectively identify random conditions, improve the problem of poor adaptability of the strategy, and further improve the economy of the vehicle.
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    Energy management strategy for speed prediction of plug in hybrid electric vehicles

    2023, 37 (9):  79-87. 
    Abstract ( 123 )   PDF (4926KB) ( 224 )   Save
     In view of the lack of speed prediction accuracy in the predictive energy management strategy of hybrid electric vehicles, the fuel economy is reduced, a speed prediction method based on wavelet decomposition (WD) and dual channel convolutional neural network (CNN) is proposed to improve the speed prediction accuracy. Firstly, wavelet decomposition is used to decompose the original vehicle speed sequence into multiple components to reduce the nonstationarity of the original vehicle speed sequence; Secondly, each component is sent to two parallel convolutional neural networks for feature extraction, and then sent to long short-term memory neural network (LSTM) for prediction after feature fusion; Then, the final speed prediction result is obtained by superimposing the prediction results of each component. Finally, based on the results of vehicle speed prediction, the energy management strategy based on model predictive control is established to optimize the power source output in the prediction time domain. The simulation results show that the prediction accuracy of the speed prediction method proposed in this paper is 58.96% higher than that of the CNN-LSTM network model under CLTCP conditions. The fuel consumption of the predictive control strategy proposed in this paper is 13.3% higher than that of the dynamic programming strategy, but the fuel consumption is 18.98% lower than that of the rule-based strategy, which verifies the effectiveness of the speed prediction method and the predictive energy management strategy.
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    A fuzzy adaptive minimum energy management strategy for equivalent fuel consumption based on particle swarm optimization

    2023, 37 (9):  88-99. 
    Abstract ( 154 )   PDF (5117KB) ( 215 )   Save

    In order to enhance the adaptability of the Equivalent Consumption Minimization Strategy (ECMS) for different operating conditions, a Particle Swarm Optimization-based Fuzzy Adaptive Equivalent Consumption Minimization Strategy (PSO-fuzzy A-ECMS) is proposed. This strategy is built upon the method of proportional adjustment of equivalent factors based on State of Charge (SOC) feedback. Addressing the limitations of relying solely on SOC feedback for equivalent factor adjustment, the PSO-fuzzy A-ECMS introduces fuzzy control to adjust the proportional coefficients based on demand power and SOC inputs. Subsequently, the Particle Swarm Optimization algorithm is used to optimize the membership functions of the fuzzy controller, reducing the dependence on expert knowledge in energy management applications.

    In this study, by considering demand power and SOC as inputs, the fuzzy controller dynamically adjusts the proportional coefficients of the equivalent factors. This approach better adapts to energy management requirements under different operating conditions, improving fuel economy and battery charge-discharge balance of the system.

    To optimize the membership functions of the fuzzy controller, the researchers employed the Particle Swarm Optimization algorithm. This heuristic optimization algorithm simulates the foraging behavior of bird flocks to find the optimal solution. In this research, the Particle Swarm Optimization algorithm is utilized to adjust the parameters of the membership functions in the fuzzy controller, making the energy management strategy of the system more accurate and efficient.

    Finally, the performance of the PSO-fuzzy A-ECMS strategy is evaluated through simulation experiments under standard driving cycles. The simulation results demonstrate that compared to the SOC feedback-based equivalent factor correction method, the PSO-fuzzy A-ECMS strategy better maintains SOC within a reasonable range and achieves improved control effectiveness in terms of fuel economy and battery charge-discharge balance.By incorporating fuzzy control and Particle Swarm Optimization, the PSO-fuzzy A-ECMS strategy enhances the adaptability of the Equivalent Consumption Minimization Strategy for different operating conditions, reduces reliance on expert knowledge, and achieves better control performance.

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    Machinery and materials

    Design and simulation of a double gantry manipulator

    2023, 37 (9):  100-107. 
    Abstract ( 144 )   PDF (3874KB) ( 88 )   Save
    Aiming at the situation that the series manipulator cannot work reliably for a long time in the high temperature environment, this paper designed a double-gantry manipulator suitable for the high temperature harsh environment, derived its kinematics model, and carried out the motion simulation. Firstly, the gantry manipulator is transformed into an equivalent series manipulator. Then the D-H method was used to model the equivalent manipulator, and the forward and inverse kinematics equations of the gantry manipulator were derived. The relationship model between the end pose of the manipulator and the parameters of the driving joint was given. Finally, combined with obstacle avoidance path planning, the correctness of the forward and inverse kinematics equations is verified through simulation analysis, which shows that the designed gantry manipulator can achieve the predetermined functions.
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    Study on the notched specimen of carburized steel modified by abrasive water jet

