For the high energy consumption problem caused by the large torque fluctuations of inwheel motordriven vehicles on uneven roads and frequent shifting of vehicles, in this paper a method for energy consumption optimization of inwheel motordriven vehicles considering torque fluctuations is proposed. Firstly, based on the longitudinal drive dynamics equation of electric vehicles and the CarSim vehicle dynamics model, the motor energy consumption model, tire slip energy consumption model, and yaw torque tracking error model are established as the upper control target, and the inwheel motor dynamics model considering road surface excitation is established as the lower control target. Secondly, taking the upperlevel control target as the objective function of the torque optimization of the inwheel motor vehicle, the motor torque and speed energy limit as the inequality constraints, and the fuzzy control method for objective function weight distribution, the upperlevel torque optimization model is established based on NSGA II. At the same time, the lowerlevel inwheel motor vector control model of torque overshoot and delay caused by road surface excitation is established based on the sliding mode antidisturbance observer. Finally, the joint simulation of Simulink and CarSim of a fourwheel inwheel motordriven car is carried out, and the changes of the front and rear axle wheel torque, total energy consumption of the car, and the SOC of the car battery under different optimization methods are analyzed under WLTC operating conditions and CLTCP operating conditions. The inwheel motor bench test shows that the energy consumption optimization method of inwheel motordriven vehicles considering torque fluctuations can effectively reduce energy consumption under WLTC and CLTC-P operating conditions.

Establishing an accurate air spring model is key and crucial for analyzing the vibration characteristics of air suspension electric buses. For the variation in the characteristics of air springs under different load, a comprehensive model of the air spring with dynamically adjustable parameters is proposed, considering the effect of rubber airbag force and changes in payload, taking a membranetype air spring as the research object. Key parameters of the rubber airbag are identified through mechanical experiments, and the accuracy and effectiveness of the proposed model are verified. Based on the comprehensive air spring model, a 14seat, 21degreeoffreedom electric bus dynamics model is established. The model's validity is verified through simulation comparison with a CarSim model with identical parameters. Subsequently, the influence of air spring nonlinear characteristics, vehicle speed, road roughness, and passenger distribution on the dynamic performance of the electric bus system is analyzed. The study shows that the proposed comprehensive air spring model can dynamically adjust its parameters in response to variation in load and road excitation. The hysteretic mechanical characteristics of the air spring cannot be ignored. Compared with linear models, thermodynamic models without considering hysteresis, and equivalent air spring model, the comprehensive air spring model significantly reduces suspension deflection, with reduction of 22.95%, 42.13%, and 18.20%, respectively. Increase of vehicle speed, lower road quality, and uneven passenger distribution negatively affect the ride comfort of the electric bus, with discomfort increasing for passengers seated farther from the bus's center of gravity.

There are many investigated parameters in the powertrain mounting system (PMS) of electric vehicles, and it involves multiperformance design. For the problem that the traditional singleoutput sensitivity analysis is difficult to accurately evaluate the influence of system parameters on the system comprehensive performance, the multioutput response sensitivity analysis of the PMS of electric vehicle is carried out by considering the uncertainty of system parameters. Firstly, a 13degreeoffreedom analysis model of PMS is established, and the uncertain parameters of system are described by the random variables. Then, based on the summation of covariance decomposition, the first order index and the global sensitivity index of the multioutput response of system are derived. Next, a method of calculating the sensitivity indexes of multioutput response is proposed based on Monte Carlo analysis. Finally, the effectiveness of the proposed method is verified by the numerical example of the PMS of an electric vehicle. The analysis results show that the single output sensitivity analysis may not be able to accurately evaluate the comprehensive influence of parameters on the system response, and it may produce contradictory results. The proposed multioutput sensitivity analysis method can effectively evaluate the comprehensive influence of system parameters on the system response, and it can obtain more accurate sensitivity ranking for system parameters.

Threedimensional terrain scenes typically possess complex and diverse environment along with jagged terrain features, which poses challenges to path planning. To address this issue, in this paper a highly reliable path planning approach under the influence of nontopographic fluid characteristics and threedimensional complex terrain is proposed. This method encompasses initial global path planning, path inspection, and replanning under 3D terrain. For the initial global path, an AHTR algorithm that combines the advantages of Hybrid A* and Theta* is proposed. This algorithm enhances the internode sampling and detection methods in accordance with the traits of the 3D terrain scene and introduces in a terrain risk assessment function.to plan a path for the vehicle that can evade rough terrain and comply with kinematic constraints. For path inspection, the path risk test function is designed based on the results of vehicle dynamics analysis considering the characteristics of nonterrestrial fluid, and the impact of nonterrestrial fluid characteristics on path planning is verified. For path replanning, an enhanced AHTR algorithm is proposed, which takes into account of both 3D terrain features and nonterrain fluid features to guarantee that the planned path can effectively avoid risks. Simulation experiments demonstrate that compared with Hybrid A* and Theta*, the intensity of ground undulation in the path planned by the AHTR algorithm is decreased by 26.54% and 49.04%, with the average pitch angle of the vehicle reduced by 44.39% and 69.40%, the path risk lowered by 26.32% and 41.67%, and the final path safety improved by 58.06% and 88.46%, which effectively ensures path reliability.

