To improve the control accuracy of intelligent vehicle tracking controllers in variable operating conditions, controllers generally use multidimensional control parameter tables based on operating condition characteristics. When engineers manually adjust multidimensional control parameter tables, the workload is large and the tuning effect is not satisfactory. In order to enable the tracking controller of dynamic parameter adjustment capability, in this paper a vehicle speed and curvature adaptive parameter tuner is proposed based on radial basis function (RBF) neural network. Besides, a training set construction method based on Monte Carlo Probabilistic Inference for Learning Control (MC-PILCO) algorithm is proposed to address the problems of excessive real vehicle testing interactions and heavy tuning workload encountered during the training of tuner. By grouping typical operating conditions based on vehicle speed in the construction process of the training set, all different curvature working conditions within each vehicle speed working condition group are trained using the dynamic model trained on the data collected from tracking the straight-line scene at that vehicle speed for parameter tuning. By sharing the model, the number of real vehicle interactions is reduced. Real vehicle experiments show that the parameter adaptive tracking controller proposed in this paper has better lateral trajectory-tracking performance compared to controllers with fixed parameters under medium and low speed conditions.

Rain and snow often lead to slippery and low adhesion road surface, and the bottleneck of vehicle braking technology caused by it needs to be broken through. Among them, due to the differences in road adhesion in complex open-side road conditions, higher stability requirements are put forward for emergency braking control of multi-axle commercial vehicles. In order to improve the braking efficiency, the model free adaptive control (MFAC) algorithm is used to control the slip rate of each tire near the ideal value, and the electric and hydraulic coupling braking torque distribution strategy is set for distributed drive technology. In order to reduce the lateral errors in the braking process, PID-sliding mode observer (PID-SMO) is used to accurately observe the longitudinal force of each wheel, and the additional yaw torque caused by the longitudinal force difference is compensated by the middle and rear axle assisted steering. Through the joint simulation analysis, the emergency braking control strategy based on MFAC reduces the braking distance and avoids the slip rate fluctuation at the end of the braking period, ensuring the consistency of the wheel speed. The intervention of middle and rear axle steering greatly improves the lateral stability of the vehicle during braking.

Taking into account of factors such as core magnetic saturation，torque fluctuation，and component flexibility，an electromechanical coupling dynamic model for the switched reluctance motor-planetary gear electric drive system suitable for unsteady state conditions is established，with translational and angular displacements as generalized coordinates，which is validated through experiments. Through simulation analysis，the dynamic characteristics of the system under acceleration and variable load conditions are studied. The results show that under acceleration conditions，the speed at which the electric drive system is most prone to resonance is 3 900 r/min，at which multiple excitation frequencies cross the natural frequency of the system. Among them，the vibration energy generated by the excitation of the 15th order natural frequency at the gear mesh frequency is the largest，and the vibration energy is mainly concentrated in the θ_y direction of the planet carrier. At the moment of sudden load change，the system produces low-order free vibration dominated by the 5th order natural frequency，with vibration energy mainly concentrated in the θ_x and θ_y directions of the inner gear ring and gear housing.

Torsional vibration of power system is a hot and difficult problem in NVH field of extended range electric vehicles. In order to investigate the torsional vibration characteristics of the range extender under electromechanical coupling, taking a diesel engine range extender as the research object, systematic quantification of the shaft system is carried out and a torsional vibration mechanical model of the eight-degree-of-freedom shafting system is established. A non-contact measurement method is used to conduct torsional vibration tests on the range extender platform to verify the accuracy of the model. The method of obtaining shafting structure parameters, electromagnetic parameters and excitation torque is discussed. The coupling calculation of the range-extender shafting is carried out and the comparison analysis with the original machine is made. It is concluded that the addition of the motor rotor system will reduce the natural frequency of shafting by 27.6Hz and the maximum amplitude by about 21%. The resonant speed is shifted forward by about 200 r/min on the basis of the original machine, and a natural frequency is increased in the first 12 steps. The influence of electromagnetic parameters on torsional vibration characteristics of shafting is analyzed according to the particularity of range extender working condition. The results show that the electromagnetic damping is linearly and negatively correlated with torsional vibration amplitude, but it does not change the natural frequency and resonant speed of shafting. The electromagnetic stiffness has no obvious effect on the amplitude of torsional vibration, and mainly affects the size of zero frequency, which will lead to low frequen-

Transformer-based models have made significant progress in Remaining Useful Life (RUL) prediction. However, existing Transformer models have the following limitation of difficulty in local feature extraction and failure to consider the importance of varying temporal and spatial input features. To solve the problems, in this paper, an enhanced two-stream Transformer model is proposed, which is reinforced by the local feature extraction module and the interaction fusion module. Firstly, the local feature extraction module captures local features from both the temporal and spatial streams to compensate for the Transformer's deficiency in local feature extraction. Then, the two-stream Transformer is used to extract long-term dependencies in the temporal and spatial dimensions, enhancing complementary learning between the two streams. Finally, the interaction fusion module is constructed to capture stream-level interaction using bilinear fusion, further improving prediction performance. Experiments using multiple models on two real-world datasets from a diesel engine manufacturer demonstrate that the evaluation metrics RMSE and Score are reduced by at least 3.23% and 5.89%, respectively.

