In order to solve the problems of low efficiency and insufficient system scalability of single-machine test platforms in the vehicle-road-map collaborative simulation environment， a distributed autonomous driving simulation platform architecture for vehicle-road-map cooperative simulation is proposed in this paper， named VIMS （Vehicle-Infrastructure-Map System）. The VIMS platform uses CARLA as the virtual simulation engine. By introducing in real high-definition maps and connecting the hardware-in-the-loop devices such as driving simulators and signal machines to VIMS， the virtual-real traffic scene is formed. Considering the interaction of functions， the VIMS platform is divided into four modules， namely， the main world， the intelligent vehicle， the intelligent roadside， and the high-definition map， adopting ROS distributed architecture to realize the relative independence of the modules and interconnection between the modules. Considering the computational reliability and availability of the platform， distributed computing is used to realize independent computation among the four modules. Through the lane-keeping and vehicle-road-map collaborative positioning algorithm as examples for application validation， data acquisition， transmission and algorithm validation tests and evaluation are realized through the platform. The results show that the platform proposed in this paper can realize the real-time simulation of vehicle， road， and map collaboration to ensure that the modules operate organically and that the system architecture is highly scalable.

In order to reduce fuel consumption and transportation cost of heavy-duty truck， this paper coordinates the human-vehicle-road interaction system， integrates multi-dimensional information of vehicles and intelligent network environment， and proposes an adaptive range-domain predictive cruise control strategy （ARPCC） based on iterative dynamic programming （IDP）. Firstly， by combining the vehicle status and multi-dimensional information of the front environment， an adaptive distance domain model is established based on the longitudinal dynamics of the vehicle to reconstruct the road network， simplify the number of grids， and obtain the global optimal speed sequence by IDP. Secondly， on the basis of the global optimal speed sequence， the segmented optimal speed sequence taken from the adaptive distance domain is obtained to realize the fast solution of vehicle control state. Finally， Matlab/Simulink is used to verify the results， and the results show that the algorithm can effectively improve the computational efficiency and vehicle fuel economy by reducing the grid several times.

The road adhesion coefficient has an important impact on the vehicle dynamics control performance. In order to accurately obtain the road adhesion coefficient in real time and improve the estimation accuracy and convergence speed of the algorithm under different road surfaces and driving conditions， an interactive multiple model adaptive unscented Kalman filter （IMM-AUKF） based on the seven-degree-of-freedom vehicle dynamics model and Dugoff tire model is proposed in this paper for the distributed four-wheel-drive vehicles. The algorithm first introduces the improved Sage-Husa noise estimator into the UKF algorithm to construct the AUKF observer， which updates the measurement noise in real time and ensures the positive characterization of its covariance matrix， improves the weight of the new observation data， and enhances the real-time tracking accuracy and stability of the algorithm. Afterwards， the algorithm selects different observation variables to construct the longitudinal driving condition AUKF observer and the lateral-longitudinal coupling driving condition AUKF observer. And the IMM algorithm is also used to switch the observer model， so as to realize the algorithm's accurate estimation of the road adhesion coefficient under different driving conditions. The results of simulation tests on high/low attachment， joint and u-split roads and real vehicle road tests show that the proposed IMM-AUKF algorithm has higher estimation accuracy and faster convergence speed than the traditional UKF algorithm， and it can adapt to the real-time and accurate estimation of the road adhesion coefficient under different driving conditions.

The intelligent vehicle cyber-physical system （IVCPS） is a complex large-scale system with cross-fusion features across multiple fields. When designing domain-specific modeling languages （DSML） for IVCPS， the application of existing modeling languages has problems such as poor scalability， low maturity， and high learning cost. Therefore， based on the V-model and innovative architecture methods， this article studies the DSML for the IVCPS from two aspects： the top-level design process of IVCPS and the definition of language elements in the IVCPS meta-language knowledge set. The normalization of the IVCPS system-level meta-language and component-based meta-language is defined， and the components that express physical implementation， dynamic characteristics， and cyber computation in cyber-physical components are studied， so as to provide reference for the encapsulation and creation of cyber-physical components.

