Aiming at solving the issues of slow response， system oscillation， large temperature fluctuation and strong coupling of the proton exchange membrane fuel cell thermal management system， this paper proposes a coolant flow following the current control， optimizes the PID control strategy of neural network based on adaptive particle swarm algorithm， and constructs a 125 kW proton exchange membrane fuel cell thermal management system on the Matlab/Simulink platform to analyze the flow distribution and heat exchange between components. Compared with PID control strategy optimized by neural network and traditional PID control strategy under different working conditions. -The simulation results show that the control strategy proposed in this paper can realize the decoupling of cooling fan and circulating water pump under different working conditions； Under the step signal test， the flow rate of the circulating water pump follows the current and responds quickly； Under the dynamic performance test， the air volume control of the cooling fan is achieved without overshoot and stable within 30 s， reducing the oscillation of the system， and both the temperature difference between the inlet and outlet coolant of the fuel cell stack and the fluctuation degree of the fuel cell stack voltage is decreased. The results indicate that the proposed control strategy achieves satisfying control performance.

A vehicle stability controller is designed based on a linear quadratic regulator with integrated four-wheel steering and direct yaw moment control providing new collaborative control strategies. The control system adopts a hierarchical structure， with the upper layer being the linear quadratic regulator-based yaw moment decision layer and the lower layer being the drive force distribution layer. The upper layer controller uses an iterative method to solve the Riccati equation in real time to obtain the rear wheel turning angle and the additional yaw moment to complete the optimization of the side-slip angle of the vehicle and the yaw rate； the lower layer controller uses two distribution methods， namely， average distribution and sequential quadratic programming， to distribute the longitudinal force and the additional yaw moment to the four wheels. In order to verify the effectiveness of the controller， it is simulated and tested in real vehicles using two operating conditions： front wheel stepping angle and sine angle. The results show that the controller can make the side-slip angle of vehicle and yaw rate track the ideal value better， and the vehicle stability is improved； meanwhile， the sequential quadratic programming method reduces the tire loading rate better and increases the service life of the motor controller.

The main structure and characteristic parameters affecting the working performance of vertical screw stirring mill were analyzed and studied， and the selection method of key working parameters was proposed. Based on the discrete element method， the simulation model of vertical screw stirring mill was established， and the effects of spindle speed， agitator lead and grinding medium size distribution on grinding performance were analyzed. The comprehensive grinding performance index was put forward， orthogonal tests were carried out for each parameter， and the quantitative value of grinding effect was obtained， and then the optimal working parameter combination under specific weight coefficient was obtained， which provided a reference method for the optimal design of the mill.

In order to quantitatively analyze the influence of the randomness of the structure and material parameters of the high-speed motorized spindle system on the machining quality of the motorized spindle， a system model of motorized spindle， bearing and tool is established based on finite element method. The model considers the influence of mass eccentricity on the vibration characteristics of motorized spindle. In addition， the reliability analysis method of machining accuracy of electric spindle system is proposed by taking allowable tool tip vibration as threshold and combining with Kriging theory. Finally， the coupling effect of spindle speed and preload on tool vibration is analyzed by using the proposed method. The results show that the vibration reliability of tool tip caused by unbalance increases with the increase of spindle speed and bearing preload respectively.

In order to improve the reliability of urban road network and reduce the damage caused by cascading failure to the network， the cascading failure of urban road network is studied. Firstly， the urban road network model with double layer network structure was built. Secondly， based on the capacity-flow model and the characteristics of urban road network， the cascading failure model of urban road network was established. Thirdly， the reliability of urban road network was evaluated by four indexes： network efficiency， congestion degree， network traffic quality and normal node ratio. The reliability of urban road network under different failure strategies and congestion failure thresholds was analyzed. Finally， the Shanghai regional road network was taken as an example to carry out a case study. The simulation results show that the reliability of the network is lower under selective attack. The smaller the failure threshold， the higher the reliability of the network， and the stronger the cascading failure resistance. The results of this paper have important practical significance to improve the reliability of urban road network and enhance the cascading failure resistance.

