The vibration characteristics of pure electric vehicles differ significantly from traditional internal combustion engine vehicles. In this paper，a research method suitable for optimizing the vibration isolation rate of mounting systems is proposed to address the insufficient vibration isolation rate of pure electric vehicle suspension. The vibration isolation rate in all directions of the engine mounting system and main factors affecting the vibration isolation rate of the engine suspension are analyzed. The vibration isolation rate of the rear suspension is defined as the optimization object，and the method of optimizing the jog stiffness of the passive side bracket installation of the suspension is proposed to improve the vibration isolation rate. Taking the optimal isolation rate and minimum mass ratio change as the optimization objectives，and using the NSGA-II multi-objective optimization algorithm，the target value of the jog stiffness of the passive side bracket installation is optimized，and the passive side bracket structure is adjusted according to the optimization results. The test results show that the optimized rear suspension Y-direction vibration isolation rate increase from 5.61 to 18.13 dB，and the driver's right side-ear noise decreases by 9.76 and 5.03 dB（A） in the 24th and 48th orders，indicating a significant improvement in driving comfort.

Predictive cruise control （PCC） performs long-term speed planning at the planning layer with the objective of predicting energy savings and short-term tracking control for the vehicle speed at the execution layer. Integrating these layers into a single optimal control problem poses significant challenges in system design due to the different time scale step requirements between the planning layer and the execution layer. To address this challenge，a hierarchical control approach is adopted in this paper. At the planning layer，an improved twin delayed deep deterministic policy gradient （TD3） algorithm is utilized to determine the long-term planning speed over the prediction horizon. Meanwhile，at the execution layer，based on model predictive control （MPC），taking the planned vehicle speed as the reference speed and considering engine fuel consumption characteristics and transmission shift laws，further economic optimization and tracking control of the planned speed are carried out in the short term. The hardware-in-the-loop （HIL） validation results show that combining the improved TD3 algorithm with MPC effectively resolves the time scale inconsistency between planning and execution in PCC，which can significantly reduce both fuel consumption and shift frequency during the cruising of heavy-duty commercial vehicles.

As a core component for regulating vehicle ride comfort，the performance of the suspension system directly determines the quality of vehicle driving. For the current problem of poor ride comfort during vehicle driving on complex roads，a composite suspension structure that is different from traditional suspensions is constructed in this paper，and the overall system architecture of this suspension is established. Firstly，in order to explore the vibration mechanism of the composite suspension of the complete vehicle，a dynamic model of the composite suspension of the complete vehicle is constructed. Secondly，combined with the complex driving requirements of the driver，a control strategy for the composite suspension system based on multiple operating conditions is constructed. The optimization effect is verified by different weighted RMS values of acceleration during vehicle driving，and the anti-air spring model is used to prove that the system can reduce the wear of the air spring. Finally，in the VI-Grade compact driving simulator，experimental verification is conducted based on the constructed complex operating conditions，and the test results of body vertical acceleration，roll angle acceleration，and pitch angle acceleration with and without control are compared. The experimental results show that the proposed composite suspension system can improve performance by 32.26%，23.77%，and 7.38% under straight，curved，and braking conditions，respectively，through vehicle performance testing under complex conditions. It can effectively improve the ride comfort performance of vehicles while driving and solve the problem of air spring wear under normal driving conditions.

To solve the problem of significant degradation in braking efficiency of medium and heavy-duty vehicles during prolonged downhill descents due to frequent engagement of the main brake system，a four salient poles liquid-cooled electromagnetic retarder structure is proposed in this paper. A vehicle downhill dynamics model is established to analyze the braking demand，using the magnetic equivalent circuit method to calculate its braking torque，and using the finite element method to numerically analyze its braking characteristics. The hierarchical variable domain fuzzy control strategy combined with a retarder is used for vehicle downhill braking control，and the controller and overall vehicle downhill braking model are established using MATLAB/Simulink for joint simulation. A 2 100 N·m prototype is designed. The braking characteristics of the prototype are tested through bench test and on-road vehicle tests. The results show that the actual measured value and the calculated value is basically consistent，with an average error within 5%，with the braking torque reaching 2 200 N·m when the speed is 1 250 r/min，which can meet the needs of medium and heavy duty vehicle braking.

The road friction coefficient is a significant factor that impacts the decision-making control strategy of the autonomous driving system. To achieve prospective and high-precision perception of the road friction coefficient，a novel estimation method for road friction coefficient based on the LiDAR equipped in vehicles is proposed in this paper. Firstly，a road dataset is constructed by collecting data from dry asphalt，concrete，wet asphalt，icy，and snowy road surface. Then，road point cloud is extracted using cloth simulation filtering and RANSAC algorithms，and abnormal noise points are removed based on Gaussian filtering. The road surface is divided into different regions according to the variation of point cloud reflectivity with distance and incident angle，and features are extracted accordingly. A road recognition model is constructed based on the deep neural network and trained by the collected dataset. Finally，the friction coefficient of the road ahead is determined based on the statistical experience of road material and peak friction coefficient. The test results show that the proposed algorithm achieves road type recognition accuracy of over 99.3%，with an average running cycle of 55ms，enabling real-time and high-precision estimation of the road peak friction coefficient.