    2023, 37 (9):  108-115. 
    Abstract ( 73 )   PDF (2995KB) ( 97 )   Save
    Conventional contact processes such as rolling and extrusion are difficult to handle notched materials with high hardness, are not prone to plastic deformation, and are difficult to introduce residual stress. Therefore, it is necessary to seek new methods for effective modification. Pre mixed jet is a new type of surface modification technology that combines high-pressure water jet technology with shot peening. It utilizes the speed of high-pressure water flow to propel shot particles into the target material at high speed, forming multiple covered craters on the surface of the part, achieving the goal of introducing residual stress and modification. The characteristic of this process is high energy concentration, and through the combination of high pressure and high-strength pellets, the hard surface after heat treatment can be modified. In addition, this method is flexible in operation and can achieve effective modification of various spatial surfaces and narrow grooves under certain nozzle size conditions and with the cooperation of relevant motion platforms. In order to investigate the effects of different process parameters on the radius size, residual stress, and roughness of the notch in the pre mixed abrasive water jet system, the general pattern of modified notch specimens was obtained and the optimal jet process parameters were determined. A pre mixed abrasive water jet system was used to modify the fatigue specimen with a notch radius R=0.6 mm of 18CrNiMo7-6 carburized alloy steel after heat treatment. A combination of theoretical and experimental methods was used to measure and analyze the notch radius size, residual stress, and roughness of the specimen under different pressures, workpiece speed, nozzle movement speed, and jet frequency using a three-dimensional morphology analyzer and residual stress analyzer, Summarized the variation law of the notch fillet radius of the modified sample and the construction law of the residual stress field. Research has found that under a certain material, the change in narrow size caused by abrasive water jet is mainly through the action of abrasive, while pure water has little effect on size change. As the pressure of the abrasive jet increases, the size of the notch fillet changes more and more, and as the pressure increases, a better residual stress field can be obtained, but at the same time, a higher roughness will also be generated at the root of the notch. Taking residual stress, roughness, and machining efficiency as evaluation indicators, a suitable ratio of 0.48 was obtained by comprehensively considering the ratio of workpiece speed and nozzle movement speed. Due to the influence of erosion, the increase in jet frequency leads to a decrease in residual stress values near the surface layer, but there will be a certain increase in residual stress layer depth, maximum residual stress value, and layer depth corresponding to the maximum stress value. Among them, the maximum stress value corresponds to a significant increase in layer depth, increasing by 20 μm and 30 μm respectively, which is 66.67% and 100% higher than that of a single jet. Due to different pressures, workpiece speeds, nozzle movement speeds, and jet frequency, the modification effect can be affected differently. Therefore, suitable jet modification process parameters can be selected based on actual processing needs.
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    Research on the influence law of GLARE laminates hot pressure forming process parameters

    2023, 37 (9):  116-123. 
    Abstract ( 77 )   PDF (3698KB) ( 82 )   Save
    The hot pressure formability of glass fiber reinforced metal laminates (GLARE) with single curvature is studied. Based on the wall thickness distribution and forming depth of specimens, the influence mechanism and law of temperature and pressure on the hot pressure formability of the thermosetting and thermoplastic resin based GLARE laminates are explored experimentally. The results show that the high temperature would cause the decomposition failure of the two kinds of resins, leading to laminates defects such as delamination and rupture. At high temperature, the fluidity of the molten resin increases, and the extrusion of the upper and lower plates makes the resin flow outward along the radial direction, which reduce the thickness of the center of the specimen. At the same time, the pressure of the blank holder lead to the accumulation of resin, which increases the thickness of the laminates in the rounded corner area. When the temperature is 110~140 ℃, the formability of the thermosetting resin prepreg GLARE is good, and the lowest pressure of the specimen laminate mold is 4 MPa. When the temperature is between 170 ℃ and 200 ℃, the formability of the thermoplastic resin prepreg GLARE is optimal, and the minimum pressure of the specimen laminate mold is 3 MPa.
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    Multi-objective optimization and experimental study on ultrasonic rolling process parameters of 18CrNiMo7-6 steel

    2023, 37 (9):  124-133. 
    Abstract ( 89 )   PDF (4452KB) ( 84 )   Save
    To improve the surface quality of 18CrNiMo7-6 steel, samples were processed by ultrasonic rolling with a self-designed ultrasonic rolling device.The effects of process parameters and their interaction on surface roughness, residual stress, and wear rate of 18CrNiMo7-6 steel were studied by single-factor tests and response surface methodology, and the significance of the established second-order response surface regression model was tested. The desirability function method is used to optimize the process parameters, and the optimum process parameters are that the static pressure is 269 N, the number of passes is 4, and the ultrasonic amplitude is 7 μm. The experimental results show that the surface plastic deformation, grain refinement, and wear resistance of the sample were improved, and the errors between experimental values and predicted values were less than 5%, which verified the feasibility of the optimization results.
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    Research on the control of a six-axis robotic arm using the DDPG algorithm

