In response to the issues of hybrid tractors’ energy management strategies, such as reliance on experience, difficulty in achieving optimal control, and incomplete analysis of typical operating conditions of tractors, an energy management strategy based on dynamic programming is proposed in combination with various typical operating conditions of tractors. This is aimed at providing a reference for the modeling and energy management strategies of series hybrid tractors. Taking the series hybrid tractor as the research object, the tractor dynamics models under three typical working conditions of plowing, rotary tillage, and transportation were established. With the minimum total fuel consumption of the tractor as the optimization target, the engine power as the control variable, and the state of charge of the power battery as the state variable, an energy management strategy based on a dynamic programming algorithm was established and simulation experiments were conducted. The simulation results show that, compared with the power-following energy management strategy, the energy management strategy based on the dynamic programming algorithm can reasonably control the operating state of the engine. Under the three typical working conditions of plowing, rotary tillage, and transportation, the battery SOC consumption increased by approximately 8.37%, 7.24%, and 0.77%, respectively, while the total fuel consumption decreased by approximately 25.28%, 21.54%, and 13.24%, respectively.

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.

Architecture modeling, simulation, and test verification are performed to meet the requirements of multi-domain modeling and simulation of tractor power shift transmission (PST). The principles of architecture modeling and high level architecture (HLA) are analyzed, and the PST architecture modeling connotation and simulation system structure are studied. Then the dynamic principles of PST mechanical, hydraulic and control subsystems are analyzed in the preparation phase, sliding friction phase and holding phase of clutch engagement. To build the PST system simulation model, the simulation models and components of the three subsystems are established, and the object and interaction classes of the simulation object model (SOM) for each component are defined. After that, the parameter mapping relationship between the components is analyzed, the interfaces between the simulation components and HLA are encapsulated, and the simulation component running sequence is analyzed. Finally, the PST clutch engagement law under tractor shift conditions is simulated and tested. The relative error and correlation coefficient between the simulated and test data of shift solenoid valve drive current are used as indexes to evaluate the PST system simulation model. The results show that the simulated drive current curves are consistent with the test ones. The maximum error between the simulated and test shift time appears at the third gear downshift point, with a value of 7.89%, and the minimum correlation coefficient value between the simulated and the test drive current at all shift points is 0.92, which indicate that the PST system simulation model is effective and accurate.

In the research on energy optimization control for parallel hybrid tractors, torque has been identified as a crucial factor influencing the tractor’s fuel economy, operational efficiency, and agricultural development. This study focuses on the tractor’s overall demand torque as the primary research parameter and designs a comprehensive scheme for the parallel hybrid tractor. The study includes the design of power system parameters and the construction of a dynamic model for the entire machine. Based on this, an energy optimization control strategy, termed adaptive immune particle swarm optimization fuzzy control strategy (AIPSOFCS), is proposed. Simulation analysis is performed using representative plowing conditions, and AIPSOFCS is compared with the power follow control strategy (PFCS) and fuzzy control strategy (FCS). The results indicate that AIPSOFCS demonstrates higher fuel economy and operational efficiency compared to PFCS and FCS. In the plowing conditions, the fuel economy of AIPSOFCS is reduced by 8.45% and 2.93% compared to PFCS and FCS, respectively. In the rotary tillage conditions, the fuel economy of AIPSOFCS is reduced by 2.40% and 4.07% compared to PFCS and FCS, respectively. Finally, hardware-in-the-loop (HIL) testing of the controller confirms the effectiveness of AIPSOFCS. This research is of significant importance for enhancing the fuel economy and operational efficiency of parallel hybrid tractors and provides theoretical support and reference for the future.

Gray level co-occurrence matrix (GLCM) is an important method to extract the image texture features of synthetic aperture radar (SAR). However, GLCM can only extract the textures under single scale and single direction. A kind of texture feature extraction method combining nonsubsampled contour transformation (NSCT) and GLCM is proposed, so as to achieve the extraction of texture features under multi-scale and multi-direction. We firstly conducted multi-scale and multi-direction decomposition on the SAR images with NSCT, secondly extracted the symbiosis amount with GLCM from the obtained sub-band images, then conducted the correlation analysis for the extracted symbiosis amount to remove the redundant characteristic quantity; and combined it with the gray features to constitute the multi-feature vector. Finally, we made full use of the advantages of the support vector machine in the aspects of small sample database and generalization ability, and completed the division of multi-feature vector space by SVM so as to achieve the SAR image segmentation. The results of the experiment showed that the segmentation accuracy rate could be improved and good edge retention effect could be obtained through using the GLCM texture extraction method based on NSCT domain and multi-feature fusion in the SAR image segmentation. © 2013 Academy Publisher.

The tractor power-shift transmission (PST) research and development is a design process that incorporates many disciplines such as mechanical, control, and electronics. Modeling and simulation are typically dependent on various commercial tools for each discipline, making simulation, integration, and verification of system-level models problematic. Aiming at this, we propose a PST multi-domain co-simulation method based on the functional mock-up interface (FMI) standard, analyze the FMI-based simulation mechanism and the PST simulation system logical structure, and established the PST mechanical system model, control system model, tractor engine model, and tractor dynamic model. Based on FMI, these models are integrated into a PST co-simulation model. The starting speed, final drive half shaft speed and torque were simulated and tested. Among them, the simulation and the test starting time are 2.7s and 2.8s respectively, and the two speed curves are consistent; the simulation and the test final drive half shaft torque are approximately equal with a value of 1.5kN·m; the average Theil’s inequality coefficients (TIC) value of the simulation and the test final drive half shaft speed is 0.1375, which is less than 0.25. The results show that the simulation and the test results are consistent, the PST co-simulation model is accurate, and the co-simulation method is feasible, which can improve the efficiency of tractor PST system development.