In order to enhance the economic performance of pure electric vehicles （EVs） while maintaining better dynamic performance，a real-time shifting strategy based on driving cycle recognition is proposed for the self-developed two-speed dry dual clutch transmission （2DCT） for EVs. A radial basis neural network is adopted to predict the vehicle speed and the optimal shifting points are extracted by dynamic programming for seven types of driving cycle. Then，a driving cycle recognition model based on similarity comparison is constructed to recognize vehicle-driving conditions so as to achieve real-time shifting. The simulation based on MATLAB/Simulink and the 2DCT bench experiments are completed. The results demonstrate that the proposed real-time shifting strategy based on condition recognition can simultaneously meet the requirements of economic performance and shift frequency.

Path tracking control is a key technology for intelligent vehicles. However，the existing vehicle tracking control methods mostly rely on more accurate vehicle control models，while actual vehicle control systems mostly have modeling errors，parameter perturbations and external disturbances，which significantly affect path tracking control accuracy. In this paper，a learning path tracking control method for intelligent vehicles considering unmodeled dynamics of vehicles is proposed. Firstly，a nominal model of the vehicle is established and a linear prediction model is used to approximate the compensation for the unmodeled dynamics of the vehicle to improve the accuracy of the vehicle model. Then，learning and updating of the parameters of the unmodeled dynamics are realized based on the principle of Extended Kalman Filtering. Next，learning Model Predictive Controller （LMPC） considering the unmodeled dynamics of the system is established. Finally，the effectiveness of the proposed method in improving the path tracking accuracy is verified by designing a joint simulation test with Carsim and Matlab/Simulink for multiple operating conditions and multiple groups.

Suspension control requires good balance between ride comfort and driving stability，while considering system uncertainties，which is a complex task. In this paper，a disturbance observer-based suboptimal-nonsingular terminal sliding mode switching control algorithm （DOB-SNTSM） is proposed，with considerations of suspension dynamic performance indicators，algorithm robustness，and cost factors. Firstly，using spring mass acceleration information as input and by Kalman filter design，effective estimation of suspension deflection and spring mass velocity is achieved. Subsequently，a disturbance observer is devised to estimate uncertainties within the suspension system，with the disturbance estimation serving as feedforward compensation. Next，based on the sliding mode surface function，a suboptimal-nonsingular terminal sliding mode switching control algorithm is proposed，integrating with the feedforward compensation from the disturbance observer to formulate a novel active suspension control strategy. Finally，simulation and bench tests are conducted on both convex road surfaces and smooth random road surfaces. The results show that the introduction of disturbance observers can significantly improve the ride comfort index of the suspension. Compared to the SNTSM algorithm with the classical sky-hook control，the ideal state LQR method and without disturbance observer，the new algorithm not only effectively balances various suspension performance indicators but also achieves control effect close to the ideal state LQR using solely spring mass acceleration information. Additionally，the controller switching scheme significantly enhances algorithm robustness.

Driver posture in frontal crash conditions with and without autonomous emergency braking （AEB） has a significant impact on kinematic response and injury risk. In this paper，the THUMS （Ver.6.1） human finite element model is used to establish three driving postures，including standard，rearward recline，and forward recline，and a frontal collision constraint system model is established to conduct six sets of 50 km/h simulation tests for comparative analysis of the kinematic response of different driver postures with and without AEB，as well as the injury parameters of driver’s head and chest. The results show that the risk of head injury is highest in the recline posture with and without AEB intervention，with the HIC₁₅ of 817.5 and 626.9 with and without AEB，respectively. The intervention of AEB has the greatest effect on the driver's chest compression，which is increased by 89%，115%，and 22% for the three postures，respectively. The chest compression in the reward recline posture suffers the most serious injury. The results clarify the effect of driving posture and AEB on driver kinematic response and head and chest injuries，providing a reference value for the development and design of automotive restraint systems and AEB.

The axle and suspension are critical components of vehicles. To achieve real-time simulation of the solid axle and various suspension structures of a commercial vehicle，the property-based modeling technical route is adopted in this paper. The axle's movement is decoupled into motion kinematics and ride dynamics，while the suspension characteristics are divided into coupled carrying characteristics，RC/PC guiding characteristics，and coupled K&C kinematic characteristics. Innovatively considering the pitch dynamic effect of the axle，the nonlinear dynamic coupling relationship of suspension between axles，and the K&C coupling relationship of suspension between axles，a dynamic model for commercial vehicles is developed to trigger the negative phenomenon of brake vibration. Additionally，a K&C testing method for the coupled suspension and a method for model parameter identification are proposed. Finally，the accuracy of the model is validated at the system level by comparing K&C test data with TruckSim model results. Inputting vehicle parameters into the UniTruck software for simulation and comparing the results with TruckSim simulation，the model’s effectiveness is verified at the vehicle level.