The operating parameters of the PEM fuel cell stack have an impact on the performance of the stack output as well as the parasitic power of auxiliary devices such as air compressor， recirculation pump and cooling fan. System maximum net power output goals can be achieved by optimizing the operating parameters of the fuel cell stack. Forthe actual system， constrained by the performance of the air compressor and the regulating capacity of the backpressure valve， the adjustment range of cathode operating parameters is limited. In this paper， the 62 kW fuel cell system model is established based on MATLAB/Simulink. Through simulation analysis， the achievable ranges of parameters optimization under various load currents are determined. Genetic algorithm is employed to optimize the fuel cell stack temperature， cathode pressure， and oxygen excess ratio. The results show that increasing the temperature of the fuel cell stack at various load currents is advantageous for enhancing the system's net power， with the optimal operating temperature being 80 ℃. However， the optimization direction for the oxygen excess ratio and cathode pressure varies at different load currents. At low load current （50， 100 A）， increasing the oxygen excess ratio and cathode pressure results in a smaller growth in stack output power compared to the parasitic power. Providing lower oxygen excess ratio and cathode pressure is advantageous for enhancing the net power of the system. At high load current （300 A）， low oxygen excess ratio and cathode pressure will limit the output power of the stack， with the lowest net power of only 35.530 kW. After the oxygen excess ratio and cathode pressure are reasonably increased， the optimal net power is 53.271 kW， and the net power can be increased by 49.9% through the optimization of operating parameters.

Multi-sensor fusion is an effective way to improve intelligent vehicle perception. For the data-matching problem of the three types of sensors of LiDAR， millimeter-wave radar， and camera， traditional methods such as bipartite graph matching can’t achieve high precision， with poor matching robustness. Therefore， a multi-sensor data fusion algorithm for intelligent vehicles based on tripartite graph matching is proposed in this paper. The problem of data matching of the three sensors is abstracted as a weighted tripartite graph-matching problem. By using Lagrange relaxation， the original problem space is decomposed into subspaces， the weights of vertices and edge inside which are determined then by the cost matrix model. Furthermore， combining the perceptual error model and likelihood estimation， the posterior distribution of perceptual errors is determined. Ultimately the Lagrange Multiplier （LM） model is used for data matching. Finally， the effectiveness of the proposed matching algorithm is validated by the nuScenes training dataset and real-world vehicle tests. On the dataset， the proposed algorithm improves F1 scores by 7.2% compared to common algorithms. In various real-world vehicle scenarios， the proposed algorithm shows excellent perceptual accuracy and robustness across.

A deep-learning LSTM-based POD model （LSTM-POD） based on long short-term memory （LSTM） and proper orthogonal decomposition （POD） is developed for the turbulent wake of the square-back Ahmed automotive general model. A high time-resolution reconstruction is achieved by establishing the mapping relationship between the POD modal coefficients of the non-time-resolved planar velocity field and the time-resolved velocity signals at a number of discrete points， and the effect of different time-step configurations， i.e.， the single time step （LSTM-Sin） and multiple time steps （LSTM-Mul） on the reconstruction results is compared. The results show that the LSTM-POD model has strong learning and generalization ability in time series reconstruction， In addition， LSTM-Mul considers temporal continuity and correlation， the reconstructed mode coefficients （lower order） and velocity fields of which are more consistent with the POD reconstructed results compared with that of LSTM-Sin. The deep learning model proposed in this study can alleviate the problems of high resource consumption and low computational efficiency in obtaining high time resolution flow field data through experiments and high-precision numerical simulation.

A lithium battery early fault diagnosis method based on WOA-VMD and Shannon entropy is proposed in this paper to solve the problem of current battery management systems being unable to diagnose early faults. Firstly， the whale optimization algorithm is introduced to optimize the parameters of the variational mode decomposition algorithm to improve its decomposition performance and obtain intrinsic mode function components containing more fault feature information. Then， the voltage signal of the individual battery is decomposed and reconstructed to reduce the impact of measurement noise and additional excitation voltage. Furthermore， a sliding window is used to calculate the Shannon entropy range of individual voltage and the overall Shannon entropy of individual voltage dispersion to set appropriate thresholds for early fault diagnosis. After verification with actual vehicle data， this method can provide fault warning about 10 minutes in advance without generating false warnings for vehicles without faults. It has strong robustness and reliability.

There may be inconsistencies in temperature， charge state， aging state （capacity and internal resistance） between individual cells in a battery module. Due to the existence of the "short board effect"， the inconsistencies will affect the overall performance of the battery module， so timely and accurate inconsistencies diagnosis is very necessary. Considering that the above-mentioned inconsistencies will affect the electrode process characteristics， which will be reflected in the Electrochemical Impedance Spectroscopy （EIS） and Distribution of Relaxation Time （DRT）， in this paper， after clarifying the effect of several kinds of inconsistencies on EIS and DRT by combining the equivalent circuits， an inconsistencies diagnosis method for battery modules based on EIS and DRT is innovatively proposed. The performance of unsupervised clustering algorithms such as K-means， AP （Affinity Propagation） and DBSCAN （Density Based Spatial Clustering of Applications with Noise） is comparatively analyzed by mixing the abnormal batteries into a group of batteries with good consistency. The results show that the DBSCAN diagnostic accuracy is 99.2%， which can realize the accurate diagnosis of the inconsistency difference of single cells within the battery module.