    2023, 37 (9):  134-140. 
    Abstract ( 248 )   PDF (1748KB) ( 157 )   Save
     A reinforcement learning method based on Deep Deterministic Policy Gradient(DDPG) is presented to more effectively tackle the problem of controlling a six-axis robotic arm in three-dimensional space in order to address the issues of low accuracy, stability, and executability of existing control algorithms in complicated situations. The simulation environment is established in the MuJoCo platform, the planned robotic arm is imported as the test object, and the DDPG algorithm, the Soft Actor-Critic Algorithms (SAC), and the Twin Delayed Deep Deterministic Policy Gradient (TD3) are utilized for repeated comparison tests in the simulation environment. The study demonstrates that the DDPG algorithm-based robotic arm control approach can successfully increase the accuracy and stability of robotic arm control, and that this algorithm is more stable than SAC and TD3.
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    Dynamic modeling and vibration response analysis of harmonic reducer considering flexible deformation

    2023, 37 (9):  141-149. 
    Abstract ( 215 )   PDF (3171KB) ( 377 )   Save
     As one of the key components in robot, the harmonic reducer directly affects the reliability and service life of the whole system. It’s necessary to establish a more precise mechanism model to explain its complicated and unique vibration features. A new methodology for harmonic drive modeling is proposed based on the lumped-parameter method and then the coupled vibration mechanism is revealed by frequency spectrum analysis. Combined with the geometric structure characteristics of the parts, the equivalent stiffness between the interacting elements is qualitatively analyzed; The dynamic differential equations of the harmonic reducer with three degrees of freedom in the torsion direction are established, and the mapping relationship between the dynamic stiffness characteristics caused by the harmonic gear meshing and the vibration response characteristics is analyzed. Through the mechanism and simulation analysis, the frequency distribution law of the vibration response of the harmonic reducer is revealed. It’s proved in the experimental signal that this harmonic drive model can replicate many features in actual vibration response
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    Effect of laser welding and heat treatment on the microstructure of CLF-1 steel for fusion reactor

    2023, 37 (9):  150-157. 
    Abstract ( 72 )   PDF (4915KB) ( 124 )   Save
    The optimized laser welding process was used to weld the 6mm thick CLF-1 steel and subsequent weld tempering heat treatment. The sample were characterized by electron channel contrast and electron backscatter diffraction, and the microstructure evolution of as-weld and tempered CLF-1 steel was studied. The results show that as-weld area mainly consist of α′-Fe single phase dislocation martensite structure, transformation martensite follows KS and NW orientation relationship; After post weld heat treatment, M23C6 phase precipitated in the interior and interface of martensite variant respectively. Post weld heat treatment can effectively reduced the orientation difference in the interior of martensite variant, but it have little effect on the martensite phase. The microhardness test results show that the microhardness of the as-weld area of CLF-1 steel is 358HV0.2 and the microhardness of the weld significantly reduce to 258HV0.2 after post weld heat treatment. At last, a good stress removal index can be obtained by tempering treatment.
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    Preparation of LDH conversion coating on AA2099-T83 aluminum-lithium alloy and its corrosion resistance

    2023, 37 (9):  158-166. 
    Abstract ( 83 )   PDF (3488KB) ( 102 )   Save
    The 3rd generation aluminum-lithium alloy is widely used in aerospace industry to realize lightweight manufacturing of aircraft. In this paper, the process of in-situ preparation of lithium salt conversion coating in lithium oxalate (Li2C2O4) aqueous solution was explored to improve the corrosion resistance of AA2099-T83 alloy. The microstructure and composition of the conversion coating were characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The corrosion resistance of the coating was studied by electrochemical impedance spectroscopy (EIS) and scanning vibrating electrode technique (SVET). The results showed that the concentration and temperature of the solution had a significant effect on the corrosion resistance of the conversion coating. The corrosion resistance increased with the increase of temperature and decreased with the increase of concentration. The conversion coating prepared in 0.01 M Li2C2O4 solution at 70 ℃ had the best corrosion resistance. The present work suggests that the Li-Al-LDH conversion coating on AA2099-T83 aluminum-lithium alloy has self-healing capacity without uploading any corrosion inhibitors. The Li-Al-LDH conversion coating can reduce the sensitivity of the alloy to localized corrosion and also inhibit the steady-state propagation of corrosion and, therefore, can be used as the primer of anti-corrosion coating for aluminum-lithium alloys.
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    Information and computer science

    A lightweight model of track detection based on knowledge distillation

    2023, 37 (9):  173-179. 
    Abstract ( 126 )   PDF (2166KB) ( 93 )   Save
    Aiming at the problems of many parameters and large calculation of the neural network model for rail defect detection, a lightweight model for rail detection based on knowledge distillation and its training method are proposed. The network model is composed of six layers of convolution layer and three layers of full connection layer. The trained DenseNet model is used as the teacher network to guide the training with the method of knowledge distillation, which makes the training of lightweight model simpler and ensures its accuracy. In the training phase of the model, SAM optimization algorithm with minimum sharpness is added to greatly improve the generalization ability of the model. Then VggNet, ResNet, DenseNet and other models are used as comparative experiments to evaluate the model. The average accuracy of the customized lightweight model trained by knowledge distillation in the rail detection data set is 97.3%, and the model parameter size is only 7.38 M, which is superior to other network models and can be deployed in many mobile terminals.
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    Fast adaptive frontier detection algorithm for in unknown environment

