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In order to improve the bearing performance and test accuracy of vehicle vibration test benches, a test bench which can simulate vehicle vibration environment in an all-round way is designed based on a 6-UPS parallel mechanism. Through the mechanism analysis and degree of freedom calculation of the 6-UPS parallel mechanism, the pose relationship between components is established, the kinematics equation of the bench is deduced, and the kinematic characteristics of the bench are studied. By establishing the simulation model of vehicle vibration test benches, the kinematic simulation analysis of the bench is carried out using Adams software, and the kinematic parameters of the bench are verified theoretically. The results show that the vehicle vibration test bench can move according to the predetermined trajectory, the simulation curve is smooth without mutation, and the designed bench can meet the expected performance indexes, which verifies the rationality of the bench design. This study lays a foundation for the dynamic analysis, control strategy research and optimization design of vehicle vibration test benches.

In response to the problems of limited algorithms and low diagnostic accuracy for fault diagnosis in large tractor transmission systems, as well as the high noise levels in tractor working environments, a defect detection approach for tractor transmission systems is proposed using an enhanced convolutional neural network (CNN) and a bidirectional long short-term memory neural network (BILSTM). This approach uses a one-dimensional convolutional neural network (1DCNN) to create three feature extractors of varying scales, directly extracting feature information from different levels of the raw vibration signals. Simultaneously, in order to enhance the model's predicted accuracy and learn the data features more effectively, it presents the multi-head attention mechanism (MHA). To overcome the issue of high noise levels in tractor working environments and enhance the model's robustness, an adaptive soft threshold is introduced. Finally, to recognize and classify faults, the fused feature data are fed into a classifier made up of bidirectional long short-term memory (BILSTM) and fully linked layers. The analytical findings demonstrate that the fault recognition accuracy of the method described in this article is over 98%, and it also has better performance in noisy environments. [ABSTRACT FROM AUTHOR]

To further enhance the crashworthiness of subway vehicle anti-climb energy-absorbing devices, this paper proposes a novel collapsible structure, which is embedded with honeycomb aluminum blocks and consists of an inner and outer double-layer square tube. An impact finite element model of the subway vehicle's anti-climb energy-absorbing device was established using LS-DYNA (v.17.2). The effects of the connecting diaphragm thickness, the inner and outer tube thicknesses of the thin-walled tubes, and the yield strength of the honeycomb aluminum on the energy absorption and impact force of the anti-climb energy-absorbing device were investigated. The results indicate that changes in the inner and outer tube thicknesses of the thin-walled tubes significantly affect the energy absorption and impact force of the anti-climb energy-absorbing device. However, changes in the connecting diaphragm thickness only increase the maximum energy absorption by up to 7.5% but have a significant impact on the maximum peak force. It was also found that the difference in energy absorption due to changes in the yield strength of the honeycomb aluminum is only 3.6%, suggesting that the yield strength of the honeycomb aluminum and the connecting diaphragm thickness have a limited influence on the crashworthiness design of the anti-climb energy-absorbing device. Furthermore, a multi-objective surrogate model characterizing the specific energy absorption and maximum peak force was established for the aforementioned four influential parameters using the response surface method. The model was then optimized using a genetic algorithm, resulting in optimal parameters that increased the energy absorption and specific energy absorption by 27.3% and 13.8%, respectively, compared to the original values, leading to a significant improvement in crashworthiness. The findings provide valuable references for and insights into the crashworthiness design and optimization of subway vehicles' anti-climb energy-absorbing devices. [ABSTRACT FROM AUTHOR]

Future field work tasks will require mountain tractors to pass through rough terrain with limited human supervision. The wheel–soil interaction plays a critical role in rugged terrain mobility. In this paper, an algorithm for the estimation of soil characteristic parameters based on the Simpson numerical integration method and Gauss–Newton iteration method is presented. These parameters can be used for passability prediction or in a traction control algorithm to improve tractor mobility and to plan safe operation paths for autonomous navigation systems. To verify the effectiveness of the solving algorithm, different initial values and soils were selected for simulation calculations of soil characteristic parameters such as internal friction angle, settlement index, and the joint parameter of soil cohesion modulus and friction modulus. The results show that the error was kept within 2%, and the calculation time did not exceed 0.84 s, demonstrating high robustness and real-time performance. To test the applicability of the algorithm model, further research was conducted using different wheel parameters of electric mountain tractors under wet clay conditions. The results show that these parameters also have high accuracy and stability with only a few iterations. Thus, the estimation algorithm can meet the requirements of quickly and accurately identifying soil characteristic parameters during tractor operation. A criterion for the passability of wheeled tractors through unknown terrain is proposed, utilizing identified soil parameters. [ABSTRACT FROM AUTHOR]

