The rapid rise of new energy vehicles under the“dual carbon” goals has shifted the focus of vehicle lightweighting from traditional structural and process innovations to the substitution and optimization of materials. Aluminum alloy materials highly match the requirements for materials used in automotive lightweighting, and are currently the most preferred materials. This paper reviews and discusses the research and development, application and new directions of aluminum alloy materials in automobile lightweighting.Firstly, the paper introduces the main brands and the application status of die casting aluminum, which accounts for more than 70% of the aluminum used in automobile drive systems, chassis systems and body structure parts. The focus is placed on analyzing the research status of integrated die casting technology and its necessary non-heat treatment aluminum alloy. The paper summarizes the use of wrought aluminum alloy in the automotive field, including the stamped parts, profiled components and forgings. It also discusses the research status of traditional automotive forged aluminum and high-strength aluminum, guided by the development trend towards high-strength and ductile aluminum alloys. Finally, the existing bottlenecks of aluminum alloy materials in the automotive field are analyzed and prospected.

Neural networks lack interpretability and the D-S theory is prone to paradoxes in high-conflict scenarios of multimodal fusion. In response, this paper proposes a result-level multimodal fusion method that integrates a confidence estimation network with an improved D-S theory. The method consist of two key components. First, a confidence estimation network reframes the classification problem in target detection as a confidence estimation task, providing confidence scores for the detection results of individual unimodal networks. Second, a fusion method with improved D-S theory uses confidence scores and class information to construct evidence, achieving final fusion of detection data from different modalities. Evaluation experiments on the KITTI dataset show that the proposed fusion method improves mAP by up to 6.64% compared to image-based detection and up to 15.43% compared to point cloud-based detection. In the comparison of fusion methods, the proposed fusion method achieves an mAP improvement 0.81% higher than the classical D-S fusion. It effectively reduces classification conflicts and addresses the limitations of the classical D-S theory.

Lithium-ion power batteries are currently the most widely used energy storage devices in electric vehicles. Rapid and accurate battery fault diagnosis is crucial for ensuring safe vehicle operation. This paper proposes a method for diagnosing self-discharge faults in power batteries based on adaptive voltage thresholds for individual cells and Particle Swarm Optimization-Support Vector Machine (PSO-SVM). This study focuses on the voltage signals of power batteries, and combines the boxplot method with expert review to label self-discharge fault samples. A sliding window method is used to extract 16 features from both the time and frequency domains. To further reduce the dimensionality of voltage features, principal component analysis is applied, selecting the top five principal components with a 95% cumulative variance contribution as inputs for the PSO-SVM model. This method aims to improve the accuracy of self-discharge fault detection in batteries. The results show that the proposed method achieves high detection accuracy, strong reliability, and promising potential for practical applications in electric vehicles. Additionally, it provides theoretical support for enhancing the safety performance of electric vehicles.

To address the heat dissipation issue of hub motor in a specialized vehicle, a serpentine flow channel was chosen as the cooling structure. CFD simulations were performed to analyze the temperature rise, temperature distribution, and radial temperature variation of the hub motor. Subsequently, the effects of the number and width of flow channels on the motor's temperature and flow fields were investigated. The appropriate number of flow channels was determined and an initial selection of channel width was decided. Furthermore, taking the flow channel height and width as optimization variables, a multi-objective optimization was conducted, with the maximum motor temperature and flow channel pressure drop as the optimization targets. The results show that after optimization, the highest motor temperature increased by 0.22℃, while the flow channel pressure drop decreased by 904.19 Pa, effectively reducing energy loss and improving the heat dissipation efficiency of the cooling system.