    2023, 37 (9):  180-188. 
    Abstract ( 170 )   PDF (2627KB) ( 111 )   Save
     Boundary sensing detection is one of the important parts of autonomous exploration of UAV. In order to improve the efficiency of boundary detection in the process of autonomous exploration in complex and diverse underground narrow environments, this paper proposes an adaptive fast boundary detection algorithm for unmanned aerial vehicles (ADPlanner) in unknown environments. First, we perceive the unknown environment of the underground tunnel through optical radar, adaptively adjust the local sampling space of the underground tunnel or mine tunnel environment, and greatly improve the sampling rate (the ratio of the sampling points added to the RRG to the number of sampling attempts) according to the environmental structure. Secondly, we propose a resampling rate to reduce the redundancy of sampling points of the adjacent adaptive sampling frame, Then, the importance sampling strategy is used to solve the oversampling problem of repeated areas in the GBPlanner and achieve incremental detection. The simulation experiment shows that in two different unknown scenarios, compared with the GBPlanner, the ADPlanner boundary detection sampling run time is reduced by 20.27%~38.33%, the path length is reduced by11.24%~18.86%, and the total exploration time is shortened by 27.38%~38.38%, which significantly improves the exploration efficiency of the UAV in the unknown environment.。
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    Cooperate iterative learning control algorithm for systems with a same structure and similar parameters

    2023, 37 (9):  189-197. 
    Abstract ( 92 )   PDF (2210KB) ( 83 )   Save

     The original iterative learning control (ILC) algorithm is a control algorithm to make the output approach a set objective, where the control input is learned from and corrected by the error of repeatedly tracking the given objective. Since it was proposed by Uchiyama and Arimoto in the 1970s and 1980s, ILC has become a powerful tool for solving the model-free control problems by applying the periodic and repetitive learning process. However, the original ILC is designed for the same system to track the same goal from the same initial condition. The strict requirement for repeatability limits the application range of the original ILC.

    For example, generic information will be generated by the operation from a series of systems with the same structure and similar parameters (SysSP). Meanwhile, the generic information is inadequately applied in original ILC due to its strict limitations in repeatability, which affects the overall efficiency of ILC for SysSP. Therefore, this paper proposes a cooperate ILC algorithm to solve the problem.

    Firstly, for a single-input-single-output-single-state SysSP, a state-space model with the property of parameter perturbation is established is established, the goal of the control is set, subsequently a mathematical description is formed. Then, inspired by the learning and recognition process, the ideology of ILC for SysSP is formulated and algorithm of ILC for SysSP is established: the law of cooperate learning is formed based on the feature where the commonalities and common cognition are formed from optimizing by repetitively learning and summarizing from the predecessors; the differences in the parameters of the different systems are ignored in the cooperate learning law and the algorithm is proceed by a fixed time sequence to obtain a convergent cooperate control input with commonalities; the control strategy initialized with the cooperate control input is developed, on the basis of the feature that the descendant will construct their cognition based on not only the fixed, precise commonalities by the predecessors but also the surrounding environment and individual requirement. Finally, simulations and validations are conducted for ILC for the selected 5 SysSPs. The control efficiencies of original ILC and the ILC for SysSP are analyzed based on the total number of the iterations under the two algorithms.

    Case simulation demonstrate that: under the random initial control input, the control input converges to the cooperate control input after 64 periods of iteration, after which the maximum absolute error in the output is less than 1.5×10-4 after 174 cycles or about 867 times of iteration (there are 5 times of iterations in a fully operated cycle), while it takes 331 cycles or 1 655 times of iterations to bring the error to the same level when original ILC is applied with the same parameter. For a given system, the error is 0.003 4 in the first iteration when the cooperate control input is applied, which is much less than the initial error when the original ILC applied; the error will converge into 0.000 15 after 79 times of cooperate iteration, while it takes 330 times of original iteration. The difference between the outputs by applying the two algorithms is of the order of 10-19 when the algorithms converge, showing that the cooperate iteration has no effect on the accuracy of the output.

    Therefore, a more general conclusion can be drawn that: compared with original ILC, cooperate ILC will be of a higher efficiency without loss of accuracy. A reduced number of iterations will be observed by taking the cooperate control input as the initial value of the control input.

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    A congestion control algorithm for remote driving data transmission

    2023, 37 (9):  198-207. 
    Abstract ( 108 )   PDF (5231KB) ( 60 )   Save
     Aiming at the high real-time network transmission application scenarios required in the remote driving field, an adaptive BBRv2 congestion control algorithm is proposed, which aims to optimize the fairness of the BBRv2 congestion control algorithm in sharing bottleneck links between different RTT flows in the remote driving network, make its working point approach the optimal working point, and reduce the high transmission delay caused by deviation from the optimal work point. The proposed algorithm improves the competitiveness of shorter RTT flows by adding a factor dynamic with RTT as the minus function, and improves the response sensitivity of longer RTT flows and shorter RTT flows by setting the queuing delay threshold to achieve relatively fair bandwidth allocation and low delay transmission. The effectiveness of the adaptive BBRV2 congestion control algorithm is verified through the Network Simulator (NS3) platform. The results show that, compared with BBRv2, the adaptive BBRv2 algorithm under the depth buffer of the remote driving network improves the fairness by 39.4% and significantly reduces the delay.
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    Research on the construction and optimization of heterogeneous distributed deep learning platform