The requirement of low carbon and lightweight in the auto industry is growing now. The new mega-casting technology applied on vehicle body can better achieve weight， cost and emission reduction， and has become spotlight to automobile manufacturers. In this paper， the traditional steel front compartment of passenger car body is replaced by integrated die casting part， and lightweight design on the aluminum alloy integrated front engine compartment is conducted. The optimal load path for stiffness is obtained through topology optimization of the front cabin by SIMP method. Considering the castability of the front cabin， the draft direction， thickness size and position distribution of the ribs are designed. Frontal impact simulation is conducted according to C-NCAP2021 and the impact resistance of the integrated die cast body is improved through Taguchi experimental design method and response surface optimization. Simulation analysis is conducted on the optimized performance of the white body. Compared with the traditional scheme， the weight of the optimal design is reduced by 13.9%， with the bending stiffness of the BIW increased by 9.7%， and the first modal meets the requirements. The research in this paper is meaningful for the platform design and industrialized application of integrated die casting car body structure in the future.

For the trade-off between prediction model accuracy and computational cost for path tracking control of autonomous vehicles， a learning-based model predictive control （LB-MPC） path tracking control strategy is proposed in this paper. A two-degree-of-freedom single-track vehicle dynamic model is established， and an in-depth analysis is conducted on its step response error with respect to variation in vehicle speed， pedal position， and front wheel steering angle compared to the IPG TruckMaker model. Methods for constructing error datasets and receding horizon updates are designed， and the Gaussian process regression （GPR） is employed to establish an error-fitting model for real-time error compensation and correction of the nominal single-track model. The error correction model is utilized as the prediction model， and a path tracking cost function is designed to formulate a quadratic programming optimization problem， proposing a learning-based model predictive path tracking control architecture. Through joint simulation using the IPG TruckMaker & Simulink platform and real vehicle experiments， the real-time performance and effectiveness of the proposed GPR error correction model and LB-MPC path tracking control strategy are verified. The results show that compared to the traditional model predictive control （MPC） path tracking control strategy， the proposed LB-MPC strategy reduces the average path tracking error by 23.64%.

The development of visual perception technology based on deep learning is beneficial for the advancement of environment perception technology in automatic driving systems. However， for corner cases of autonomous driving scenario， there are still some problems in the current perception model. This is because the ability of the perception model based on deep learning depends on the distribution of the training dataset. Especially when categories in the driving scene never appear in the training set， the perception system is often fragile. Therefore， identifying unknown categories and extreme scenarios remains a challenge for the safety of automatic driving perception technology. From the perspective of processing data sets， in this paper a novel multimodal automatic corner case mining process called "Corner Case Mining Pipeline （CCMP）" is proposed. In order to verify the effectiveness of "CCMP"， the concern case subset "Waymo-Anomaly" on the basis of Waymo open datasets is established， with a total of 3 200 images， each of which will contain the corner case scene defined in the text. Then based on the private data set Waymo-Anomaly， it is proved that the recall rate of "CCMP" corner case mining can reach 91.7%. In addition， the effectiveness of object detectors targeting long-tailed distributions in datasets containing corner case is experimentally verified. Ultimately， the authenticity of the automatic driving perception model in the real world is expected to improve from the perspective of datasets processing.

The three-dimensional object detection algorithm based on point cloud is one of the key technologies in the autonomous driving system. Currently， the voxel-based anchor-free detection algorithm is a research hotspot in academia， but most researches focus on designing complex refinement stage， at the expense of huge algorithm latency， to bring limited performance improvement. Although the single-stage anchor-free point cloud detection algorithm has a more streamlined detection process， its detection performance cannot satisfy the needs of autonomous driving scenarios. In this regard， based on the anchor-free detection algorithm CenterPoint， a single-stage anchor-free point cloud object detection algorithm for autonomous driving scenarios is proposed in this paper. Specifically， the encoding and decoding sparse module is introduced in this paper， which greatly promotes the information interaction of the spatial non-connected areas of the three-dimensional feature extractor， ensuring that the three-dimensional feature extractor can extract features that satisfy various target detection. In addition， considering that it is challenging to adapt the existing two-dimensional feature fusion backbone to the center-based head， in this paper self-calibrated convolution and large kernel attention modules are introduced in to effectively extract point cloud features of the target area， which are then gathered into the center point area， thereby improving the algorithm's recall and accuracy of the target. The proposed algorithm in this article is trained and experimentally verified on the large-scale public dataset of nuScenes. Compared with the benchmark algorithm， mAP and NDS are increased by 5.97% and 3.62% respectively. At the same time， the actual road experiments with the proposed algorithm are conducted on a self-built vehicle platform， further proving the effectiveness of the proposed algorithm.

The weak cold-start capability of fuel cells with graphite plates for vehicles is an important bottleneck that affects the large-scale promotion of fuel cell vehicles in the cold regions of northern China. Starvation self-heating is a common cold-start strategy whose basic principle is to increase overpotential by reducing the supply rate of reactants，and generate a large amount of heat inside the cell in a short period of time to achieve rapid heating. This approach is simple，but it requires a high degree of consistency in the initial water content of the stack monomers and is prone to single-chip reverse polarity and excess hydrogen concentration emission，which can affect the safety and durability of the fuel cell. To solve the above problems，the research group has developed a multi-channel AC impedance measurement device，proposed an optimized purging strategy for single cell impedance consistency，and established a constant voltage and variable air flow control method for cold-start of fuel cells，to achieve multi-objective and multi-parameter coupled coordinated control that provides high heat production，high safety，and high dynamics for voltage，current，and inlet/outlet air flow in the low-temperature start transient process. The bench test results show that the maximum impedance deviation of fuel cells is decreased from 0.7 to less than 0.2 mΩ，and the fuel cell engine system can achieve a fast start at -40 ℃ within 124 s，with good repeatability. The relevant technology is applied in the fuel cell demonstration at the 2022 Winter Olympics，with its effectiveness verified.