The development of hydrogen fuel cell vehicle is one of the important measures to realize the "Double carbon" strategic goal in our country. As the main power source of fuel cell vehicle, proton exchange membrane fuel cell (PEMFC) system has nonlinear, strong coupling and timedelay characteristics. Those characteristics make PEMFC system have many difficulties when it is faced with complex power demand under various conditions like vehicle acceleration and climbing, especially in terms of precise control of gas supply and dynamic regulation of system response. The flow rate and pressure of gas supply play a decisive role in the output performance of PEMFC. Improper gas supply can lead to low efficiency of the stack and even damage or failure of the stack, and then affect the overall performance and service life of the system. Therefore, accurate gas supply system by optimizing the gas supply system is the key to improve the performance and extend the service life of PEMFC. Based on the establishment of a gas supply system model for PEMFC, in this paper the influence of key operating parameters such as oxygen excess ratio, gas pressure and gas pressure difference on the output performance of the system is analyzed. The synergetic control of oxygen excess ratio, cathode pressure and bipolar gas pressure difference in PEMFC system using nonlinear active disturbance rejection control (ADRC) algorithm is researched, which is then compared with those under the proportional integral derivative (PID) controller. Under PID control, the maximum overshoot of the oxygen excess ratio can reach 1, while under ADRC control, the overshoot only around 0.2, and the time to reach steady state is approximately 0.1 seconds, compared to around 1 seconds under PID control. After a sudden change in load current, the overshoot of the cathode gas pressure under the PID control algorithm is around 0.08 with large fluctuations, reaching a stable value within 2 seconds. Under the ADRC control algorithm, the cathode gas pressure can reach stable value within 0.8 seconds, with an overshoot much smaller than the PID control algorithm. Under PID control, the overshoot of the twostage gas difference can reach up to 0.15 with large fluctuations and longer time to reach stability, but under the ADRC controller, it can quickly and stably reach the set value of 0.2 bar with smaller fluctuations. The results show that the ADRC controller has better decoupling, robustness and stability under the disturbance factors of load current and hydrogen displacement action.

To meet the demand of efficient and accurate perception in autonomous driving systems, relying solely on cameras makes it challenging to achieve highprecision and robust 3D object detection. An effective solution to address this issue is to combine cameras with costeffective millimeterwave radar sensors, enabling more reliable multimodal 3D object detection. An effective approach to address this problem is to combine cameras with costeffective millimeterwave radar sensors, enabling more reliable multimodal 3D object detection, which not only improves the accuracy of environmental perception but also enhances the system's robustness and safety. In this paper, an autonomous driving perception algorithm based on the fusion of millimeterwave radar and cameras, named HPRDet (historical pillar of ray cameraradar fusion bird's eye view for 3D object detection) is proposed. Specifically, a radar BEV (bird's eye view) feature extraction module called RadarPRANet (radar point RCS attention net) is designed firstly. It comprises a dualstream radar backbone that extracts radar features with two representations, and an RCSaware BEV encoder that distributes radar features into the BEV space based on radarspecific RCS characteristics. Secondly, Historical radar of Object Prediction paradigm is adopted, designing both longterm and shortterm decoders that operate only during training, thus avoiding additional inference overhead. Due to the sparsity of the input data in this network, multimodal historical multiframe input is introduced to facilitate more accurate BEV feature learning. Lastly, the millimeterwaveoptimized ray denoising method is proposed, which utilizes the information from the current frame's millimeterwave radar point cloud as prior knowledge to assist in proposal generation, thereby enhancing the query feature representation for the camera. The proposed algorithm is trained and validated on the largescale public dataset nuScenes, with the NDS reaching 56.7% on the backbone of Resnet50.

In order to adapt to the high power density of automotive high-speed motors and the high thermal load under extreme working conditions, the current motor cooling mostly adopts the direct contact oil cooling heat dissipation method, and it is necessary to establish a motor oil temperature model suitable for the study of thermal control methods. Existing methods are mainly based on finite element simulation calibration, which cannot meet the realtime application requirements, while the multi-physical field coupling of the complex oilwater heat transfer circuit makes it difficult for the online reconstruction of oil temperature. In this paper, a secondorder lumpedparameter oil temperature model is proposed to strengthen the time-sequence cyclic process and consider the strong autocor-relation. The oil circuit unit is modeled according to the calibration, and the motor loss response is determined based on bench-top measurements. The time-sequence convolution method is adopted to describe the heat transfer process, and a cyclic dynamic recursive model with high and low oil temperature coupling is established. Oil temper-ature-sensitive parameters are introduced to improve the adaptability of the working conditions to solve the difficult problem of describing the oil temperature distribution in the flow path. Finally, the model accuracy is verified online by road spectrum working conditions, with the average absolute estimation error of the oil coolant temperature within 1°C, which can support the refined thermal management of the motor.

A noninvasive driving fatigue state identification method based on the surface electromyographic signals of the driver's legs is proposed. Firstly, the electromyographic signal of the tibialis anterior muscle of the driver's right leg is collected through a simulated driving fatigue experiment, and the fatigue status is marked through a subjective evaluation scale. Secondly, a variational mode decomposition algorithm is used to filter out noise on the surface electromyographic signal, and 12 timefrequency domain eigenvalues are extracted from the five IMF components obtained by decomposition. Finally, a driving fatigue state discrimination model based on whale algorithm optimized support vector machine is constructed. The results show that this method has a good discrimination effect on three fatigue states, with an accuracy of more than 84%.