In shared road space， there are path conflicts between different road users moving in various directions. Road users must negotiate right-of-way through driving interactions to avoid collision risks， thus resolving potential conflicts. The description and modeling of interactive behaviors is crucial for accurately understanding and predicting the dynamic environment. Therefore， a semantic-level representation and extraction method for multi-vehicle interactive behaviors is proposed in this paper， taking interactive trajectory primitives as analysis units. Firstly， a nonparametric Bayesian method is utilized to segment interactive behaviors， obtaining interaction segments with significant behavior patterns. Then， the sticky hierarchical Dirichlet-Hidden Markov Model is employed to extract interaction primitives from these interaction segments. Finally， unsupervised clustering is applied to the normalized interaction primitives to obtain semantic-level behavioral features of interaction scenarios. An empirical study based on 20 797 pairs of multi-vehicle interaction data from the NGSIM highway dataset shows that the method proposed in this paper can extract and analyze complex interactive scenarios involving multiple participants， breaking through the limitation of existing research that only constructs interaction primitives for two vehicle interaction scenarios， and supporting the analysis of interaction among multiple traffic participants. The experimental results show that the proposed method can segment continuous driving behaviors into discrete interaction primitives. The clustering results correspond to actual interaction scenarios and can be used to characterize the interaction behaviors among vehicles in different interactive trajectory primitives. Furthermore， the method can enhance performance of downstream driving tasks in complex scenarios. In multi-step vehicle trajectory prediction， by integrating with baseline prediction methods， the proposed method can reduce the average prediction error and final position error by 19.3% and 14.6%， respectively， compared to baseline methods.

For the data imbalance problem of the current automotive CAN network intrusion detection algorithm due to the lack of attack samples， a CAN intrusion detection data enhancement method based on BEGAN is proposed， which introduces in one-hot coding to image the CAN message features and combines with the constructed Generative Adversarial Network to generate valid samples with the same format as the real attack and with different content. The practicality of the generated enhanced dataset is verified from the perspectives of feature maps， t-SNE visualization， statistical analysis and classifier validation by collecting real vehicle data as real samples for training， which can improve the intrusion detection classifier accuracy. With higher accuracy compared with the traditional oversampling algorithms including Random Oversampling （ROS）， Synthetic Minority Oversampling Technique （SMOTE）， SMOTE combined with Edited Nearest Neighbors （SMOTE-ENN） and Adaptive Synthetic Oversampling （ADASYN）.

For the characteristic of significant load variation in urban logistics autonomous light trucks and to meet the needs of low computational load and high stability， a path-tracking control method based on Linear Parameter-Varying Model Predictive Control （LPV-MPC） is proposed in this paper. Firstly， a linear parameter-varying model is constructed， and nonlinear mapping rules between the model and scheduling variables - speed and load - are established， to improve driving stability and mitigating system sensitivity to parameter fluctuations. Then， for the rolling optimization stage， a trajectory reconstruction method is designed to reconcile disparities between the discrete trajectory points provided by the planning layer and the demand of the control module's prediction layer. A smooth trajectory sequence tailored to the temporal scale of the prediction layer is constructed to effectively decrease the deviation between predicted and actual states. In addition， a multi-point state deviation prediction method is used instead of the traditional single-point prediction， fully leveraging reference trajectory information for improved tracking accuracy. Finally， the effectiveness of the proposed control strategy is verified through combined simulation and empirical vehicle tests.

Accurate prediction of collision risk is crucial for ensuring the driving safety of intelligent vehicles. However， the risk differentiation among heterogeneity vehicle types and its coupled effect in longitudinal and lateral directions has rarely been considered in existing driving risk assessment methods. Therefore， firstly， the behavior patterns of drivers of heterogeneous vehicle types are explored to analyze the influence of vehicle types on drivers' sensitivity to risk in this paper. Secondly， the heterogeneous risk thresholds for different combinations of vehicle types are identified， and the risk differentiation in such traffic surroundings is further quantified based on two-dimensional indicators. Finally， the coupled two-dimensional collision risk prediction model considering vehicle types is proposed， and the effectiveness of the model is validated through comparative analysis. This research helps to enhance the driving safety of intelligent vehicles， which also can provide a theoretical foundation for the development of collision warning systems for human-driven vehicles.

For the problem that existing numerical simulation methods are unable to accurately reflect the probabilistic temperature variations caused by thermal runaway in lithium batteries， s a modeling method for the thermal runaway propagation of lithium battery modules based on probability function triggering is proposed. This method calculates the triggering probability of thermal runaway in each temperature range by statistical analysis of the temperature range and distribution of actual thermal runaway events in lithium batteries. Based on the proposed probabilistic trigger simulator， the simulation process is probabilistically triggered. The effectiveness of the method is verified by comparing simulation results with experimental data， showcasing a high degree of correlation. Then， the thermal spread paths and their probabilities under different triggering conditions of the probability function are analyzed， revealing multiple potential routes for thermal runaway， including the jump thermal runaway event. The sequential thermal runaway path is identified as the most probable， while the jump phenomenon is deemed least likely. The proposal of this method further improves the consistency between numerical simulation and actual process of thermal runaway in lithium batteries， providing an effective research tool and analysis method for studying the probability of thermal runaway propagation in lithium battery modules.