    2023, 37 (9):  208-216. 
    Abstract ( 149 )   PDF (1555KB) ( 154 )   Save
    The combination of deep learning and big data technology is the general trend. There are still many problems to be solved and optimized in terms of resource management and task scheduling. Aiming at the three problems of weak management ability of heterogeneous resources, poor flexibility of native scheduling algorithms, and lack of a unified interface for multiple frameworks, a distributed deep learning framework integration platform under heterogeneous resources is proposed, and the optimization of task scheduling algorithms is studied. Based on the Spark framework, the platform expands and manages heterogeneous resources downwards, integrates the two frameworks SparkOnAngel and TensorFlowOnSpark upwards, and uses physical labeling to label machines with different computing resources. The dual representation of the model is used to optimize the scheduling algorithm. The results show that compared with the traditional spark cluster, the execution time of this platform is reduced by 13.4% in the mixed task scenario of 5 minist_spark and 5 WordCount tasks; can be reduced to 32.31%. The platform can expand the management of GPU resources, make the scheduling algorithm more flexible and efficient, and provide a unified calling interface for multiple frameworks.
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    A novel framework for occluded facial expression recognition by integrating attention mechanism

    2023, 37 (9):  217-226. 
    Abstract ( 165 )   PDF (2426KB) ( 132 )   Save
    Traditional facial expression recognition technologies rely heavily on manually formulated feature extraction rules, while deep learning-based facial expression recognition technologies can automatically perform the operations of feature extraction, feature selection and feature classification. However, for the faces with occluded parts, the existing facial expression recognition models based on deep learning cannot effectively deal with the interference of the occluded part of the face, and cannot accurately capture the features of facial unobstructed parts, thus leading to the degradation of recognition accuracy. To solve the aforementioned problems, a novel occluded facial expression recognition framework by integrating attention mechanism called FER-AM(facial expression recognition framework based on attention mechanism) is proposed, the local feature network is used to extract the local key features of facial expressions, and the global feature network is designed to learn the complementary information in the whole face, and the attention mechanism can effectively deal with facial occluded parts, such as glasses, masks and scarves. A large number of experiments are conducted on RAF-DB, AffectNet, CK+(Cohn Kanade) and FED-RO data sets, and the results show that the seven expression classification performance of FER-AM is better than the representative facial expression recognition models based on deep learning, and the recognition accuracy can reach 88.1%.
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    Application of improved NSGA-Ⅱ to water reservoir flow control system

    2023, 37 (9):  227-233. 
    Abstract ( 64 )   PDF (1639KB) ( 39 )   Save
    NSGA-Ⅱ is a classical multi-objective optimizer. However, the crowding distance in NSGA-Ⅱ has the drawback of ineffectively identifying promising individuals. Regarding this issue, this paper firstly analyzes the essential reason for this drawback, and further proposes a new evaluation strategy which is based on the sum of objectives. Based on the proposed evaluation strategy, this paper then proposes an improved NSGA-Ⅱ. To assess the improved NSGA-Ⅱ, this paper uses DTLZ test suite and multiple state-of-the-art optimizers to analyze the improved NSGA-Ⅱ. The experimental results indicate that the improved NSGA-Ⅱ outperforms most state-of-the-art methods. Besides, this paper further applies the improved NSGA-Ⅱ to a water reservoir flow control system problem. The experimental results demonstrate the outstanding performance of the improved NSGA-Ⅱ on practical engineering problems, indicating the practicality of the improved NSGA-Ⅱ.
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    Electrical and electronic

    Power coordinated control of three port converter based on sparrow search algorithm

    2023, 37 (9):  234-242. 
    Abstract ( 86 )   PDF (2958KB) ( 70 )   Save
    This study proposes a sparrow search algorithm-based power coordination control method for three-port converters in microgrids. Firstly, a novel isolated three-port converter is topologically analyzed, and an equivalent circuit model is established, followed by the derivation of the basic working principles and power transmission formulas of three-port converters. Secondly, a small-signal model of the three-port converter is developed, and a power coordination control strategy based on the sparrow search algorithm is designed. Subsequently, the superiority of the sparrow algorithm over traditional PI control is preliminarily verified through simulation circuits. Finally, the feasibility of the proposed strategy is validated through the RT-LAB experimental platform. The experimental results show that the three-port converter can operate stably, switch operating conditions according to external conditions, and achieve power coordination control.
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    Machinery and materials

    Short-term wind power prediction based on improved chimp algorithm and LSSVR-BiLSTM dual scale model

