To address the lack of in-depth analysis in existing research on the dynamic amplitude-frequency characteristics of bushings and their influence mechanisms under time-varying preload effects during braking, a systematic research methodology is proposed. This methodology integrates multi-body dynamics load calculation, bench testing, and full-vehicle validation. On this basis, a design optimization strategy for the bushing's limiting direction is developed based on its dynamic loading response under braking conditions. Dynamic simulation is used to calculate the loads on the rear bushing under different braking intensities. A rubber bushing testing machine is employed to measure the dynamic stiffness and loss angle under preload. Combined with finite element simulation and full-vehicle road tests, the influence mechanism of the Y-direction dynamic characteristics of the rear bushing on brake judder is systematically revealed. The results show that when the braking intensity increases from 0 g to 0.25 g, at a frequency of 15 Hz, the Y-direction dynamic stiffness of the rear bushing increases from 729 N/mm to 1 970 N/mm, and the loss angle decreases from 6.8° to 5.8°. The longitudinal rigid-body modal frequency of the front suspension increases from 11.5 Hz to 14.5 Hz. When this modal frequency exceeds the upper limit of the excitation frequency of braking torque fluctuations (17.4 Hz), brake judder disappears. This indicates that brake judder mainly occurs under low-to-medium braking intensity conditions. By reducing the compression-side clearance of the bushing during braking and optimizing the rubber compound formulation, the dynamic stiffness and loss angle at critical frequencies are significantly improved. The vibration response of the steering wheel is effectively suppressed.

To address the issues of high modification cost and long development cycles associated with control strategy changes in the thermal management system of battery electric vehicle air conditioning, a collaborative modeling and coupled simulation study of physical mechanism models and control strategies is conducted based on the Modelica language. A high-fidelity white-box physical model is constructed, which covered key components: vehicle air conditioning, passenger cabin, and control strategies. Through a unified Modelica simulation architecture, a fully coupled closed-loop simulation is achieved, integrating thermal, fluid, and control domains. The model is validated using real vehicle test data under a winter heating and dehumidification condition. Results show that in the independent simulation, steady-state temperature errors of the physical system are within ±2 ℃. The responses of valve opening and compressor speed agreed with measured data. In the coupled simulation, the steady-state deviation of compressor speed is below 2%, and the discharge pressure deviation is less than 6%. The steady-state error of pipeline temperature is within 2 ℃. The transient response trend agreed well with the real vehicle data.

To address the problems of increased driving resistance and degraded battery capability under low-temperature conditions, an energy management strategy based on adaptive power regulation at low temperatures is proposed. The variation patterns of vehicle driving resistance and battery energy under low-temperature conditions are analyzed. Based on the recent temperature variations during vehicle operation, the SOC (state of charge) balance points for different operating conditions at low temperatures are optimized, and the engine is controlled to start in advance to supply energy for the vehicle. The driving resistance model under low-temperature conditions is reconstructed. Taking the SOC change rate per unit mileage determined by real-time driving conditions as the input, a dynamic regulation model for the power generation of the range extender based on a power penalty coefficient is constructed, and the output speed and torque of the range extender are controlled in real time in a closed loop. Vehicle tests are conducted on public roads under low-temperature conditions. The test results show that the engine can be started in time to supply energy for the vehicle when the SOC is above 25%; compared with the strategy at normal temperature, the proposed strategy starts the engine earlier and slows the battery discharge rate, ensuring a longer driving range under low-temperature conditions. In low-temperature driving scenarios (-20 ℃ to -35 ℃) at vehicle speeds of 80, 100, 120 and 140 km/h, the battery SOC is maintained with slight recharging and without depletion; during the process, the range extender operates normally, no engine runaway or rotational speed overshoot is observed, and the battery is free from overcharging or over-discharging, providing a sound foundation for the driving and heating requirements of users under low-temperature conditions.