The study aims to optimize the uniformity of bolt preload by constructing elastic interaction models and optimizing the flange connection with convex surfaces. The elastic interaction coefficient method is extended to multiple tightening processes， and the initial preload of all bolts at each tightening step is predicted by the analytical model. A three-dimensional finite element model is established to verify its accuracy and explore the effects of tightening sequence and steps. The results show that the symmetric tightening method of three steps minimizes the elastic interaction and improves the uniformity of preload. The model considering elastic interaction further optimizes the uniformity， closer to the target value. This method effectively solves the problem of uneven preload and provides scientific basis for the safety and reliability design of bolt flange structures.

Based on the Bunsen flame method and digital image processing technology， the ground-based experiments for measuring the flame speed of methane and ethylene were carried out using combustion science rack aboard china space station， to provide ground data for future comparison of space-ground （microgravity-normal gravity） combustion experiments， and to lay the foundation for the on-orbit combustion scientific research of Combustion Science Rack. According to the results， as the equivalence ratio increased， the flame speed increased first and then decreased， but the flame height decreased first and then increased， both of which reached their maximum or minimum value at around the equivalent ratio of 1.04. The maximum flame speed of methane premixed combustion was approximately 37.36 cm/s， while that of ethylene premixed combustion was approximately 67.58 cm/s， with the latter being about 1.81 times higher than the former. As the equivalence ratio was constant， the variation of outlet flow rates and Reynolds number of unburn air-fuel mixture gave a noticeable change in flame height but did not affect the flame speed.

In order to control the residual displacement of connected wall structures with connected beam metal dampers and promote the formation of the overall yield mode of the structure system， a metal damper based on the combination of sliding joints is proposed in this paper. The seismic performance of the damper is studied comprehensively by pseudo-static test， and the characteristics of the phased yield and stiffness enhancement are verified. The experimental results show that the damper can not only achieve the effect of step-by-step yield and stiffness enhancement， but also has good deformation capacity and energy dissipation performance， and can be optimized according to the parameters of slip distance and rectangular shear plate thickness. Further， a numerical comparison model of a new type of energy-dissipating beam-jointed wall structure system is established for dynamic time-history analysis. The results show that the proposed damper can control the residual displacement by strengthening the second-order stiffness in rare earthquakes， which is conducive to strengthening the collapse resistance of the structural system under extreme earthquakes and achieving the purpose of easy repair after extreme earthquakes.

To provide theoretical data for the spiral stirrup-corrugated pipe grout splicing used in prefabricated structures field， 21 anchorage pull-out specimens were designed and fabricated. The failure mode，load-displacement curves and the influence of different influencing factors on the bond anchorage strength of the specimen were analyzed by the pull-out test.The test results show that the failure modes of the specimens include rebar pull-out， splitting pull-out， rebar fracture and splitting fracture. The bond strength decreases with the increase of anchorage length and rebar diameter.The ultimate bond strength varies nonlinearly with the diameter of the corrugated pipe as the diameter increases. The average ultimate bond strength increases with increasing grout strength and the anchorage length can be further reduced to 5d.In engineering applications，when the rebar diameter is no more than 16 mm，the anchorage length is recommended to be 5d to 7d and the corrugated pipe diameter to be 2.5 to 2.8，and when the grout strength f_cu≥80 MPa， the anchorage length is not less than 5d.

To determine the repair cycle of H-shaped finned elliptical tubes， a discrete phase model DPM was used to conduct three-dimensional numerical simulation of smoke erosion of H-shaped finned elliptical tubes and obtain the wear characteristics of the wall. And the wear law of the main pipe wall and the fin wall surface due to the ratio of long to short axis， fin thickness， and fin pitch was studied. The results show that the wear rate of the main pipe wall and fins increases with the ratio of the long and short axes， and the fin pitch increases， showing a continuous decreasing trend in wear rate. As the thickness of the fins increases， the wear rate of the fin wall surface continuously increases. By changing the fin spacing， the wear rate of the base tube wall can be reduced by 46%. By changing the thickness of the fins， the wear rate of the fin wall can be increased by 212%.

In order to enhance the load-bearing capacity of single-layer corrugated steel plates （CSP）， bolts were welded on the CSP as shear connectors， and then a concrete layer was poured on the CSP to jointly bear the force. Three concrete-reinforced corrugated steel plate assemblies were statically loaded to analyze the effect of different concrete thicknesses on the force behavior of the structure， which verified the reliability of welded studs as shear connectors. The test results show that compared with a single corrugated steel plate， the load-bearing capacity of the composite slab was increased by 60%-111%， and flexural stiffness was more than 4 times. Based on the force performance of corrugated steel plate-concrete （CSPC） composite slab， the calculation method applicable to the flexural stiffness of corrugated steel composite slab was proposed. The numerical model were established in the ABAQUS software to verify the reliability. This study provides design references for the application of concrete reinforced corrugated steel composite structures in long span bridges and culverts.