To thoroughly review the current state and identify future trends of the Interior Sound Quality of Electric Vehicles （EVs）， the research progress and distinctive features of interior sound quality of electric vehicles are introduced firstly in this paper. Then， the limitation of the A-weighted sound level in evaluating the interior sound quality of EVs is introduced in detail， alongside objective evaluation approaches that incorporate psychoacoustic parameters and several unconventional parameters. Following this， a summary of subjective evaluation methods for the interior sound quality of EVs is made， including the advantages and disadvantages. Then， the objective quantification models for the interior sound quality of EVs both at home and abroad are classified and summarized. Finally， a summary and outlook are made on the evaluation of the sound quality of electric vehicles. It is believed that in the future the evolution of this field will likely pivot towards adopting high-precision objective models over traditional subjective methods， aiming to diminish evaluation time and cost while improving accuracy.

The pose control method of the vehicle with dual motor active lateral stabilizer bar is studied in this paper. Firstly， a dynamic model of a dual motor active lateral stabilizer bar and an eight-degree-of-freedom vehicle model including roll， lateral， yaw pitch， and suspension vertical displacement are established. Secondly， for the problem that the control algorithm parameters are difficult to adjust under complex working conditions， the actual pitch angle estimation method， the ideal pitch angle calibration method， and the pitch condition identification method based on vehicle state information such as suspension height signal and road slope signal are proposed. The pitch sub-controller and the roll sub-controller based on PID control algorithm are designed with the dual motor active lateral stabilizer bar as the actuator. Genetic algorithm is used to tune the parameters of each sub-controller. Finally， combined with the pose control matrix， the vehicle pose joint control algorithm is designed and verified through experiments. The Hardware in Loop results of MATLAB/Simulink CarsimRT and Rapid ECU show that the improvement of the roll angle， roll angle speed， and pitch angle of the vehicle equipped with dual motor active lateral stabilizer bar is more than 10% under different complex working conditions， which proves the feasibility and universality of the control algorithm.

In this paper， an efficient forward collaborative design method for high-performance switched reluctance electric drive systems is proposed to enhance the power density and reduce vehicle loss under typical driving cycle conditions. Firstly， dynamic torque and radial force models of the switched reluctance motor are established. Subsequently， a loss model is developed for both the motor and the electric drive system by incorporating the gear transmission system's loss model to evaluate overall loss during typical driving cycle conditions. Finally， using mechanical， electrical， and control parameters as co-design variables and taking cycle loss， mass， torque fluctuation， and radial force fluctuation as optimization objectives， a double-layer nested optimization method is employed to optimize the design. Based on this approach， an optimized 12/8 grade switched reluctance electric drive system achieves a 19.76% reduction in total weight after collaborative optimization design. Moreover， under typical cycle conditions （CLTC-P）， the total system loss decreases by 42.45%， while overall system efficiency increases by 7.66%.

As a three-phase power electronic device that emulates the characteristics of motor ports， the electric machine emulator （EME） provides an efficient test method for the testing of electric drive systems. The main sign of EME's restoration of the target motor port characteristics is to accurately restore the working current of the target motor， though the existing research has realized the reduction of the fundamental current and the lower order harmonic current， the restoration of the high-frequency ripple current of the motor driven by the motor controller still relies heavily on the matching of the filter inductor and the inductance of the target motor， which reduces the versatility of the EME. Therefore， based on the circuit equivalence virtual method， the equivalent circuit of PMSM is divided into two parts， with one part replaced by the actual circuit of EME， and the other part emulated by the control algorithm. Then， combining with the predictive control of the non-differential beat current， the control differential beat is compensated and the feedforward decoupling non-differential beat current following strategy is proposed. The experimental results show that based on the newly proposed port simulation algorithm of the permanent magnet synchronous motor， the EME can emulate PMSM with different parameters without replacing the inductor， with the frequency domain tracking error of high-frequency ripple current reduced from 160% to 20% of the traditional strategy， which significantly improves the emulation accuracy of the EME for the port-characteristics of PMSM.

For the complex hydraulic nonlinearity and time-varying friction disturbance of the Integrated Electro-hydraulic Brake System （IEHB）， an adaptive pressure control strategy is proposed. The outer-loop pressure controller introduces in a dynamic linearization model of hydraulic characteristics and realizes the adaptation of nonlinear hydraulic characteristics based on real-time identification of model parameters by a sliding mode observer. The inner-loop servo controller adopts pressure-based continuous friction compensation and back-stepping dynamic surface control to address frictional disturbance in the transmission mechanism. Hardware-in-the-loop test results show that， compared with the existing advanced cascade pressure control， the designed pressure control strategy exhibits higher control accuracy and robustness in various operating conditions， significantly improving the pressure control performance of IEHB under different hydraulic circuit structures.