Under the L3 level automatic driving condition, the driver is in the state of leaving the driving link. Real time detection of the driver's non-driving task is an important mechanism to ensure the safe operation of the L3 level automatic driving system for the automatic driving system to understand the driver's state and evaluate the driver's emergency ability according to the driver's state. For the detection and evaluation of drivers' non-driving tasks, this study built L3 level automatic driving conditions in the real vehicle cockpit driving simulator, carried out real non-driving task collection tests on 40 subjects, carried out 10 kinds of real non-driving tasks experiments such as mobile device operation and turning around to pick up items, and constructed the corresponding real operation data set. By integrating wavelet transform convolution into MobileViT architecture, combined with backbone network optimization, embedding position adjustment and convolution kernel parameter reconstruction, the MobileViT-WT model is established by integrating multi-scale processing and enhanced local global attention mechanism. The test results show that the model achieves 93.85% classification accuracy for non-driving tasks on the self-built data set, and the accuracy of the generalized test set is 82.90%, which is 5.15% higher than the original model, and the number of parameters is 11% lower than the original model. The reasoning delay of the model processing image is only 8.0 ms. The research results can provide optimization for man-machine takeover and safety early warning of L3 automatic driving.

Aiming at the problem that the Automatic Emergency Braking (AEB) system in mixed traffic scenarios between vehicles and two-wheelers exhibits delayed response and difficulty in simultaneously ensuring safety and comfort when responding to vulnerable road users such as two-wheelers, this paper proposes a control strategy integrating trajectory prediction and an adaptive Time-to-Collision (TTC) threshold. By employing a self-attention mechanism and an encoder-decoder architecture, the trajectory of two-wheelers is predicted. Based on the prediction results, a control logic with two-level braking thresholds is designed: the first-level braking strategy adopts fuzzy control and threshold constraints to ensure occupant comfort, while the second-level braking is dynamically triggered according to collision risk. The model-in-the-loop simulation and hardware-in-the-loop validation results over the vehicle speed range of 30~85 km/h demonstrate that, compared with the conventional strategy, the proposed strategy achieves an average improvement of 24.52% in speed reduction; when the road adhesion coefficients are 0.5 and 0.35, the collision speeds of the proposed strategy are 26.75 km/h and 31.14 km/h, respectively, lower than the corresponding 40.60 km/h and 52.30 km/h obtained with the conventional strategy.

This paper presents a systematic comparative study on the high-frequency noise of permanent magnet motors driven by two-level and three-level inverters. First, the voltage harmonics under space vector pulse width modulation (SVPWM) are analyzed through theoretical derivation, identifying the dominant voltage harmonic components and their variation with the modulation index. Second, based on the frequency and phase sequence of current harmonics, the resulting radial forces of spatial order 0 and spatial order 2p are derived. Finally, simultaneous current and noise measurements are conducted on a test bench to compare the PWM current harmonics and sideband noise of the two inverter types over a wide speed range. The results indicate that for noise near the first carrier frequency, the three-level inverter does not provide advantages across the entire speed range. When the modulation index falls below 0.5, its noise performance is worse than that of the two-level inverter. For noise near the second carrier frequency, the three-level inverter consistently exhibits lower noise than the two-level inverter across all speed ranges.

To address the problem of synergistic optimization of injection parameters in the double diesel injection strategy for a methanol-diesel dual-direct injection engine, a hybrid multi-objective optimization approach coupling Response Surface Methodology, Non-Dominated Sorting Genetic Algorithm and Technique for Order Preference by Similarity to Ideal Solution with Entropy Weighted (RSM-NSGA-Ⅱ-EWTOPSIS) was employed to optimize the timing of the first diesel injection (θ_D1), the first injection energy ratio (R_D1), and the interval time between injections (I_D) in this study. Under medium load conditions at 2 000 r/min, the optimal injection parameter combination was obtained as θ_D1 = -38.9 °CA ATDC, R_D1 = 43.8%, and I_D = 16.8 °CA. Compared to the initial strategy (θ_D1 = -29 °CA ATDC, R_D1 = 43.1%, I_D = 12 °CA), peak cylinder pressure increased by 7.72% after optimization, combustion phase advanced, and the heat release process became more concentrated. Concurrently, the indicated thermal efficiency improved by 1.06%, while the emissions of NO_x and CO decreased by 9.55% and 10.4%, respectively. The results demonstrate that the proposed optimized injection strategy achieves synergistic improvements in thermal efficiency and pollutant reduction.