    2023, 37 (9):  243-252. 
    Abstract ( 111 )   PDF (4468KB) ( 119 )   Save
     Accurate wind power prediction is crucial for the efficient and safe operation of the power system. To improve the accuracy of wind power prediction, a short-term mixture model for wind power prediction was proposed based on the combination of improved complete ensemble empirical modal decomposition with adaptive white noise (ICEEMDAN), permutation entropy (PE), improved chimp optimization algorithm (ICHOA), least squares support vector regression (LSSVR) and bi-directional long short memory (BiLSTM) network. Firstly, the non-stationary original wind power sequence is decomposed into relatively stationary modal components through ICEEMDAN, and PE aggregation is used to reduce computational complexity. Secondly, the BiLSTM model and LSSVR model are applied to predict high-frequency and low-frequency components, respectively. ICHOA is used to optimize the parameters of the model. Finally, the final prediction result is obtained by overlaying the values of each predicted component. Through the analysis of specific examples, the proposed LSSVR-BiLSTM dual scale deep learning model is compared with other models, which can better fit the wind power data and has higher prediction accuracy and feasibility.
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    Electrical and electronic

    Intelligent path planning method for unmanned serial vehicle inspection of power transmission lines

    2023, 37 (9):  253-260. 
    Abstract ( 202 )   PDF (3097KB) ( 374 )   Save
    In order to improve the path planning efficiency of unmanned aerial vehicle (UAV) when patrolling transmission lines, a new method is proposed to enable UAV to independently carry out intelligent path planning to avoid surrounding obstacles during patrolling. By collecting the point cloud data of transmission lines, the best point cloud segmentation method is selected to perform real-time semantic segmentation of the collected point cloud data, and the point cloud data with semantic attributes are merged into voxels. Euclidean symbolic distance fields (ESDFs) are used to reconstruct the distance and angle between the obstacle surface and surrounding obstacles in real time, and the two-dimensional image recognition technology is used to accurately take target photos and identify insulators in the transmission channel to adjust the frame angle and focus of the UAV. The experimental comparison between the proposed method and some of the most advanced methods shows that the position error of the UAV automatic patrol method is less than 10 cm. Compared with manual inspection, the efficiency of the proposed method can be improved by 57.98%~62.88%, which can be applied to large-scale inspection of transmission lines.
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    Thermal defect identification and diagnosis method for substation equipment based on improved YOLO and resnet

    2023, 37 (9):  261-269. 
    Abstract ( 166 )   PDF (4103KB) ( 245 )   Save
     Aiming at the problems of large background interference in infrared images of substation equipment, various types of thermal defects, and the inefficiency of existing fault diagnosis methods, which are difficult to meet the actual inspection application requirements, a thermal defect identification and diagnosis method for substation equipment based on improved YOLO and Resnet is proposed. Firstly, construct a typical infrared image dataset of substation equipment, use convolutional kernel decomposition and multi-layer feature fusion technology to improve the YOLOv4-Tiny algorithm, locate the faulty equipment and obtain a priori frame of the equipment; Then, we propose a Res_DNet network that integrates the idea of dense connections to obtain multi-scale features of local image data within a prior frame, improving the accuracy of fault classification; Finally, Bayesian algorithm is used to improve the model hyperparameters to obtain the optimal combination of learning rate, convolution kernel number, etc., to achieve efficient and accurate fault identification and classification. The research results show that: Compared with the original algorithm, the improved YOLOv4-Tiny algorithm improves the accuracy rate by about 5.3%, and the improved Res_DNet algorithm improves the accuracy rate by more than 4.6% compared with the classical algorithm, which can realize the high-precision identification of thermal defect status of substation equipment
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    Sensorless control of PMSM based on improved sliding mode

    2023, 37 (9):  270-279. 
    Abstract ( 92 )   PDF (3904KB) ( 114 )   Save
    Aiming at the slow approaching speed of the traditional sliding mode control system of permanent magnet synchronous motor (PMSM) and the poor observation accuracy and serious chattering of traditional sliding mode observers, a PMSM vector control method based on improved sliding mode anti-disturbance controller and full-order sliding mode observer is proposed. Firstly, an improved power reaching law is designed that introduces the sliding surface segmentation function and the hyperbolic tangent function, which can shorten the approach motion time and effectively suppress the system chattering. Then, an extended sliding mode disturbance observer is introduced to observe the load disturbance and improve the anti-load disturbance ability of the system. Finally, a full-order sliding mode observer based on second-order generalized integrator is designed to realize PMSM sensorless control, a full-order sliding mode observer is used to suppress the generation of sliding mode chattering source, and an adaptive filter based on second-order generalized integrator is constructed to extract the back EMF fundamental wave, while a phase-locked loop is used to obtain motor rotor position and speed information. Simulation and experiments verify the feasibility and effectiveness of the proposed method
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    “24th International Conference of Fluid Power and Mechatronic Control Engineering” Special Column