To satisfy the testing requirements for intelligent vehicles and pedestrians interaction under urban conditions， a scenario generation method that comprehensively considers the appearance frequency of scenarios in the real world and their challenges to intelligent vehicles performance is proposed. First， the original scenarios are extracted from the natural driving dataset. Then a critical scenario extraction method based on importance sampling theory is designed to extract essential scenarios from the original scenarios according to the accelerated test requirement， and pedestrian crossing road scenarios based on natural driving data are constructed. Finally， comparing the distribution of essential scenarios and original scenarios，the results show that this method can effectively screen out scenarios that may pose challenges to intelligent vehicles safety and it also realizes accelerated testing while retaining the statistical characteristics of test scenarios.

To finely grasp the curb parking demand pattern at a micro-spatial scale， quantile regression models for two typical scenarios， namely workdays and weekends were proposed， to explore the nonlinear effects of land utilization， curb parking spaces， traffic factors， and socioeconomic and population on curb parking space occupancy. The results show that quantile regression is superior to linear regression in capturing the complex relationship between variables. And the coefficients and significance levels of explanatory variables change as the quantile changes. At the same time， there is an obvious heterogeneity between workdays and weekends. For example， the number of bus stations around the parking sites is closely related to the low and high occupancies， but this variable has no statistical significance under the medium occupancy. The catering services POIs do not significantly influence curb parking space occupancy on workdays.

Due to the complex and dynamic spatio-temporal correlation of traffic patterns leads to the inadequacy of existing methods to learn traffic evolution in terms of structural depth and prediction scale. a Deep learning model combining CapsNet and deep bi-directional LSTM （D-BiLSTM） was proposed. This model was used to identify the spatial topology of road networks and extract spatial features using CapsNet,was fused with the D-BiLSTM network, taking into account both the forward and backward dependencies of traffic states, and capturing the bi-directional temporal correlations of different historical periods, to forecast traffic on large-scale complex road networks in the target region. Experiments conducted on real traffic road network speed datasets show that the prediction accuracy of the proposed model is improved by more than 10% on average， outperforming other methods， with high prediction accuracy and good robustness in traffic prediction of regional complex road networks.

By introducing the survival analysis， the waiting tolerance time of non-violation and violation electric bicycle riders was taken as deleted and complete data respectively. The violation rate function of electric bicycles was developed to estimate the violation rate under the influence of single factor. Secondly， part of the waiting time was assumed with a distributed form， and a Cox proportional risk regression model was proposed that integrated the characteristics of parametric and non-parametric methods to describe the violations of electric bicycle under the influence of multiple factors. The waiting behavior data of 1 335 non-motor vehicles were extracted， and by using the product limit estimation method， the violation rate function of electric vehicles under the influence of gender， age and non-motor vehicle type was obtained. The results showed that the violation rate of middle-aged and elderly riders is higher than that of young riders； the violation rate of female riders is higher with the waiting time is less than 25 s； the violation rate of electric bicycles is higher than that of traditional bikes and e-tricycles when the waiting time is less than 44 s or between 88 s and 100 s. Unlike the "bandwagon" effect of traditional bicycle violations， when the non-motorized lanes are narrow or the group size is large enough， electric bicycle violations can be suppressed. The escort and dedicated left turn phase can effectively reduce the violation rate of electric bicycles.

Co-driving autonomous vehicles have manual and automated driving mode. The driving behavior characteristics of co-driving autonomous vehicles will show new characteristics. The driving behavior data of 15 drivers are collected. The characteristic map of driving behavior is constructed by using symbol aggregation approximation method and video recording calibration method. The common behavior sequence of autonomous vehicle man-machine takeover is excavated based on longest common subsequence algorithm. The results show that when obstacles appear in front of the autonomous vehicle the common behavior sequence is： looking ahead and pressing the brake pedal - observing the left rearview mirror and turning on the left turn signal - turning the steering wheel to the left - closing left turn signal. This study may helpful to improve the effectiveness of autonomous vehicle takeover.

