To fully utilize the energysaving and emissionreduction performance of the oilelectric hybrid system on mine truck, a dualmode energy management strategy (EMS) is proposed for a series hybrid electric mine truck. The back propagation neural network (BPNN) was used to construct the models of "optimal fuel consumption mode of engine" and "optimal efficiency mode of range extender" in this EMS. On this basis, a dualmode EMS was designed to adjust the power output of the range extender and battery pack, realizing the realtime adjustment of the energy consumption for the vehicle under complex working conditions. Finally, the proposed EMS was verified by hardware in the loop simulation with actual working condition data. The results show that compared with the rule strategy and equivalent consumption minimization strategy, the fuel saving rate of the dualmode EMS increases by 12.74% and 7.4% respectively, further improving the fuel saving performance of the realtime strategy.

Aiming at the problem of how to recover the waste heat of the motor to improve the thermal performance of the passenger cabin, a simulation model of the thermal management system of a battery electric passenger vehicle is constructed by using AMESim software. On this basis, the effects of refrigerant distribution ratio and thermal management system architecture on passenger cabin heating performance are analyzed under the motor waste heat recovery mode. The results show that at a vehicle speed of 60 km/h, the heat generation of the motor can be up to 1 402 W and the heat generation of the motor controller can be up to 427 W. Compared with the nomotor waste heat recovery mode, the total heat absorbed by the thermal management system from the electric drive system and the environment can be increased by 58.69%100.57% and the passenger cabin heating power can be increased by 71.36%100.37% by distributing the refrigerant rationally. In the motor waste heat recovery mode, the passenger cabin heating power with the parallel architecture was 23.42% to 27.23% higher than that with the series architecture.

A selfadaptive adjustment scheme is proposed to address the problem that traditional PI controllers cannot simultaneously achieve nonlinear gain transformation in both highspeed and lowspeed weak magnetic field voltage feedback loops. By analyzing the factors that lead to system nonlinearity in the decouplingvoltagefeedback currentleadangle control loop, an inverse function speed regulator and a fuzzy control current lead angle regulator have been designed. These regulators compensate for the nonlinearity of the voltage control loop under weak magnetic field conditions.Additionally, a clamping antisaturation module is developed to solve the issue of integral saturation in voltage feedback. On this basis, the particle swarm optimization algorithm is adopted to conduct offline optimization of the fuzzy rule weights and proportional factors in the fuzzy controller. Finally, simulation and experimental results show that the adaptive weak magnetic algorithm exhibits better response characteristics compared to traditional PI voltage feedback control algorithms.

Analyzing the extreme drifting conditions of vehicle tires can greatly improve the horizontal and vertical control capabilities and driving safety of autonomous vehicles. This paper first adopts the UniTire model to describe the friction characteristics in high slip regions, and then optimizes the vehicle drift control algorithm. Next, based on the vehicle stability control principle, the target pressure for the wheel cylinder was calculated to achieve a quick return of the vehicle to steady straightline driving after drifting. Finally, the rapid prototype verification was carried out on the CANoe's industrial computer platform by integrating DYNA4 and Simulink. The results show that the control algorithm proposed in this paper allows the vehicle to quickly achieve lateral and longitudinal stability during drifting and to promptly return to straightline driving after drifting, meeting the realtime control requirements.

Stress distribution at the tireground contact interface on soft terrain becomes increasingly complex under the influence of tire slip and sinkage, making it difficult to accurately model tire behavior. Using finite element analysis, the paper simulated tire longitudinal slip/skid under constant sinkage conditions. The variation in stress distribution at the tireground contact interface was investigated as the slip/skid degree changed. The results show three distinct stress distribution patterns corresponding to slip, small skid and large skid states, respectively. Soil characteristic parameters were obtained through simulating sinkage and shear tests, and the stress distribution model was established for the three slip/skid states. On this basis, the tiredeformable terrain interaction model for longitudinal slip was further developed, which effectively represents the inplane characteristics of tires on soft terrain.