Unmanned tractors under ploughing conditions suffer from body tilting, violent shaking and limited hardware resources, which can reduce the detection accuracy of unmanned tractors for field obstacles. We optimize the YOLOv8 model in three aspects: improving the accuracy of detecting tilted obstacles, computational reduction, and adding a visual ranging mechanism. By introducing Funnel ReLU, a self-constructed inclined obstacle dataset, and embedding an SE attention mechanism, these three methods improve detection accuracy. By using MobileNetv2 and Bi FPN, computational reduction, and adding camera ranging instead of LIDAR ranging, the hardware cost is reduced. After completing the model improvement, comparative tests and real-vehicle validation are carried out, and the validation results show that the average detection accuracy of the improved model reaches 98.84% of the mAP value, which is 2.34% higher than that of the original model. The computation amount of the same image is reduced from 2.35 billion floating-point computations to 1.28 billion, which is 45.53% less than the model computation amount. The monitoring frame rate during the movement of the test vehicle reaches 67 FPS, and the model meets the performance requirements of unmanned tractors under normal operating conditions. [ABSTRACT FROM AUTHOR]

In recent years, more and more attention has been paid to fuel cell-based hybrid tractors. In order to optimize the global power distribution of tractors and further improve the fuel economy and fuel cell durability of the system, this paper designs an energy management strategy to maximize external energy efficiency based on fuel cell/lithium battery/supercapacitor hybrid tractors. This strategy aims to reduce the real-time hydrogen consumption of the system while maximizing the external energy output so as to reduce the impact of load randomness on the output power of the fuel cell. Under the typical ploughing conditions of the tractor, the simulation is compared with the state machine strategy and the equivalent hydrogen consumption minimization strategy. The results show that the proposed strategy meets the power requirements of a given ploughing condition, and compared with the two traditional strategy systems, the performance characteristics of auxiliary energy are more fully exerted. It reduces the burden on fuel cells and improves the durability of fuel cells. The hydrogen consumption of the system was reduced by 11.03 g and 16.54 g, respectively, improving the overall economy of the hybrid system. [ABSTRACT FROM AUTHOR]

A method for parameter identification of the magnetorheological damper (MRD) model with an improved firefly algorithm (IFA) is proposed, and a semi-active seat control system with three-degree-of-freedom (3-DOF) is established by combining with a quarter car model to investigate the ride comfort. The dynamic characteristics of the MRD were analyzed by experimental method. Combined with the IFA, the parameters of the MRD phenomenon model were identified, and the forward model of the MR damper was constructed. The semi-active control model of a 3-DOF seat suspension was established. The MRD controller and suspension system controller were designed. The passive control, PID control, and Fuzzy-PID control on the vibration reduction of the semi-active seat suspension were compared and analyzed, under different road excitation. The simulation results show that the semi-active seat suspension controlled by the PID and Fuzzy-PID can effectively reduce the seat acceleration and dynamic stroke, which significantly improve the ride comfort and operation safety compared to the passive seat suspension.

Energy management strategies are crucial for improving fuel economy and reducing the exhaust emissions of hybrid tractors. The authors study a series diesel-electric hybrid tractor (SDEHT) and propose a multi-operating point Fuzzy PID control strategy (MOPFPCS) aimed to achieve better fuel economy and improved control. To further improve the vehicle economy, the adaptive particle swarm optimization method is used to optimize the key parameters of the Fuzzy PID controller. A co-simulation model in AVL-Cruise and Matlab/Simulink environment is developed for plowing mode and transportation mode. The simulation results show that under the two operation modes, the equivalent fuel consumption of the adaptive particle swarm optimization multi-operating points Fuzzy PID control strategy (APSO-MOPFPCS) is reduced by 18.3% and 15.0%, respectively, compared to the engine single-operating point control strategy (ESOPCS). Also, it was found to be reduced by 9.5% and 4.6%, respectively, compared to the MOPFPCS. [ABSTRACT FROM AUTHOR]

Aiming at the unreasonable determination of the power coupling device speed ratio and the power battery capacity in the initial design stage of the dual-motor electric tractor, a dual-motor drive system is designed, and a parameter optimization method based on driving cycles (POMBDC) is proposed. By analyzing the driving characteristics requirements and actual working conditions of the tractor, the dynamic model of the dual-motor drive system under different working modes is established, and the parameters of the dual-motor, transmission and maximum service mass are designed. On this basis, based on the driving cycles and aiming at the lowest power consumption, the POMBDC is formed, this method can collaboratively optimize the power coupling device speed ratio and the power battery capacity. In order to verify the rationality of the POMBDC, the instantaneous optimization-constant speed ratio design method (IO-CSRDM), rule-optimization speed ratio design method (R-OSRDM) and rule-constant speed ratio design method (R-CSRDM) are developed as comparison methods, and simulation experiments are carried out. Under plowing conditions, the power battery capacity of the POMBDC is 3.08%, 5.71%, and 8.73% lower than those of the IO-CSRDM, R-OSRDM, and R-CSRDM, respectively. The power consumption resulting from the POMBDC is reduced by 3.11%, 5.74%, and 8.8%, compared with those of the IO-CSRDM, R-OSRDM and R-CSRDM, respectively. Under rotary tillage conditions, the power battery capacity of the POMBDC is 6%, 8.64%, and 11.11% lower than those of the IO-CSRDM, R-OSRDM, and R-CSRDM, respectively. The power consumption resulting from the POMBDC is reduced by 6.05%, 8.66%, and 11.13%, compared with those of the IO-CSRDM, R-OSRDM and R-CSRDM, respectively. The POMBDC can effectively increase the operating mileage of pure electric tractors and reduce costs. [ABSTRACT FROM AUTHOR]