To explore the application of hammer system in bridge dynamic test， simply supported bridge with standard span were selected. Research on dynamic load allowance （DLA） test of simply supported bridge were carried out， adopting theoretical derivation， numerical simulation and experimental verification. Firstly， the vehicle-bridge-suspension system coupled vibration theoretical equation was derived according to d'Alembert principle. Then， the analysis program was compiled based on ANSYS to obtain the DLAs of suspension hammer method. The influence of suspension length， wire diameter， elastic modulus and suspension hammer mass on the accuracy of DLA were discussed， comparing with the results of scaffolding method. Furthermore， combining with response surface methodology， the suggested value of suspension hammer system parameters about different bridges types were proposed. Finally， a 30 m simply supported box girder bridge was tested under dynamic load. Dynamic deflection of the bridge was tested by scaffolding and suspension hammer method to verify the suggested value. Results demonstrated that there is a significant interaction between wire diameter and suspension hammer mass. To satisfy the difference of less than 5% between deflection DLA measured by suspension hammer and scaffolding method， the optimal values of wire diameter and suspension hammer mass should increase with wire length.

In order to improve the crack resistance of wet joints in prefabricated small box girder Bridges and simplify the site construction process， a construction form and design method of UHPC wet joint without formwork was proposed and its bending resistance was studied. Based on the assumption of flat section， the condition of cross-section equilibrium， the constitutive relation of steel bar， UHPC and concrete， a method for calculating the flexural capacity of the UHPC-NC composite section considering the contribution of UHPC in the tensile zone was proposed. The calculated value of the formula was compared with the finite element calculation. Based on the design method of UHPC wet joint without formwork， the feasible values of each parameter under the section form were obtained， and the functional relationship between the width of the wet joint rectangle strip and the calculated span of the small box girder bridge panel was obtained by fitting. Meanwhile， based on the finite element method， the numerical model of UHPC wet joint without formwork was established， the influence of the structure form of wet joint and the width of wet joint on the flexural capacity of UHPC wet joint was analyzed. The results show that the calculated value of the formula and the finite element calculation are in good agreement， which can provide guidance for the design of wet joints. The wet joint without formwork can give full play to the excellent mechanical properties of UHPC， inhibit the development of cracks， improve the durability and bearing capacity of bridge deck， and speed up the construction efficiency. The minimum width of the wet joint without support mold is 140 mm at the condition of the transverse steel bar diameter of the small box girder bridge is 16 mm， and the construction performance and economic performance of the precast bridge panel can be played.

In order to analyze the possibility and riskiness of accidents caused by different abnormal driving behaviors， quantify the threshold size of acceleration and deceleration under different driving behaviors. Based on the trajectory data of the vehicle， the interaction relationship between speed and acceleration was analyzed， and the function relationship between speed and acceleration was calibrated using regression， and finally the judgment model of different abnormal behaviors was constructed. The results show that the sum of the mean acceleration and 1 times the standard deviation can be used as the general abnormal behavior judgment threshold， in this range drivers have the potential to create risk and need to be reasonably alerted； The sum of the mean value of acceleration and 2 times the standard deviation can be used as the threshold for judging extreme abnormal behavior， in this range of drivers already has some risk and requires some control. The model overcomes the previous requirement of using fixed thresholds and allows for real-time dynamic monitoring of driving behavior， providing a new approach to the study of microscopic driving behavior.

Based on smart card data （SCD） of urban rail transit， a method was proposed to identify travel patterns by modeling the spatio-temporal sequences of individuals. Firstly， all stations visited by a passenger individual were extracted， and the similarity of stations was calculated in terms of inter-station travel frequency， inter-station distance， and the station activity duration， thus the main spatial activity areas of this individual were classified using a hierarchical clustering algorithm. Secondly， the spatio-temporal sequence was inferred based on the travel order of the individual， which is a set of discrete values characterizing the spatio-temporal state. PCA-KL and K-Means++ were used to extract the similarity sequence structure to identify passenger travel patterns. Finally， using one-month SCD as an example to identify passenger travel patterns for Xi'an rail transit. The results show that the complex passenger flow has five travel patterns， among which three typical travel patterns are macroscopic commuter travel behavior， accounting for 79% of the total passenger flow. Thus， the pattern identified based on the similarity of individuals' travel spatiotemporal sequences fully reflects the particularity and universality of the research method and it is highly operable for different cities.