Taking a domestic fuel commercial vehicle as an example, an energy consumption optimization prediction model suitable for commercial vehicles was constructed using the Internet of Vehicles big data platform and a neural network model. Firstly, the historical vehicle operation data was preprocessed to analyze the correlation between different vehicle operation characteristic data. Secondly, an adaptive weight attention mechanism was introduced based on Bi-directional Long Short-Term Memory (BiLSTM) and the characteristics of vehicle data. The Improved Whale Optimization Algorithm (IWOA) was used to optimize the network hyperparameters of the model, leading to the construction of the IWOA-BilSTM-Attention commercial vehicle energy consumption optimization prediction model. Finally, the prediction performance of multiple models under different driving conditions were compared and analyzed. The results show that under actual driving conditions, the root mean square error and the mean absolute error of the optimized model are reduced by approximately 26.73% and 20.0%, respectively, compared with the original model. This verifies the feasibility of the optimized model for predicting the energy consumption of commercial vehicles.

A multi-objective optimized automatic design process is developed based on the airflow velocity required for cooling performance in the passenger cabin. This process considers the airflow performance on the driver's side and the passenger's side, focusing on the positioning of the grille vent blades. Based on CFD simulations and a multi-disciplinary optimization design platform, the Latin hypercube sampling method is used to generate sample points and construct the DOE matrix. A neural network-based proxy model is then built to predict the blow-face airflow velocity performance parameters. The NSGA-Ⅲ algorithm is used to obtain the Pareto frontier diagram for the multi-objective optimization problem. The optimized grille blade position increases the airflow speed by 109.1% on the driver's side and by 137.5% on the front passenger's side. The reliability of the optimization results is verified through unsteady CFD simulations and cooling performance tests before and after the optimization.

To improve vehicle ride comfort on bridges and reduce its impact on the bridge structure, a parametric optimization method for the inerter-spring-damper (ISD) suspension was developed, taking into account the influence of vehicle-bridge interaction (VBI) on vehicle and bridge responses. A coupled vehicle-bridge system was modeled using a Euler-Bernoulli simply supported beam and a single-/dual-mass vehicle to analyze the impact of the interaction on the inherent characteristics of both the vehicle and the bridge. The advantages of the ISD suspension under coupling effects were examined and the influence of the inertial coefficient on transmissibility characteristics was analyzed. The fixed-point theory was employed to optimally adjust suspension damping, obtaining the optimal frequency and damping ratio for the vehicle. The results show that the ISD suspension improves high-frequency system characteristics, effectively reduces the transmissibility amplitude, enhances ride comfort and significantly suppresses bridge loads. The study is valuable for improving vehicle ride quality and the structural health of bridges.

In view of the limited accuracy of vehicle dynamics described by traditional control schemes, it is difficult to achieve high precision tracking of the expected state. Therefore, a data-driven model predictive control method for path tracking is introduced. Firstly, a vehicle state parameter observer was constructed using the random forest method. Based on this observer, the nonlinear mapping relationship of vehicle dynamics was analyzed to optimize the controller's underlying mathematical model, thereby reducing the adverse effects of external environmental and mechanical structural disturbances on control performance. Secondly, according to the model predictive control mechanism and vehicle dynamics mapping relationship, the vehicle state space equation was constructed. The linear pattern of vehicle state changes within the local range was analyzed. The quadratic programming cost function for optimizing the steering wheel angle and four-wheel driving force was designed and calculated, aiming to achieve the optimal utilization rate of four-wheel adhesion. Finally, the simulation results show that the proposed control scheme can prevent excessive fluctuations in vehicle body state in the presence of disturbances, and it also maintains a low utilization rate of tire adhesion on undisturbed road sections, achieving safe, stable and high-precision tracking.

To address the issue of discontinuous curvature in autonomous parking path planning, this paper analyzes vehicle kinematics, and combines the arc-line-arc planning method with the reverse parking process. A fifth-degree polynomial optimization approach is employed to generate a compact parking trajectory with continuous curvature. To enhance parking tracking accuracy, the discrete LQR tracking controller based on the kinematic model is improved using fuzzy control methods. Simulations and experimental validations are conducted to verify the effectiveness of the algorithm. In the Simulink/CarSim co-simulation, the maximum tracking error is 0.027 m, and the average tracking error is 0.013 m. In real-vehicle experiments, the maximum tracking error is 0.07 m, and the average tracking error is 0.029 m. Compared to the LQR tracking controller, the FUZZY-LQR tracking controller reduces the average tracking error by 33%, improving the autonomous parking path tracking performance.