At present， the assembly ratio of air suspension in different vehicle models is increasing， however， the air suspension matching of vehicle performance is mainly carried out based on the constant ambient temperature， and the control performance of air suspension in wide temperature range is rarely considered. For the problem of air suspension height control of passenger cars in wide temperature range， this paper proposes a wide temperature range air spring characterization model from the static characteristics of air springs， and simulates the height control of the suspension system of passenger cars in the non-motion state to carry out simulation and experiments， so as to realize the improvement of the service performance of the vehicle in the wide temperature range. Firstly， a wide temperature domain air spring model is established by wide temperature domain test data， which fully considers the influence of temperature on rubber and on gas. Secondly， an air suspension control algorithm based on the online linear quadratic regulator method （LQR） is proposed， which takes into account of the influence of temperature on airbag parameters. Finally， the robustness of the controller is verified under wide temperature domain conditions. Simulation and experiments show that the controller can control the body height to reach the target height and avoid oscillations under wide temperature range， with good stability and robustness.

The load spectrum of commercial vehicle cab assembly is the key factor affecting the accuracy and computational efficiency of virtual fatigue prediction. In this article， key links such as road load spectrum collection and editing， high fidelity dynamic modeling， and virtual iteration are explored， in order to obtain accurate and efficient external point time-domain loads from the engineering application perspective. Firstly， the full path road load spectrum of the driver's cab assembly is collected from the actual vehicle in the test field， and the original data is normalized， split， and reassembled considering random errors to obtain a statistically strong total damage target in the test field. Then， using the principle of equal damage， 9 operating conditions and their number of cycles are optimized， which not only controls the error within 10%， but also increases the efficiency by 75%. Subsequently， based on the performance parameters of the measured damping components， a high fidelity rigid flexible coupling dynamic model of the cab is established， and the accuracy of the model is verified through a 7-channel road simulation bench in the cab. Finally， the load decomposition of the optimal operating conditions is completed through virtual iteration， with an iteration error of less than 10%. Based on the above optimization and decomposition of the external connection point load， the virtual fatigue calculation of the cab body is efficiently completed， and the failure of the cab welding points is accurately predicted， which has a high degree of consistency with the durability test results of the road simulation bench， providing strong technical support for the design and optimization of commercial vehicle cabins.

Bipolar plates are one of the important components of hydrogen fuel cells. Titanium has many advantages as a metal bipolar plate substrate， but titanium has poor forming properties and severe rebound. In this paper， taking the micro channel hydroforming process of 0.1 mm TA2 pure titanium sheet as the research object， the microstructure deformation behavior of pure titanium is studied through the combination of experiment and finite element simulation， and the influence of process parameters on the forming quality of micro flow channel is analyzed， so as to provide guidance for the hydroforming of titanium bipolar plate. A finite element model has been developed for micro flow channel hydroforming of TA2 pure titanium sheet， and the accuracy of the finite element model is verified with the contours and thickness distribution of the test pieces. The effect of fluid pressure， loading rate and pulsatile loading on micro flow channel forming has been studied. The results show that the strain path of material in the process of micro flow channel hydroforming is plane strain， and the upper rounded corner position is the easiest to rupture. The loading rate does not have a great influence on the micro flow channel forming， and the molding depth decreases slightly by 3% with the increase of the loading rate. The pulsating loading path can improve the flow deformation ability of the material. Under the condition of critical fracture， the forming depth has a high increase compared with the linear loading path， which is up to 232.2 μm， an increase of 23%.

In order to identify and obtain the automotive structure fatigue damage of user route， the concepts of ‘element spectrum’ and ‘survey vehicle’ are proposed， and a convenient method for obtaining fatigue damage of automotive structure of user route based on road condition identification， classification and element spectrum is proposed. Firstly， the mathematical model of IRI （International Roughness Index） is established to identify the road grade and the method of identifying the user's road surface grade through the simple sensor scheme is established. And the correlation and independence between vehicle speed and IRI are studied. Subsequently， the method for determining the road classification and mileage distribution of the survey vehicle for user route based on IRI and vehicle speed is proposed. Then， combined with the element spectrum and unit mileage damage collected locally by the full-channel vehicle， the fatigue damage of the vehicle structure on the user's route is finally calculated efficiently. Thereby， the solution of ‘element spectrum + survey vehicle’ to identify the total damage of user route is created. The verification results are good， indicating that the technology and method studied is operable， exact and effective.

A study is conducted on the fatigue failure problem caused by the opening and closing of a car door. Firstly， the transient impact load under the door closing condition is discretized， and the vibration acceleration response signal and frequency response function are collected through the whole vehicle and bench tests respectively. Thus， the transfer path analysis （TPA） method is used to calculate the discretized transient impact load. Secondly， the obtained transient impact load is input into the finite element model of the door， and then the stress-time history response cloud diagram under the door closing condition is obtained， and the fatigue life under the door opening and closing condition is further calculated based on the fatigue damage theory. Finally， the fatigue analysis results based on load identification， the fatigue analysis results based on explicit dynamics and the fatigue test results under the door closed condition are compared and verified with each other. The conclusion shows that the three results are consistent， which has verified the rationality of the method.