To establish a crack width calculation formula applicable to reinforced ultra-high performance concrete （UHPC） beams， a crack width calculation method considering bond slip effects was proposed. Based on the four-point flexural tests of 10 UHPC-T beams with steel fiber content， reinforcement ratio and protective layer thickness as parameters， the effects of each parameter on the failure pattern and maximum crack width of the test beams were studied. The equations for calculating the crack width of reinforced UHPC beams were established by using the balance and deformation differential governing equations based on microelements and the interfacial bond slip relation considering many influential factors. The correctness and applicability of the formula for calculating crack width were verified by comparing with the experimental and literature values. The results show that the established formula can fully reflect the tensile contribution of steel fiber and the influence of interface bonding and sliding properties of steel bar and UHPC. The calculated values are in good agreement with the experimental values， and the crack widths of reinforced UHPC beams can be accurately calculated.

In order to research on the mechanical properties of desert sand concrete （DSC） after high temperature， desert sand from Mu Us sandy land was used to partially replace medium sand to make DSC. The uniaxial compression experiments of DSC subjected to different loadings and temperatures were carried out to obtain the stress-strain curves. The influences of loading level， temperature and cooling methods on the mass loss rate， ultrasonic velocity and axial compressive properties were analyzed. Experimental results showed that the mass loss rate of DSC gradually increased with the temperature. The uniaxial compressive strength and modulus of elasticity of DSC declined， peak strain increased greatly， and the stress-strain curve gradually became flat. Taking into account of temperature and loading level， the constitutive model was established to simulate the mechanical properties of DSC based on the two-stage constitutive model， which provided technical support for the performance evaluation of DSC after high temperature.

In order to explore the influence of rice straw ash on the properties of ordinary concrete， the compression test was carried out with rice straw ash concrete with different curing days and different substitution rates （0%， 5%， 10%， 15%， 20%） as the research object. Poisson's ratio is used as an evaluation index to reveal the influence of rice straw ash on the properties of concrete， a constitutive model of rice straw ash concrete is proposed and established， the compressive failure mode of rice straw ash concrete specimens is analyzed by finite element method， and the microscopic analysis is carried out by scanning electron microscope， the results show that compared with ordinary concrete， with the increase of substitution rate， the stress-strain curve of rice straw ash concrete cured for 28 days is significantly different， and the peak stress， The peak strain decreased to varying degrees， the curve changed slightly after 128 days of curing， the peak stress and peak strain increased to different degrees， and the peak strain increased by 12.24% when the substitution rate was 5%， and the long-term strength was the best. The results of this study can provide technical support for the industrial application of rice straw ash concrete， and provide a theoretical basis for the utilization of rice straw ash by-products produced by biomass power plants to promote agricultural cycle and increase agricultural income.

Due to the high heterogeneity of semi-structured data and the huge amount of data， the data structure of different sources is inconsistent， resulting in low accuracy and integrity of data extraction. To this end， machine learning and deep learning are deeply integrated， and an extraction algorithm for large-capacity semi-structured data is proposed. By using the principal component analysis method of machine learning， the dimensionality of large volume semi-structured data is reduced. The converter network structure based on deep learning improves the embedding layer， encoding layer - decoding layer and encoding layer respectively， and obtains two kinds of data extraction algorithms for identifying the named entity of data and extracting the relationship of data entity， so as to realize the extraction of large-capacity semi-structured data. The test results verify that the proposed algorithm has a significant effect on correct extraction， the minimum extraction amount of invalid data items is only 4， the extraction complexity is low， and the aging value is high. The ablation experiment results of F-value and extraction time fully prove that the fusion of the two technologies is of great significance to the research of data extraction， and the F-value is always kept above 92， and the extraction time is shortened to 125 ms. It has strong feasibility and provides an important means for improving operational efficiency and optimizing resource allocation.

