To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent’s response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing.

Transmission efficiency is a key characteristic of Hydro-mechanical Continuously Variable Transmission (HMCVT), which is related to the performance of heavy-duty tractors. Predicting the HMCVT transmission efficiency is beneficial for the real-time adjustment of transmission ratio during heavy-duty tractor operations, so as to obtain better performance. Aiming at the problems of accurate method, low accuracy, and high noise in the prediction of HMCVT transmission efficiency, this paper proposes a method based on Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and Back Propagation (BP) neural networks to improve the quality of transmission efficiency prediction. Firstly, a simple theoretical model was established to obtain the influencing factors of transmission efficiency. Then, based on these factors, the transmission efficiency was tested on the bench under multiple conditions and the influence degree of each factor on transmission efficiency was divided using Partial Least Squares (PLS) method. Finally, the VMD method was used to denoise the test data, and a BP model, which was improved using the PSO method, was established to predict the processed data. The results showed that transmission efficiency of HMCVT is most affected by output speed, followed by power, and least by input speed. The VMD method can accurately extract effective signals and noise signals from the original data, and reconstruct signals, reducing the noise proportion. Using three conditions, the prediction regression accuracy of the PSO–BP model is 7.02%, 7.88%, and 9.26% higher than that of the BP model, respectively. In the three prediction experiments, the maximum differences in the MAE, the MAPE, and the RMSE of the PSO–BP model are 0.002, 0.463%, and 0.004, respectively, which are 0.006, 0.796%, and 0.003 lower than those of the BP model.

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy source, and a dual-source cooperative optimization energy management strategy was proposed. Firstly, a general energy efficiency optimization method was designed for the power source composed of a traction motor and PTO motor, and the energy source was composed of a fuel cell and power battery. Secondly, the unified objective function and constraint conditions were established, and the instantaneous optimization algorithm was used to construct the weight factor. The instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were designed, respectively. Finally, with the demand power as the transfer parameter, the instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were integrated to form a dual-source collaborative optimal energy management strategy. In order to verify the effectiveness of the proposed strategy, a rule-based energy management strategy was developed as a comparison strategy and tested in an HIL test under plowing and rotary plowing conditions. The results show that the average fuel cell efficiency of the proposed strategy increased by 7.86% and 8.17%, respectively, and the proposed strategy’s equivalent hydrogen consumption decreased by 24.21% and 9.82%, respectively, compared with the comparison strategy under the two conditions. It can significantly reduce the SOC fluctuation of the power battery and extend the service life of the power battery.

To address the issue of over-regulation of the temperature of a liquid-cooled power battery thermal management system under the plowing condition of electric tractors, which leads to high energy consumption, a nonlinear model prediction control (NMPC) algorithm for the thermal management system of the power battery of electric tractors applicable to the plowing condition is proposed. Firstly, a control-oriented electric tractor power battery heat production model and a heat transfer model were established based on the tractor operating conditions and Bernardi’s theory of battery heat production. Secondly, in order to improve the accuracy of temperature prediction, a prediction method of future working condition information based on the moving average theory is proposed. Finally, a nonlinear model predictive control cooling optimization strategy is proposed, with the optimization objectives of quickly achieving battery temperature regulation and reducing compressor energy consumption. The proposed control strategy is validated by simulation and a hardware-in-the-loop (HIL) testbed. The results show that the proposed NMPC strategy can control the battery temperature better, that in the holding phase the proposed control strategy reduces the compressor speed variation range by 24.6% compared with PID, and that it reduces the compressor energy consumption by 23.1% in the whole temperature control phase.

To solve the problems of low energy utilization efficiency, high equivalent hydrogen consumption, and significant power battery capacity degradation in hydrogen fuel cell tractors under row-crop seeding operations, a High-Efficient Low-Degradation Energy Management Control Strategy based on the Deep Deterministic Policy Gradient (HELD-DDPG) was proposed in this paper. Firstly, a tractor dynamics model was established based on the row-crop seeding operations. Subsequently, a power battery model was established based on the equivalent internal resistance theory to evaluate the state and energy consumption of the power battery, and a battery life model was established that accounts for the impact of capacity degradation on long-term system operation. A hydrogen fuel cell model was constructed according to the characteristics of the hydrogen fuel cell. To achieve a unified quantitative assessment of energy consumption across different energy sources in the entire power system, an equivalent hydrogen consumption model was established. On this basis, the HELD-DDPG was proposed with the objectives of minimizing the equivalent hydrogen consumption of the whole machine, stabilizing the SOC of the power battery, and minimizing battery capacity degradation, subject to the battery discharge current constraint. Finally, a rule-based energy management control strategy was selected as a comparative strategy. Simulation experiments on the HELD-DDPG were conducted using the MATLAB platform, and the effectiveness of the strategy was further validated through the hardware-in-the-loop (HIL) test platform. The simulation results showed that, compared with the comparative strategy, the HELD-DDPG significantly improved energy utilization efficiency of the tractor. Specifically, the chemical energy consumption of the hydrogen fuel cell was reduced by 45.43%, the cumulative energy throughput of the power battery was reduced by 32.92%, the total equivalent hydrogen consumption of the power system was reduced by 15.57%, and the battery capacity degradation was reduced by 19.76%. Meanwhile, the real-time implement ability of the HELD-DDPG was evaluated through the HIL test.