Thermal oxygen aging is one of the primary thermal failure modes for nonmetallic components in the engine compartment. The traditional vehicle thermal management development process typically only focuses on the maximum working temperature of components, which is insufficient for effectively predicting the thermal degradation of these parts over their lifecycle. Based on China's climate conditions and extensive vehicle driving data, an equivalent thermal aging driving condition model is established to evaluate the thermal aging life of components. Using this method, the thermal aging life of an engine mount rubber bushing is evaluated. It is found that improving the rubber material formula increases its thermal aging life by approximately 3 times. However, if the operating temperature of the rubber bushing increases by 10 °C, its thermal aging life decreases by about 50.0%. In addition, an analysis of the heat transfer path of the rubber bushing shows that its actual working temperature can be lowered by optimizing the fan wake and reducing heat conduction between the engine and the rubber bushing, thereby improving its thermal aging life.

The paper aims to solve the problem of forecasting passenger travel demand in ehailing car operations, thereby reducing vehicle idle rates and minimizing passenger waiting times. Considering the dynamic spatiotemporal dependencies of passenger travel demand, this study proposes a method based on spatial data visualization and the Granger causality test for analyzing the spatial dependency. A spatiotemporal graph convolutional neural network model incorporating attention mechanisms is established to predict passenger travel demand. The case study shows that this model effectively captures the dynamic characteristics of the timespace dependencies of passenger travel demand, improves the prediction performance of the model, and achieves high accuracy and practicability.

Due to its low material costs and versatility in producing diverse products, 3D printing technology has attracted attention from researchers studying the crashworthiness of automotive energyabsorbing structures. This paper conducts quasistatic experimental research on automotive energyabsorbing structures, such as crash beams and honeycomb fillers, made from different base materials but with identical structural dimensions, using 3D printing technology. A comparative analysis of their mechanical responses and deformation modes is performed. Furthermore, finite element simulations are employed to study the influence of structural parameters on crashworthiness indicators. The results show that the loaddisplacement curves of 3Dprinted PLA crash beams and honeycomb filler structures exhibit similar trends to those of metal structures and reflect the deformation characteristics of automotive energyabsorbing structures. Increasing the wall thickness raises the relative density, which enhances the crashworthiness of both the crash beam and the honeycomb structure. Additionally, changes in the cell size also affect the crashworthiness of the honeycomb structure. This paper proposes an optimization scheme for automotive energyabsorbing structures based on 3D printing technology, systematically studying the effects of structural parameters on crashworthiness through experiments and simulations. This research offers valuable insights for designing educational tools in automotive engineering and analyzing automotive energyabsorbing structures.

As the core product of the hydrogen energy industry, proton exchange membrane fuel cells are characterized by high energy conversion efficiency, zero emissions and no pollution. They are one of the most promising power sources in the automotive field. However, the high cost limits the largescale application and promotion of PEMFCs for vehicles. The development of lowplatinum PEMFCs for vehicles is an important technical route to improve cost competitiveness. However, the serious mass transfer and lifespan issues faced in the lowplatinum process urgently need to be solved. This paper summarizes the research progress of lowplatinum membrane electrode assembly technology and the shortcomings of existing technologies. It also looks forward to the future development trends, providing reference value for the advancement of lowplatinum membrane electrode technology for vehicle PEMFCs.

For the control strategy of the vehicle fuel cell system, the paper summarizes the current research status and development trends in the aspects of system structure, control objects, control objectives and control methods. In terms of system structure design, the development of highperformance key components is essential to simplify the control system structure and to reduce control complexity. Concerning control objects, the decoupling control for strongly coupled physical quantities requires further indepth study. As for control objectives, multiobjective optimal control strategies will be the focus of future research. In regard to control methods, composite control strategies and intelligent control strategies based on learning will shape the future research trajectory.