Aiming at the problems of poor underwater visibility， low accuracy and slow speed of object extraction， a yellow catfish object extraction model based on RGHS algorithm and improved YOLOv5 was proposed. Firstly， in order to solve the image quality problems caused by uneven illumination and high noise， RGHS algorithm was used to enhance the brightness of yellow catfish image. Then， C3ghost and CA attention mechanisms were introduced into the YOLOv5 backbone network， and g^nConv was used to replace the common convolution in the neck part， so as to establish an improved YOLOv5 model and improve the target extraction accuracy of yellow catfish. The results show that compared with YOLOv5， the AP value， accuracy rate and recall rate of the improved model are increased by 2.76%， 3.16% and 3.1 %respectively， the F1 value is increased by 0.03， the memory occupied by the improved model is reduced by 2.3 MB， and the reasoning time of a single image is reduced by 0.001 s. Meanwhile， compared with the YOLOv4， SSD， Faster-RCNN and YOLOx models， the AP values of the improved models are increased by 3.27%， 8.63%， 2.48% and 2.52% respectively. The improved YOLOv5 model based on RGHS image enhancement can significantly improve the target extraction accuracy of yellow catfish while maintaining a fast speed， which can provide useful reference for the study of fish status monitoring methods.

Aiming at the problems of traditional object detection methods on remote sensing images based on deep learning networks need hand-crafted architectures， which are overly dependent on expert experience and time-consuming， an object detection method for remote sensing images based on neural architecture search is proposed. The network is automatically built by pathwise sampling and evolutionary search strategy for object detection of remote sensing images. Experiments on DIOR dataset and RSOD dataset show that the mean average precision of object detection reached 67.8% and 85.5%， and the FLOPs are 208.47 G and 201.67 G， which are better than the network models such as Faster R-CNN， RetinaNet， NAS-FCOS， ResNet Strikes Back， HRNet and GRoIE in terms of detection accuracy and computational efficiency. The proposed method can automatically search the network architecture for object detection of high-resolution remote sensing images， which is superior to the hand-crafted classical networks.

Aiming at the object detection problems of the existing network such as poor remote object recognition effect， insufficient object feature expression and inaccurate object positioning， a road object detection method based on the improved YOLOv5 algorithm was proposed. Firstly， the feature extraction structure of YOLOv5 algorithm was summarized， and the shortcomings of the original network structure were analyzed. Secondly， the small object detection layer was added to the original network， and the recognition ability of the network for distant objects was improved by supplementing the fusion feature layer and introducing additional detection heads. Thirdly， the original detection head was decoupled， and the expression ability of the network to object features was improved by changing the border regression and object classification process to two branches. Then， the prior box was reclustered， and the height to width ratio of the prior box was adjusted by the K-means++ algorithm to enhance the network's ability to locate the object. Finally， AP， mAP and FPS were used as evaluation indicators for ablation， comparison and visual verification experiments. The results show that the detection speed of the proposed algorithm on the BDD100K dataset is 95.2 frames per second， and the average accuracy reaches 55.6%， which is 6.7% higher than that of the YOLOv5 algorithm. It can be seen that the improved YOLOv5 algorithm not only meets the requirements of real-time detection， but also has good object detection accuracy， which is suitable for road object detection tasks in complex traffic environments， and has guiding significance for improving the visual perception ability of autonomous vehicles.

To improve transmission rates in wireless sensor network and solve problems of interference， data anomalies， collision and congestion during clustered routing， a data transmission algorithm was proposed to optimize link quality. Applying cross layer design, introducing physical layer channel information, and reducing interference between clusters during data collection through random frequency hopping of each cluster. The priority of unusual data was defined to optimize slot allocation within clusters. With the access control in MAC layer， factors affecting inter-cluster transmission was considered and a competitive scheme was developed to avoid conflicts when multiple nodes access cluster heads. It compared data queues of accessing cluster heads and pre-reserved queues of receiving cluster heads to detect congestion. And the original routing was changed to solve congestion. Experimental results show that compared to LEACH， UCDTS and FPCRA algorithms， it effectively improves the data transmission quantity， the network life cycle and other indicators.

In response to the issue of differences in driving vehicles among drivers, an H_∞ robust controller was designed to ensure the closed-loop stability of the whole system when different drivers drive the vehicle， and at the same time minimize the tracking error of the vehicle trajectory. A preview control model that can describe different driver behaviors was established， and the vehicle dynamics model and tire model were combined to design the overall system model. At the same time， the H_∞ robust controller was designed on the basis of the double-loop structure of human-machine shared control. Finally， Simulink/CarSim co-simulation platform was used to verify that the controller can improve the vehicle tracking performance and system stability.