To extend the range of rear-drive electric buses, a brake force distribution control strategy based on intention recognition is proposed. Firstly, 400 sets of real-vehicle braking data were collected and analyzed. The brake pedal opening and its rate of change were used to calculate the braking force applied to the front and rear axles. Considering the battery constraints and the regenerative braking constraints of the rear axle motor, the braking control strategy is formulated and validated using Simulink-Trucksim joint simulation. The results show that the composite braking control strategy based on intention recognition achieves an accuracy of 95.7% in detecting the driver's braking intention. Under typical urban driving cycle in China, the fuzzy neural network-based energy recovery strategy, the fuzzy control recovery strategy, and the conventional recovery strategy increased the final state of charge (SOC) by 2.69%, 2.09% and 1.83%, respectively, compared with the non-recovery control strategy.

The slide movement pattern of traditional mechanical presses is relatively fixed, and quasi-static stamping simulations usually ignore the effect of strain rate. In contrast, servo presses feature a flexible slide stroke and adjustable stamping speed; thus, a material constitutive model that includes the strain rate effect is necessary to achieve accurate servo stamping simulations. To address the limitation of obtaining the stress-strain curves at only a finite number of strain rates through tensile tests, this paper analyzed and evaluated the strain rate-sensitive models, including the power law model, linear power law model, Johnson-Cook model and Cowper-Symonds model, using DDQ steel test data for model fitting as an example. Curve fitting is performed for segmented strain range and grouped strain rates, the parameter identification methods in each model are constructed and the applicability of each model in finite element software is discussed. The results provide guidance for selecting an appropriate constitutive model for servo stamping simulations.

This paper discusses the challenges of applying virtual reality (VR) in visual simulation and real-time rendering for automotive research and development. Focusing on the application scenarios and practical needs of automotive virtual development, a high-performance VR computing system based on real-time ray tracing and parallel rendering has been established for the first time among domestic automotive companies. By using the Bounding Volume Hierarchy (BVH) acceleration structure while optimizing algorithm parameters and network configuration, high-precision real-time visualization of large-scale vehicle models has been achieved. The offline rendering process, which previously took several hours, has been optimized to real-time rendering in just 1～2 seconds. The system effectively simulates ambient light refraction and reflection on parts, light uniformity and leakage, and physical occlusion between parts. Compared with traditional rasterization rendering, this approach has made a significant breakthrough, achieving over 90% physical realism. Through full-process implementation in several vehicle projects, it has replaced many physical models and reduced sample costs in the R&D process.

The paper examines the door sealing cavities near the B-pillar, C-pillar and the top of the tailgate of a SUV. The geometric features of these cavities are extracted and represented using three equivalent regular cavity models. The experimental studies on the sound phenomena and formation mechanism of these cavities were carried out in a small acoustic wind tunnel. The results indicate that the three cavities exhibit two different sound phenomena, i.e. strong resonance and weak resonance. Further numerical simulations are performed to analyze the acoustic characteristics of the cavities. The results show that self-excited oscillations are difficult to form in the small opening cavities, such as those near the B-pillar and C-pillar. Instead, pulsating excitation at the opening induces weak resonance in the cavity mode. In contrast, the cavities with wider openings near the tailgate can form self-excited oscillations, which resonate with cavity acoustic modes or Helmholtz resonance modes to produce strong resonance with whistling noise. The differences in phenomena and mechanisms between automobile door sealed cavities and large cavities are revealed. A method for determining the frequency of self-excited oscillations is proposed by characterizing the vortex motion in the Q-factor cloud diagram. Additionally, the contribution of self-excited oscillations to the peak values in the cavity sound pressure spectrum is clearly explained, while effectively defining their frequency.