The noise source of hybrid electric vehicle is varied, which makes the frequency band from low-frequency to high-frequency distribution. Using a single material to control the hybrid electric vehicle interior noise to improve the performance of vehicle, noise, vibration, and harshness performance is limited. In this article, the composite noise reduction materials which have the function of damping and acoustic absorption are used to control the vehicle interior noise. First, the noise characteristics of the hybrid electric vehicle are analyzed. The distribution of the hybrid electric vehicle interior noise is obtained. Then, the sound-absorbing properties of polyester-polypropylene bi-component fiber are analyzed. The low-frequency noise reduction principle of butyl rubber damping material is also analyzed. Finally, the noise reduction materials consisting of polyester-polypropylene and butyl rubber are added to the hybrid electric vehicle roof and floor. After improving material, the hybrid electric vehicle interior noise is test. As the result, the car driver’s right ear A-weighting sound pressure levels have been greatly improved. The low-frequency noise below 400 Hz reduced 1.5 dB(A), and the high-frequency noise above 400 Hz reduced 5.2 dB(A). The total noise reduction reached 3.3 dB(A) near the driver’s right ear, which significantly improve the noise, vibration, and harshness performance of hybrid electric vehicle.

Long-term exposure to high levels of vibration and noise can have detrimental effects on the health of tractor drivers. This study aimed to evaluate the subjective comfort experienced by drivers operating large-horsepower tractors. A total of 10 tractors sourced from 5 different manufacturers were subjected to testing. The assessment encompassed three operational conditions, namely, idle, maximum torque, and rated power. Objective measurements, including A-weighted sound pressure level (A-SPL), loudness, sharpness, roughness, articulation index (AI), hand vibration, and seat vibration, were collected. Additionally, subjective comfort evaluations were carried out using a paired comparison test. To predict the subjective comfort of tractor drivers, a novel prediction model was developed by employing a simulated annealing (SA) algorithm to optimize a backpropagation neural network (BPNN). The model successfully achieved accurate predictions of subjective comfort, yielding a maximum prediction error of 4.4%. The study findings revealed that vibration had a more pronounced impact on driver comfort in environments with lower-amplitude noise, whereas high-decibel noise exerted a masking effect on vibration-induced discomfort. In conclusion, the SA-BPNN model, utilizing A-SPL, loudness, sharpness, roughness, AI, hand vibration, and seat vibration as objective parameters, effectively predicted the subjective comfort of tractor drivers. This discovery holds significant implications for tractor manufacturers, who can employ the model to optimize the design of tractor cabs and enhance driver comfort.

During long-term continuous braking, high-intensity braking, or frequent braking, the temperature of the brake disc or brake drum will increase significantly, resulting in a decrease in the friction coefficient, the aggravation of the wear degree, and the dangerous heat recession of partial or even total loss of braking efficiency. This paper focuses on an innovative double rotor permanent magnet braking device, which is located on the inner side of the wheel hub, to improve the heat decay resistance of the friction braking device. The structure and principle of the double rotor permanent magnet braking device were given, and its main structural parameters were designed, calculated, and optimized. The geometric model and finite element model of the double rotor permanent magnet braking device were established. The static and transient magnetic field analysis and the braking torque characteristic analysis of the double rotor permanent magnet braking device were carried out by using Maxwell electromagnetic analysis software. The results show that the magnetic flux density in the working area of the double rotor permanent magnet braking device increases with the increase in rotation speed, the braking torque changes with the change of rotation speed, and the maximum braking torque occurs in the low-speed area, which is consistent with the theoretical calculation results. This provides a theoretical basis for the follow-up prototype test of the double rotor permanent magnet braking device.

In this paper, we address the problem of jerk and disturbance suppression during mode transitions in power-split hybrid electric vehicles. First, a transient switching model of the PS-HEV is developed. Next, the mechanisms underlying shock generation and the influence of disturbances on transition smoothness are analyzed. Based on this, a fixed-time dynamic coordinated control strategy is proposed, comprising a novel sliding mode control law and a fixed-time extended state observer. The proposed fixed-time sliding mode control law is independent of initial state values and ensures superior convergence performance. Meanwhile, the fixed-time extended state observer enables real-time estimation of external disturbances, thereby reducing the conservatism of the control law. Finally, simulation and hardware-in-the-loop results demonstrate that the proposed strategy markedly improves mode transition performance under various disturbance scenarios. This work provides a new perspective on hybrid mode transition control and effectively enhances transition smoothness.

This paper proposes a predefined time transient coordinated control strategy based on an adaptive nonlinear extended state observer (ANLESO) to address the adaptability challenges of mode transition control in power-split hybrid electric vehicles (PS-HEVs). Firstly, building upon a conventional dynamic coordinated control framework, the influence of varying acceleration conditions and external disturbances on mode transition performance is analyzed. To enhance disturbance estimation under both positive and negative as well as large and small errors, an ANLESO is developed, which not only improves the speed and accuracy of disturbance observation but also guarantees symmetric convergence performance with respect to estimation errors. Subsequently, a predefined time feedback controller is developed based on the theory of predefined time control. Theoretical stability analysis demonstrates that the convergence time of the system is independent of the initial state and can be guaranteed within a predefined time. Finally, the feasibility and superiority of the proposed control strategy are validated through Hardware-in-the-Loop (HIL) testing and vehicle experimentation. The results show that, compared with PID control based on a linear expansion state observer, the proposed strategy reduces the mode transition time by 45.7% and mitigates drivability shock by 59.2%.