To meet the stringent thermal management requirements of electric vehicles and address global climate change issues, in this paper an integrated thermal management system is developed for electric vehicles based on R290 refrigerant. The system's performance is analyzed and validated through simulation and experiments. The results show that the cooling and heating capacities of the R290 dualside indirect thermal management system increase with the increase of the compressor speed, while the coefficient of performance (COP) decreases with the increase of the compressor speed. The heating capacity from the compressor's hot gas bypass increases with higher system pressures. Under hightemperature cooling conditions at 40 °C, the system's maximum cooling capacity is 9.25 kW. Under lowtemperature heating conditions at 18 °C, the maximum heating capacity is 7.24 kW. At extremely low temperatures of 20 °C, the maximum heating capacity from the compressor's hot gas bypass is 4.3 kW.

The dualmotor coupled drive is a common configuration for the Electromechanical Transmission (EMT) system in tracked vehicles, which is characterized by inputoutput coupling, high power transmission efficiency, and variable load conditions. However, most existing torsional vibration control strategies for EMT are designed for symmetric excitation conditions on both sides, which do not align well with realworld operating scenarios. To improve the torsional vibration under asymmetric excitation, an EMT torsional vibration model is first established to investigate the vibration energy coupling effect between the two sides of the EMT under asymmetric excitation and its influence mechanism on the system's dynamic behavior. Based on these findings, a disturbance compensation method based on dualloop feedback is proposed, and a torsional vibration suppression strategy tailored for EMT under asymmetric excitation is developed. Verification results show that this strategy can effectively suppress torsional vibration of the EMT system under such excitation conditions.

For the problem that the spatiotemporal separation trajectory planning method used in autonomous vehicles is prone to insufficient vehicle flexibility, and even cannot generate feasible trajectories under complex working conditions, while the existing spatiotemporal unified trajectory planning method is difficult to meet the requirements of structured road application, a spatiotemporal unified planning method based on dynamic programming and numerical optimization algorithm is proposed. Firstly, the spatiotemporal unified coarse trajectory is generated by dynamic programming algorithm in Frenet coordinate system. In the process, deterministic sampling method is used to expand the child nodes. Then, taking the coarse trajectory as reference, the feasible spatiotemporal corridor is constructed in Cartesian coordinate system, and the NMPC optimization model is established to generate the final trajectory. Finally, the algorithm is verified by simulation. The results show that the proposed algorism has good adaptability to structured road, and can better balance the requirements of traffic efficiency, trajectory comfort and time consumption than other spatiotemporal unified algorithms.

In this paper, an electric vehicle's aerodynamic drag and wake are numerically studied. The results show that the flow separates from the rear of the car may roll up into a largescale vortex at Re_L=1.1 × 10⁷. The ratio of the RMS drag and the mean drag reaches to 3.27%, making an unneglectable effect on ride comfort and mileage prediction. The pressure at the back, the underbody, the middle and lower parts of the near wake, the aerodynamic resistance of the entire vehicle, the pressure near the wall of the rear guard plate in the bottom, and the separation flow at the bottom all have a characteristic frequency of 12 Hz. However, the flow separation at the top and Cpillar of the car does not have this characteristic frequency. It proves that the underbody flow separation at the rear is the main cause of dynamic changes of the aerodynamic drag.

An adaptive antidisturbance angle control strategy is proposed to address the nonlinear disturbances such as system parameter uncertainty, tire return torque obstruction, and coupling of electromagnetic characteristics of steering motors, which are faced by the active steering of SteerbyWire (SBW) system. A radial basis function neural network and robust sliding mode theory are used to design the outerloop cornering controller to adaptively compensate for the SBW system parameter uncertainty and tire return torque obstruction. Linear selfimmunity control is introduced into the innerloop current controller to cope with the coupling problem of electromagnetic characteristics of the steering actuator motor so as to improve the dynamic response performance of the SBW system. The simulation and hardwareintheloop test results show that the designed control strategy can help the SBW maintain the cornering steady state following error within 1.5° under various operating conditions.

In order to solve the problem of roaring sound inside the vehicle caused by intermittent engine intervention during charging and discharging of dieselelectric hybrid vehicles, in this paper a semicoupled cluster control strategy with better comprehensive performance is proposed based on the traditional multichannel active noise control (ANC) system by combining the advantages of the centralized control strategy and decentralized control strategy. Compared with the centralized control strategy, the computational cost of the cluster control strategy is reduced by about 50%, and the noise attenuation performance is comparable to that of the centralized control strategy. Compared with the decentralized control strategy, the stability is obviously better, and the noise reduction effect is outstanding. Based on the MATLAB simulation platform, a variety of cluster control strategies and traditional control strategies in the vehicle are compared and analyzed, and the road test experiments of a rangeextended electric vehicle are carried out under its common working conditions. The results show that the cluster control strategy can be well applied to the multichannel active noise control system in the vehicle, and the average noise reduction amount of the second, fourth, and sixthorder range extender noise at the four seat headrest positions can reach 15.9, 10.6 and 5.7 dB(A), respectively, showing good noise reduction effect and stability. The research results can be applied to the noise control of manned cabins, such as aircraft, submarines and other fields, which has important scientific significance and engineering value.