Tire matching selection is an important part of the vehicle development process. Currently, the mainstream subjective evaluation methods have problems such as poor consistency between enterprises, shortage of driver resources, and lagging evaluation nodes. In this paper, based on the subjective and objective test data of real vehicles, considering the inherent correlation between tire mechanical performance and structural parameters of vehicle handling stability, by extraction of subjective evaluation influencing factors and generation of the dimensionality reduction feature space based on correlation analysis, a subjective and objective fusion index system is established. Further, with the objective indicators and subjective rating prior trend relationship as the constraint penalty term, a tire and vehicle handling stability matching evaluation model based on subjective and objective fusion is constructed by designing an ensemble learning algorithm. The mean MSE on multi region test data is 0.247, and the predicted results show good consistency with the test results. The relevant achievements can build a quantitative evaluation system for the subjective and objective consistency of vehicle handling stability, providing support for precise selection of tire matching.

Instance segmentation algorithms based on deep learning are capable of helping intelligent vehicles to obtain accurate perception information. However, due to the limitation of manufacturing cost, the computing resources on intelligent vehicles are usually limited. In order to obtain highprecision recognition and segmentation under limited computing resources, the algorithm itself is required to make full use of the extracted features. Meanwhile, although the onestage instance segmentation algorithm has a relative fast inference speed, it has poor performance in accuracy. To this end, structural improvement based on the onestage instance segmentation algorithm SparseInst is conducted to enhance the model's utilization of effective features. Specifically, firstly, residual connection is added inside the basic building block of the backbone. Secondly, a threescale feature fusion module is designed to overcome the problem of indirect interaction of crossscale features in the encoder. A decoupled instance activation module is designed to enhance the model's ability to learn instance features. In addition, the improved algorithm makes full use of detail features to refine the mask features to improve the quality of the generated masks. Finally, the kernel is used to initialize the score of the target object, which improves the utilization rate of the extracted features. The improved algorithm surpasses similar algorithms in mask accuracy on multiple datasets and has strong realtime performance. To further verify the effectiveness of the improved algorithm, experiments using data

For the limitation of traditional type1 Smith fuzzy control in terms of inadequate time delay compensation and insufficient robustness under varying parameter driving conditions, an interval type2 Smith fuzzy time delay compensation control method is introduced for magnetorheological (MR) semiactive suspension systems. This approach incorporates the vehicle's vertical acceleration, suspension deflection, and tire dynamic displacement as the state input of the control system, enabling a comprehensive capture and response to dynamic vehicle changes. By introducing in upper and lower membership functions, the method defines clear membership intervals for fuzzy variables, which are then leveraged to calculate activation intervals under various fuzzy rules, significantly enhancing the system's antiinterference capability. Additionally, the Centerofsets algorithm is innovatively introduced into the fuzzy reduction process, avoiding redundant normalization calculation during the type reduction of type2 fuzzy sets, thereby improving the system's execution speed and realtime performance. Simulation results demonstrate that the proposed interval type2 Smith fuzzy delay compensation control strategy achieves improvement in both control effectiveness and robustness for MR semiactive suspension systems, effectively tackling complex and varied driving environment.

Distributed electrically-driven heavy-duty vehicles achieve compound steering through the differential between the steering assist motor and the wheelend drive motor. By means of coordinated control of multiple motors, various active safety control functions are realized and the driver's operational burden is reduced. For the driving safety issues brought about by the failure of the drive motor and the steering assist motor, in this paper a faulttolerant control strategy encompassing mode-switching and faulttolerant torque distribution is proposed. The proposed modeswitching strategy, based on the vehicle pose information, introduces in the yaw rate residual function as the switching condition for the faulttolerant mode. The proposed faulttolerant torque distribution strategy takes into account of the output redundancy and the vehicle's stability to solve for the target output torque of the drive motor and the steering assist motor. Finally, a hardware-in-the-loop simulation platform is established to verify the effectiveness and real-time performance of the control strategy.

To solve the problem that a singlestage reduction hub drive system cannot meet the performance specifications of specialized vehicles, and that a twostage reduction would require the addition of extra control mechanisms, a novel coupled dual motor hub drive system scheme which is composed of two motors, a planetary gear mechanism, and a oneway clutch is proposed in this paper. The system is designed to operate in two coupling modes: torque coupling mode at low speed and speed coupling mode at medium and high speed with an autonomous mode switching capability as a functional requirement. Parameter matching is conducted with the objective of maximizing power utilization rate, and a control strategy for autonomous mode switching is developed. Under the same simulation initial condition, compared to the single motor two speed hub drive system, the coupled dual motor hub drive system exhibits an 81.25% reduction in maximum vehicle speed fluctuation and an 81.58% decrease in maximum acceleration during the mode switching process. An instantaneous optimal control strategy is established. Under the same operating conditions, the coupled dual motor hub drive system demonstrates a 21.42% reduction in energy consumption compared to the single motor twospeed hub drive system. Experimental tests are conducted using a prototype model to further validate the functionality and feasibility of the novel coupled dual motor hub drive system.

In the field of intelligent connected vehicles, the recognition accuracy of incar systems for noncommand voice input in complex environment (the proportion of correct voice input recognition by the system) is of great significance. To address this challenge, in this paper a multimodal rejection model is proposed. The model is based on the opensource ChatGLM26B large language model and has undergone exclusive rejection dataset construction and model finetuning for the invehicle interaction scenario. The rejection dataset is collected from real driving scenarios, integrating voice information with the driver's facial orientation, gestures, and emotion, and other nonverbal signals to provide richer interaction information, effectively overcoming the limitation of pure language recognition mechanisms in complex environment. Through experiments, it is found that the multimodal rejection model shows higher recognition accuracy (ACC) and lower false rejection rate (FRR) on the test set compared to the pure language rejection model.