In automotive electronic controls， safety issues are always of paramount importance. Therefore， this paper proposes a slip rate control method based on MPC to improve the longitudinal stability of four-wheel-wheel electric vehicles during emergency braking. Firstly， the vehicle longitudinal velocity estimator based on extended Kalman filter accurately estimates the longitudinal speed of the vehicle， and then the recursive least squares method based on limited memory is used to identify the optimal slip rate of the vehicle under the emergency braking condition. Secondly， the upper model predicts that the controller tracks the optimal slip rate， optimizes the braking torque required by the front and rear wheels under different road surface attachment conditions to meet the safety constraints， and the lower torque distribution controller distributes hydraulic and regenerative braking torque under the constraints of battery state of charge to improve the energy recovery efficiency. Finally， simulation experiments are carried out under different working conditions and it is shown that the proposed control system can ensure the safety and reliability of the emergency braking process.

In order to improve the fertilizer utilization rate and uniformity of fertilizer spreading in maize mid-tillage applications， a swing tube fertilizer spreading device controlled by a crank rocker mechanism without sharp return characteristics was designed. Through theoretical analysis， the dimensions of the four-bar mechanism and the main parameters affecting the uniformity of the fertilizer spreading were determined separately. The oscillating fertilizer spreading process of the swing tube was simulated using the discrete element software EDEM， and a two-factor five-level quadratic generalized rotational combination test was designed to analyze the effect of changes in swing frequency and swing tube inclination on the coefficient of variation of the uniformity of transverse and longitudinal fertilizer spreading. By optimizing the test results， the coefficient of variation of transverse and longitudinal uniformity is less than 20% when the swing frequency is in the range of 1.21~1.5 Hz， and the swing tube inclination is in the range of 36°~59°. The prototype and bench test were carried out with the optimal combination of parameters， and the relative errors of the bench test and the simulation test are 1.44% and 3.13%， respectively. The test results are consistent with the simulation results， proving that the swing tube fertilizer spreading device can achieve the purpose of precise and uniform fertilizer spreading.

For sweet potato two-stage combine harvester operation， the secondary conveyor separator device injures potato seriously， and the effect of soil and sweet potato separation is poor. A high and low interval secondary conveyor separator device was designed. By means of structural parameters and kinematic analysis， the key factors of the operational effect of the secondary conveyor separator were derived. At the same time， the value range of each influencing factor was determined. Based on a quadratic orthogonal rotation combination test， the value of the best factors for the test index was drawn. The field harvest test shows that the potato injury rate is 0.797%， and the soil removal rate is 74.839%，in the case of the speed of high and low interval secondary conveyor separator device is 0.6 m/s， the vibration amplitude is 26.2 mm， and the vibration frequency is 6.6 Hz. The results of the study can provide a reference for the structure improvement and operation parameters optimization of the two-stage sweet potato combine harvester.

Design of orchard multi-duct sprayer air delivery system for the problems of uneven airflow distribution and poor droplet penetration of traditional ring-shaped air delivery sprayers. The optimal structural parameters were determined by simulation of the internal flow field consisting of the air duct， transition interface， flexible duct and air outlet， and the optimal parameters of the fan speed and total height of the air outlet were determined by external flow field simulation. According to the simulation results， the optimal combination of parameters was 170 mm duct width， 40° conversion interface angle， 123 mm flexible duct diameter， 52 mm outlet width， 2 160 r/min fan speed， and 1.9 m total height of the outlet. The air delivery system of the sprayer was set up according to the optimized parameters and designed for the airflow velocity test， and the results showed that the simulated values were in good agreement with the test values， and the error of airflow velocity on the left and right sides of the outlet was less than 10%. The droplet penetration was good， the deviation of droplet deposition density between canopies was less than 12%， and the droplets were evenly distributed in the vertical direction of the canopy.

The bionic friction-increasing principle of locust foot pad was mainly applied to the rubber surface of the ball hinge of the thrust rod in commercial vehicles， and the bionic spherical crown-like flexible projections are constructed on the rubber ball hinge surface to increase the stiffness of the ball hinge. The height， radius and spacing of the bionic flexible projection were varied， and the radial stiffness， deflection stiffness and torsional stiffness of the thrust rod ball hinge were investigated by finite element analysis using Abaqus software. The results show that the optimum results are obtained for a projection height of 1 mm， radius of 4 mm and spacing of 10 mm. Finally， the optimal solution is analyzed comprehensively， and the feasibility of the solution is verified by simulation and experiment.