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%.

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.

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.

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.

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.

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.

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.

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.