With the increasingly strict emission standards for automobiles, non-exhaust particulate matter emission, especially brake wear particulate matter emission, is becoming more prominent. In order to investigate the emission characteristics of brake wear particles under different test cycles, in this study a set of drum brakes is selected to conduct research on PM2.5 and PN10 emission under different test cycles(WLTP-Brake、WLTP、C-WTVC and CHTC-LT cycle) on a brake emission testing system modified based on a brake inertia table. The research results show that there are significant differences in the average initial/final braking temperature of the brake drum under different test cycles, and the highest final braking temperature generally occurs in the braking event corresponding to the maximum initial braking speed in the cycle. The PM2.5 emission factor of the test brake drum under WLTP-Brake cycle is 1.67 mg/km/wheel (the Euro 7 vehicle emission limit of 7 mg/km/vehicle), so the particulate matter emission control of the brake drum, like the brake disc, needs our attention. In addition, the initial braking speed of each cycle has a significantly greater impact on brake particle emission than braking characteristic parameters such as average braking deceleration. This study has practical reference value for the development and testing of low emission brakes for brake enterprises.

To enhance the passenger space and safety performance of electric vehicles, Cell to Body (CTB) technology is used to integrate batteries as structural components into the bottom of the vehicle body, which not only reduces the number of body components and connectors, but also helps to achieve the lightweight and range requirements of electric vehicles. Addressing potential collision safety issues and the risk of interrupted force transfer paths associated with the CTB structure, in this paper a frontal collision safety design process for vehicle bodies based on the CTB structure is proposed. The design method of "force decomposition-simulation analysis-test benchmarking" is adopted. Firstly, safety of the battery under frontal collision is ensured. Then, multi-level force transmission path is designed for optimization of vehicle body and the front structure of the vehicle is planned and designed based on the collision force value. The feasibility of the research method in this paper is verified through finite element simulation analysis and experiments, providing an effective design method for future vehicle body design and application.

Based on the research on the combination of vehicle application scenarios and electric drive torque near zero anti-jerk control requirements, in this paper four vehicle anti-jerk control requirements and their software architectures from the perspective of vehicle anti-jerk, including transmission clearance, torque limitation, electric drive torque near zero, and high-frequency de-noising of wheel speed fluctuation. Based on the requirements of anti-jerk control, vehicle anti-jerk algorithm architecture as well as the control strategy with electric motor torque near zero is designed, and the corresponding control process and calculation analysis are provided. Through simulation and real vehicle testing of the designed anti-jerk control strategy, it is proved that the control algorithm architecture and strategy can effectively achieve the anti-jerk function of the vehicle. According to the longitudinal acceleration curve of the real vehicle test and the fluctuation of the electric drive speed, it can be seen that the designed software architecture and control strategy have achieved good results in vehicle drivability.

With the increasing power levels and integration of electric vehicles, the thermal load of power modules is rising rapidly, which puts higher demand on the thermal management technology of power modules. The topology optimization design of power module liquid cooled plates is becoming a key technology for achieving high heat flux density heat dissipation due to its high heat transfer and low-pressure drop loss characteristics. In this paper, based on the density topology method, a topology optimization design model is constructed for the flow channel structure of the power module liquid cooling plate. Through the coupling of multiple physical fields of flow and heat transfer; multi-objective topology optimization design for the flow channel of the liquid cooling plate is carried out. The results show that the topology-optimized liquid cooling plate design presents a multi-level biomimetic flow channel structure, which significantly reduces pressure drop loss and improves heat dissipation capacity. Compared to the traditional finned liquid cooling plate structure of the benchmark, the pressure drop loss of the flow channel structure after topology optimization is reduced by 72.8%, with a maximum temperature reduction of 33.28 K, which provides a new design idea for high-performance liquid cooling plates of automotive electronic control power modules.

With the aim of improving vehicle ride comfort, in this paper a fractional-order skyhook control strategy based on the fractional order calculus theory is proposed. Firstly, a fractional-order skyhook vehicle suspension dynamic model is established to derive analytical expressions for the fractional-order skyhook damping force and fractional-order skyhook inertial force. Subsequently, particle swarm optimization algorithm is used to optimize the key parameters of the suspension. In order to solve the problem that fractional-order force cannot be realized physically, a vehicle ISD (inerter-spring-damper) suspension with mechatronic inerter is chosen as the controlled model. A model reference adaptive controller based on fractional-order skyhook is designed to track the mechanical performance output of the fractional-order suspension. Dynamic performance analyses of fractional-order skyhook inerter suspension and fractional-order skyhook damper suspension is conducted from both frequency-domain and time-domain perspectives. Simulation results show that fractional-order skyhook ISD suspension has more significant advantages in reinforcing ride comfort than integer-order skyhook ISD suspension. Under random road input, the root-mean-square value of vehicle body acceleration of fractional-order skyhook damper suspension decreases by 18.3%, while the fractional-order skyhook ISD suspension decreases by 20.6%. The bench test results demonstrate that the vehicle ISD suspension based on fractional-order skyhook further enhances ride comfort, offering new insights for the design of vehicle ISD suspension.