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.

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.

The rapid development of new energy vehicles has dramatically increased the demand for battery materials such as lithium, cobalt, nickel, and manganese. To assess the supply risks of these resources, the demand for battery materials in China's new energy passenger vehicles from 2023 to 2050 was predicted using the Gompertz curve model and material flow analysis, under two different scenarios of battery technology development. The results indicate that by 2050, under the lithium iron phosphate route (LFPR), the demand for lithium, cobalt, nickel, and manganese will be 238, 169, 362, 158 kilotons, respectively. Under the nickel-manganese-cobalt (NMC) lithium battery route, the estimated demands will reach 242, 201, 1 084, 187 kilotons, respectively. Recycled lithium, cobalt and nickel in 2050 are expected to meet at least 86.5%, 93.5% and 65.8% of their annual demand, respectively. Given the current lack of comprehensive laws and regulations for waste battery recycling in China, it is essential to develop relevant standards.

To address the issues of poor yaw stability and low trajectory tracking accuracy in in-wheel motor-driven vehicles under complex operating conditions, a coordinated control method was proposed. By using neural networks for dynamic identification, the stable region in the phase plane of vehicle center-of-mass sideslip angle and sideslip angular velocity was determined. And the instability factor was obtained based on the boundary line features. This factor served as a parameter influencing the objective function weight, while sliding mode control was employed to prevent excessive wheel slip. Simulation results show that compared to the single-target MPC trajectory tracking control strategy, the proposed method reduces the maximum lateral tracking error on low-adhesion road surfaces by 61.7%, and decreases the maximum sideslip angle of the vehicle's center of mass by 75.7%. Even at high speeds, the vehicle maintains stable motion, achieving a balanced improvement in both trajectory tracking accuracy and yaw stability.

To enhance the thermal safety of lithium-ion battery packs, this paper proposes a liquid-cooled thermal management structure with bionic channels resembling leaf veins. The thermal performance of the model is analyzed and optimized using the fluid dynamics software STAR-CCM+. Using the polar-variance method in orthogonal testing, the effects of multi-parameter coupling, including the number of cooling plate channels N, the channel width W, and the inlet flow rate Q, on key factors such as the maximum battery temperature T_max, the average temperature T_avg, the surface temperature difference ΔT, and the cooling hydraulic pressure drop Δp are investigated. The results show that N and Q are the primary factors affecting cooling performance, with W being a secondary factor. The optimal overall performance of the cooling plate is achieved at N=12, W=8 mm, and Q=25 g/s. After optimization, the leaf-vein-like cooling plate shows a 1.32% reduction in T_max, a 0.64% reduction in T_avg, and an 88.2% reduction in Δp compared to the S-type channel cooling plate.

Rack force is a critical parameter in road feel design and steering follow-up control for steer-by-wire systems. Since it is difficult to measure rack force directly in mass-produced vehicles, its estimation becomes a key aspect in steer-by-wire. This article adopts two methods to estimate the rack force. The first method is based on vehicle dynamics, using Luenberger observer to obtain lateral velocity and the tire brush model is applied to analyze steering resistance torque. The second method is based on steering dynamics, involving a dynamic model for the steering actuator and the direct estimation of rack force using a Kalman filter. The results of the two methods are compared through hardware-in-the-loop (HIL) testing, and then a rule-based fusion strategy using multiple variables is proposed to combine the strengths of the two methods. Finally, bench test results show that the proposed fusion strategy effectively improves the accuracy and real-time performance of rack force estimation under different working conditions.

Considering the redundancy advantages of the drive system in all-electric drive-brake electric vehicles, the paper focuses on the electric vehicles equipped with a novel distributed steer-by-wire system. The differential drive assisted steering (DDAS) and assisted return-to-center characteristics are studied after the steering motor fails. The DDAS control strategy for reference steering wheel torque tracking is developed using an adaptive fuzzy PID algorithm. The assisted return-to-center control strategy for steering wheel angle tracking is formulated based on a PID algorithm. To adaptively adjust the assist return torque at various vehicle speeds in this strategy, the PID parameters are optimized using a particle swarm optimization algorithm with adaptive weights and learning factors. An 8 DOF vehicle model, a driver model, a steering system model and a motor model are constructed by using Matlab/Simulink/Simscape. The effects of assisted steering and return-to-center on the studied vehicle are verified through simulations. The results show that the steering wheel torque can be decreased by 54.3%, 48.7% and 40.7% under step, double lemniscate and sine conditions, respectively. The road feel of the vehicle at high speed can be improved effectively. And the differential torque can assist in returning the steering wheel to center under hands-off and return-to-normal conditions.

This article analyzes the structural characteristics and operating principles of a two-stage piston hydrogen pressure reducer with constant output. Based on the principles of statics and aerodynamics, a theoretical calculation model is established to examine the pressure output characteristics and flow properties of this pressure reducer. Theoretically, it has been proven that a two-stage structure pressure reducer provides more stable output pressure than a single-stage pressure reducer. Due to the extremely small size of hydrogen molecules, they are prone to leakage, making dynamic sealing between the piston and the housing very challenging. This article proposes a design strategy to replace dynamic sealing with static sealing, providing guidance for the development of high-pressure hydrogen pressure reducers.