To enhance the handling stability and active safety of Steering-By-Wire (SBW) vehicles, this study proposes a stability control strategy based on sliding mode control, integrating yaw rate and sideslip angle as feedback variables on the foundation of variable steering ratio control. A Particle Swarm Optimization-Grey Wolf Optimization (PSO-GWO) algorithm is applied to optimize the variable steering ratio with constant yaw rate gain, ensuring high sensitivity at low speeds and stability at high speeds. In addition, an adaptive global fast terminal sliding mode controller is developed to suppress chattering, which generates an auxiliary front-wheel steering angle to further improve vehicle stability. The SBW system is modeled in MATLAB/Simulink and co-simulated with CarSim under double lane-change maneuvers on high-speed/high-friction and medium-speed/low-friction roads. Simulation results show that compared with the uncontrolled case and the variable steering ratio control strategy alone, the proposed control strategy effectively improves the vehicle dynamic response and stability. Under the high-speed/high-friction condition, the lateral displacement error is reduced from 6.37% to 3.57%; under the medium-speed/low-friction condition, it is reduced from 9.74% to 2.11%. This study enhances the stability and active safety of SBW vehicles and provides valuable insights for the development of advanced SBW systems.

To address the lateral stability control problem of four-wheel steer-by-wire vehicles, a control strategy based on Piecewise Affine (PWA) identification of tire cornering characteristics is proposed. The PWA identification method is employed to model the nonlinear tire cornering characteristics, based on which a Global Fast Terminal Sliding Mode (GFTSM) lateral stability control strategy is developed. The proposed strategy adopts a coordinated active steering architecture: the front-wheel controller aims to track the desired yaw rate and calculates the active front-wheel steering angle, while the rear-wheel controller considers the tracking errors of both yaw rate and sideslip angle and generates the active rear-wheel steering angle through a weighted fusion approach. Co-simulation results using MATLAB/Simulink and CarSim demonstrate that, under the low-adhesion double-lane-change condition, the proposed method reduces the maximum sideslip angle by 57.6%, 73.5%, and 98.2% compared with the PWA-based Linear Quadratic Regulator (LQR) control, linear tire model-based GFTSM control, and proportional control, respectively. Under the high-adhesion large-steering-angle condition, the peak sideslip angle is reduced from 4.852 3° to 0.065 9°, while the peak yaw rate is decreased from 19.53 (°)/s to 16.07 (°)/s. These results verify that the proposed method can effectively suppress vehicle lateral state fluctuations and improve the lateral stability of FSV under extreme operating conditions.

To address the difficulty in coordinating path tracking accuracy and lateral stability of multi-axle steering vehicles under complex conditions such as high-speed driving and low-road adhesion, this paper proposes a path-stable tracking control method that integrates fuzzy control with integral sliding mode control. The proposed method calculates the front-wheel steering angle based on a driver preview model to track the desired path, and obtains the desired yaw rate and desired sideslip angle through a linear two-degree-of-freedom vehicle model. In the controller design, the yaw rate error and sideslip angle error are weighted and combined into a composite control error, based on which an integral sliding mode controller is constructed. Active rear-axle steering is then used to coordinate path tracking performance and vehicle stability. A fuzzy controller is further introduced to adjust the weighting coefficient online according to vehicle state errors, thereby improving the adaptability of the controller under different driving conditions. Simulink-TruckSim co-simulation results show that, under the high-speed and high-adhesion condition, compared with the conventional sliding mode control method, the proposed method reduces the maximum, average, and root mean square path tracking errors by approximately 26.5%, 43.3%, and 28.3%, respectively. Under the medium-speed and low-adhesion condition, the average and root mean square errors are reduced by approximately 24.2% and 3.8%, respectively. A low-speed double-lane-change real-vehicle test further verifies the feasibility and path tracking effectiveness of the proposed method.