Hydrogen fuel cell city bus is a type of new energy public transportation. In this paper, in order to evaluate the safety performance of a newly developed hydrogen fuel cell city bus body frame designed by the collaborating enterprise, finite element analysis is conducted to investigate its structural mechanics and dynamic characteristics under four typical operating conditions, including horizontal bending, ultimate torsion, emergency cornering, and emergency braking. Based on the simulation results, although the body frame of the bus meets the stiffness design requirements and avoids body resonance, it exhibits maximum stresses of 328.9 MPa and 348.6 MPa under emergency cornering and ultimate torsion conditions, respectively, exceeding the material yield strength and failing to satisfy the strength design requirements. Therefore, the size optimization method is employed to optimize the thickness of the body frame components. After optimization, the maximum stresses are reduced to 262.7 MPa and 300.6 MPa, respectively, representing a reduction of up to 20.13%. The optimization significantly improves performance and meets the strength design requirements. Furthermore, the body frame is lightened by 106 kg, achieving the goal of weight reduction.

The longitudinal distance between the vehicle and the forward vehicle, as well as the longitudinal distance between the vehicle and the opposite vehicle, is the main risk factor of overtaking behavior on two-way two-lane roads. Accurate measurement of these distances is the basis and key to automatic driving technology of two-way two-lane roads. In order to measure these longitudinal distances and improve the ranging accuracy, a vehicle distance measurement method of two-way two-lane roads based on monocular vision was proposed. Firstly, the vehicle detection model suitable for two-way two-lane roads was trained using YOLOv5s neural network. Secondly, aiming at the problem that the camera roll angle is not considered in the traditional geometric ranging method, the influence of the roll angle of the camera on ranging results using the traditional geometric ranging method was analyzed. In addition, the improved geometric ranging method considering the roll angle of the camera was proposed. Then, tests were conducted on a two-way two-lane road, and the results showed that the proposed method was effective. Compared with other methods, the improved geometric ranging method has higher ranging accuracy in this scene and can provide a reference for vision-based vehicle distance measurement in multi-lane scenes.

This study presents a novel optimization framework applying the multi-objective response surface method to enhance the safety of electric micro commercial vehicles (E-MCVs) during side pole impacts. By focusing on seven critical load-bearing components, including the B-pillar and door frame beam, we achieved a 2% reduction in component weight while significantly improving energy absorption by 22.2%. The optimization led to a substantial decrease in intrusion, with B-pillar abdominal intrusions reduced by 22.5% and lower threshold intrusions down by 26.3%. Despite these improvements, challenges remained regarding battery pack deformation. To address this, we proposed two innovative solutions: strengthening the side longitudinal beams and integrating a bionic thin-walled energy-absorbing structure. These approaches effectively reduced side intrusions of the battery pack by 43.5% to 43.8%, with the bionic structure showing superior performance in weight management. However, the manufacturing feasibility and cost implications of the bionic design necessitate further exploration. The innovation in this study lies in the dual application of a response surface optimization method for load-bearing components and the integration of biomimetic design principles, significantly advancing collision safety for E-MCVs while providing new insights into the weight-efficient safety design.

The rapid development of autonomous driving has underscored the pivotal role of 3D perception. Monocular 3D object detection, as a cost-effective alternative to expensive lidar systems, is garnering increasing attention. However, existing pseudo-lidar methods encounter challenges such as coarse quality and insufficient semantic information when generating 3D point clouds from monocular images. To address these issues, this paper introduces MSFNet3D, which aims to overcome the quality limitations of pseudo-lidar point cloud. Our contributions are threefold: (1) We introduce a dual-branch network to optimize depth maps and propose a multi-scale channel spatial attention module (MS_CBAM). This module captures multi-scale geometric features through a hierarchical feature pyramid and an adaptive weight allocation mechanism, thereby addressing the scale sensitivity inherent in traditional attention mechanisms. (2) We propose a consistency-weighted fusion strategy that employs local gradient consistency analysis and differentiable weighted optimization to achieve a pixel-level fusion of image and depth features. This approach reduces feature conflicts within the dual-branch network and enhances the model’s robustness in complex scenes. (3) We introduce a semantic-guided pseudo-point cloud enhancement method that leverages an instance segmentation network to extract object-specific semantic regions and generate high-confidence point cloud, consequently improving the accuracy of object detection. Experiments on the KITTI dataset show that the proposed method performs excellently under various detection challenges, achieving an average precision of 18.87% in the 3D detection of car objects, which is a 1.67% improvement over the original model. The method also shows good performance in detecting pedestrians and cyclists. The proposed framework can provide economical and reliable 3D perception for mass-produced electric vehicles.

Extended-range hybrid electric vehicles (E-RHEVs) require optimized parking charging strategies that consider both charging time and battery health. Existing research often neglects the crucial impact of ambient temperature and long-term cycling on battery degradation. This study addresses this gap by developing a novel parking charging strategy for E-RHEVs that leverages a temperature-dependent battery aging model and a Multi-Objective Mantis Search Algorithm (MOMSA)—a metaheuristic optimization algorithm designed to solve multi-objective problems by efficiently exploring trade-offs between conflicting objectives. The MOMSA optimizes a five-stage State-of-Charge-based Multi-stage Constant Current (SMCC) charging profile—a dynamic current adjustment strategy that minimizes battery capacity degradation by dividing the charging process into sequential phases. The MOMSA-based SMCC strategy achieves an optimal balance between charging time and battery capacity degradation across a range of ambient temperatures (5 °C to 35 °C). Compared to a conventional 0.5C CC-CV charging strategy, the MOMSA-based SMCC strategy demonstrably reduces battery degradation with a moderate increase in charging time. Furthermore, the MOMSA-based charging strategy outperforms a Multi-Objective Particle Swarm Optimization (MOPSO)-based approach, achieving comparable degradation mitigation while significantly reducing charging time. One-week cycling simulations under realistic driving conditions further validate the MOMSA-based charging strategy’s superior long-term performance in delaying battery degradation across various temperatures. This strategy extends E-RHEV battery lifespan while maintaining operational efficiency.

Accurate online tracking of state of charge (SOC) inconsistency in lithium-ion battery packs is essential for safety. It is equally critical for effective battery management in real-world operation. To achieve robust performance in dynamic battery environments characterized by temperature fluctuations and cell aging, a method combining enhanced Rime optimized-unscented particle filter (ERIME-UPF) with cubature smooth variable structure filter-varying boundary layer (CSVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the hysteresis effect dynamic migration model, and the Rint model. First, module SOC is estimated using an ERIME-UPF, which adaptively adjusts the noise covariances of UPF via the enhanced RIME optimizer. Simultaneously, CSVSF-VBL employs the Rint model to estimate cell SOC inconsistencies, incorporating capacity and internal resistance coefficients into the second-order performance chattering to better capture cell inconsistency. Experiments focus on LiFePO₄ batteries under various inconsistencies, temperature, and aging states. The results show that ERIME-UPF achieves an average mean absolute error (MAE) of 0.33% for module SOC estimation, while CSVSF-VBL achieves a peak MAE of 3.28% for cell SOC estimation. Demonstrating superior accuracy and robustness in tracking SOC inconsistency under dynamic and degraded operating conditions.