    Stepper piezoelectric actuators with large strokes: a review

    2023, 37 (9):  280-294. 
    Abstract ( 167 )   PDF (5140KB) ( 315 )   Save
    In order to overcome the limited working stroke of a single piezoelectric element, various stepper motion principles have been proposed in recent years and various configurations of stepper-type piezoelectric actuators have been designed accordingly. The three main types of stepper piezoelectric actuators, namely the looper piezoelectric actuator, the ultrasonic piezoelectric actuator and the viscous-slip piezoelectric actuator, are highlighted, the motion principles of these three piezoelectric actuators and the current state of development of the various actuators are discussed, and the problems and shortcomings of these three stepper piezoelectric actuators are pointed out.
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    Experimental exploration of laser overlap cladding on 27SiMn steel as base material of hydraulic support column

    2023, 37 (9):  295-301. 
    Abstract ( 82 )   PDF (2734KB) ( 60 )   Save
    According to the working characteristics of the axial and tangential force and wear of the pillar of the mine hydraulic support, the hardness and wear resistance of the 27SiMn steel are investigated and analyzed in this paper, and the laser cladding technology is used to modify the surface of the 27SiMn steel. As the laser cladding lap experiment was carried out on the basis of the single channel cladding experiment, the substrate temperature had increased when cladding second channels, and the experimental conditions were slightly different from the single channel experiment. A series of laser single channel cladding experiments were carried out on the 27SiMn steel surface of the base material of the hydraulic column. Compared the test results of the thickness of the single channel cladding layer, the friction and wear experiment and the hardness, the suitable parameters were found on the surface of the base material of the hydraulic prop by 40% lap ratio. The surface characteristics of the lap joint were tested. The hardness of the lap joint was higher than the base. The material is more than 3 times, and the cladding layer is more wearable than the substrate after lap, at the same time, the thickness of the cladding layer is improved effectively. The experimental results meet the needs of the actual processing procedure, and the effective experimental theoretical foundation is laid for the laser cladding after the surface of the hydraulic column.
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    Research on location of revised oil pipeline leakage model based on WOA

    2023, 37 (9):  302-311. 
    Abstract ( 85 )   PDF (2577KB) ( 42 )   Save
    The Whale Optimization Algorithm (WOA) is then used to solve the model and determine the size and location of the leak holes. Initially, considering that the fluid flow state will change when the pipeline leaks, the shear stress is correct to obtain a more accurate oil pipeline leakage model, and then the characteristic line method is used to solve the problem. Subsequently, based on the principles of leak localization, the localization problem is transformed into an optimization problem, and develop an oil pipeline leak localization model. The WOA is then utilized to solve the oil pipeline leak model, and the results obtained under different leak conditions were compared with those obtained using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results show that the modified oil pipeline leak model was accurate, with a maximum relative error of 1.02% in the pressure distribution and 3.84% in the flow velocity distribution. Under different leak conditions, the WOA demonstrated localization errors within 1% and leak hole size errors of about 15%, which were more precise than the other two algorithms and exhibited good adaptability.
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    Numerical simulation of countercurrent evaporative flue gas total heat exchanger

    2023, 37 (9):  312-321. 
    Abstract ( 73 )   PDF (3685KB) ( 105 )   Save
     To address the problem that the heat transfer efficiency of the system is limited due to the mismatch of the heat capacity of the three fluids in the air-flue gas total heat transfer process, a counter-flow evaporative condensing total heat exchanger suitable for flue gas waste heat recovery is proposed. A water film with a very low flow rate is used instead of the intermediate fluid to remove its limitation on the heat transfer efficiency. A two-dimensional model of the heat exchanger is established and its heat and moisture transfer process is numerically simulated. The effects of Gas flow patterns,flue gas inlet flow rate, spray water mass flow rate, heat exchanger plate aspect ratio and channel spacing on the performance of the total heat exchanger are investigated. The results show that when the flow of gas is from above into below and air from below into above the flue gas, inlet flow rate is 2.5~3.5 m/s, the heat exchanger plate aspect ratio is 3~5 and the channel spacing is 5~7 mm, the heat transfer performance of the full heat exchanger is better. Under the condition that the air remains saturated, the smaller the spray water mass flow rate is, the better the performance of the full heat exchanger is.
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    The invention relates to the structure of a fluid driven pipeline robot optimization design and motion analysis

    2023, 37 (9):  322-331. 
    Abstract ( 187 )   PDF (2671KB) ( 180 )   Save
    A modular chain pipeline robot was designed based on fluid dynamics theory and oil and gas pipeline parameters. It is used to solve the problem of large circumferential volume of traditional fluid-driven pipeline robots and the difficulty of accurate prediction of frictional resistance. The robot model was established by the 3D software Solidworks, and the robot and pipe geometry were analyzed based on the robot and pipe geometry to analyze the driving force and friction variation law of the pipe robot. The new design of pipeline robot friction resistance can be detected in real time, and the maximum friction force is reduced by 40% compared with the traditional skin bowl contact support pipeline robot, and the lowest applicable pipeline flow speed is 4 m/s. The speed control scheme of the robot in the pipeline is given based on the theoretical basis of the study. The simulation of the robot differential pressure regulation effect was performed using Ansys Workbench software. Using the maximum flow rate of oil and gas pipelines and municipal water supply pipelines in service as the condition, the variation of the maximum bearing pressure displacement of sealing bowls of different structures in the robot drive unit was analyzed. It is concluded that the edge curvature inclination bowl is better than the traditional straight bowl in terms of resistance to pressure deformation and sealing performance. The edge-variable curvature inclination skin bowl is more suitable for pressure retention and sealing between the pipeline robot drive unit and the pipe wall.
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    Energy, power and environment

    Temperature control study of PEMFC thermal management system using APSO to improve BP-PID

    2023, 37 (9):  332-339. 
    Abstract ( 94 )   PDF (2630KB) ( 84 )   Save
    A thermal management control method (APSO-BP-PID) with adaptive particle swarm optimization algorithm (APSO) to improve the control method of back propagation neural network proportional integral differential (BP-PID) is proposed to address the issues of large temperature fluctuations and poor response speed in the high-power proton exchange membrane fuel cell (PEMFC) thermal management system of city buses under continuous load changes and changes in operating parameters. This improves the slow learning speed and susceptibility to local extremum problems of BP-PID, enable the fuel cell system to quickly adjust and reduce temperature fluctuations when operating conditions change. Building a model simulation on the Simulink platform, using the proposed method to control the outlet temperature and inlet/outlet temperature difference of the stack, and comparing it with two control methods, BP-PID and PID, the results show that the APSO-BP-PID method has better control effect. Compared with BP-PID and PID, the average adjustment time under continuous variable load conditions is shortened by about 59 seconds and 97 seconds, respectively, and the temperature fluctuation is relatively reduced by 46% and 40% when the working parameters change. The proposed control method has smaller temperature fluctuations and shorter adjustment time.
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    Experimental and simulation studies on the thermal characteristics of monolithic nickel-cobalt-manganese acid lithium batteries

    2023, 37 (9):  340-348. 
    Abstract ( 127 )   PDF (4595KB) ( 398 )   Save
    To investigate the thermal characteristics of NCM ternary lithium batteries under FSEC event conditions and the feasibility of their application in FSEC events.The relationship between the internal resistance and the SOC of the battery and the temperature change of the battery when discharged under specified operating conditions were first investigated experimentally.Then based on the relationship between the internal resistance of the battery and the SOC, a model of the thermal effect of the battery was developed, using this model to calculate the temperature rise and temperature difference variation of the battery.A comparative analysis of the experiments and simulations leads to the following conclusions: ① the temperature rise and temperature difference of the studied battery are positively correlated with the discharge time; ② the battery can work normally under the set normal discharge conditions; ③ the established thermal effect model is reasonable.Finally, by adding a study of the thermal characteristics of the battery when discharged under extreme operating conditions, the above conclusions are verified to be more accurate.This study will provide the appropriate basis for the design of subsequent battery thermal management systems for vehicles.
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    Study on influencing factors of particle deposition in leakage current particle sensor

    2023, 37 (9):  349-355. 
    Abstract ( 70 )   PDF (2426KB) ( 46 )   Save
     In order to meet the new requirements of particulate matter quantity monitoring in the sixth national emission regulations, it is urgent to put forward a particulate matter measurement technology with excellent accuracy and real-time correspondence adapted to the diesel engine emission environment. This paper introduces a kind of leakage current particle sensor, which uses venturi tube structure to absorb particles, and describes the value of particle concentration by the leakage current generated by charged particles in the internal concentration test area. In order to solve the problem of sensor measurement drop caused by particle deposition inside the sensor, it is necessary to understand the movement law and deposition mode of particles in the sensor, so the simulation study of the distribution law of flow field and other fields in the sensor is carried out. Based on the simulation results, the influence of particle deposition mode on the distribution of each field is studied, and the mechanism of particle deposition is obtained. The results show that the increase of temperature directly leads to the increase of three deposition rates. At the same time, the gas velocity has different effects on the three kinds of deposition velocity. The deposition velocity of electrophoretic and inertial collision is positively correlated with the gas velocity, while the deposition velocity of thermal swimming shows an opposite trend.
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    Optimization of heat storage performance of a new phase change regenerative electric heating device

    2023, 37 (9):  356-364. 
    Abstract ( 72 )   PDF (3538KB) ( 94 )   Save
    The heating method for reducing the viscosity of crude oil is mainly electric heating currently. However, all-day electric heating has the problem of high energy consumption and high cost. To meet the needs of environmental protection and industrial production, a new type of phase change storage thermoelectric heating device is designed by combining the crude oil viscosity reduction heating method with grain electricity and phase change materials. Then, the performance of phase change storage heat release of the device is studied by numerical simulation, and the influence of different inlet flow rate and the porosity of metal foam on the heat release process is analyzed. The results show that the outlet temperature decreases with the increase of inlet flow. At the same time, the addition of nickel foam makes the internal temperature of phase change materials more uniform, and the internal temperature of phase change materials decreases with the decrease of the porosity of metal foam. This paper adopts the method of increasing the number of electric heating rods and reducing the power of a single electric heating rod to optimize the structure of the device, and the local high temperature phenomenon is obviously improved after optimization. This device can not only maintain high heat transfer efficiency, but also reduce the production cost.
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