To address the issue of high energy consumption in battery electric buses at signalized intersections, this paper proposes an eco-driving optimization model based on the Twin Delayed Deep Deterministic (TD3) policy gradient algorithm. First, a simulation training platform is developed using SUMO, which balances energy consumption, travel efficiency, comfort, and safety in a multi-objective optimized reinforcement learning reward function. Next, an eco-driving optimization model is created within the TD3 framework, tailored to the operational characteristics of electric buses at signalized intersections, and its parameters are trained. Finally, the performance of the proposed model is validated against the classic intersection passage strategy, Green Light Optimal Speed Advisory (GLOSA). The results show that the proposed eco-driving strategy reduces energy consumption by 9.82%, 26.13%， 19.00% and 14.51% in four typical intersection scenarios, while also maintaining vehicle safety, comfort, and travel efficiency.

Parking tracking accuracy directly affects parking safety, efficiency, and available parking space. Currently, most autonomous parking path tracking relies on model-based feedback control. High tracking errors can arise from a decline in the algorithm's control performance due to uncertainties in system model parameters. In this paper, a feedforward control approach based on iterative learning was developed to reduce the impact of model parameter uncertainty on parking path tracking. Considering that iterative learning control of the system in the time domain was usually affected by the actual speed of the actuator, the system was transformed from the time domain to the space domain, which was related to the desired path. Due to the difficulty in measuring some state variables in the system model and the system's failure to meet the D-type iterative learning rate convergence condition, the design criteria for an H_∞ observer were proposed to accurately estimate state information. Meanwhile, an augmented system with observation errors was constructed to implement iterative learning control, which further reduced the parking path tracking error based on the initial parking tracking information from linear quadratic optimal control (LQR). Finally, a hardware-in-the-loop (HIL) test was established, which proved that the proposed method had excellent practical application potential. The experimental results show that after several iterations, the proposed control method tracks the desired path more accurately than the initial LQR control.

Aiming at the instability of high-speed vehicles in windy and rainy environments, the Euler-Lagrange method was used to numerically simulate the external flow field of automobiles under such conditions. The aerodynamic characteristics was investigated at different side wind speeds and rainfall intensities. The whole vehicle dynamics model was established and subjected to aerodynamic loads. The lateral displacement of the vehicle under the windy and rainy conditions was calculated. The results show that, at a constant side wind speed, rainfall increases the drag vortex and the wake vortex diffusion region on the leeward side. Changes in the vortex structure increase the negative pressure area on the leeward side of the body and reduce the the trailing airflow velocity. As a result, aerodynamic drag and side forces are increased. Additionally, changes in road surface adhesion further reduces the vehicle's lateral stability.

To address the difficulty in determining the optimal return angular speed at various vehicle speeds in traditional active Return-to-Center (RTC) control, a new active RTC control method for steer-by-wire systems is proposed, combining a return speed reference model and sliding mode control. A time-window-based mechanism for determining the active return-to-center state is designed. The return speed reference model is established based on the tire aligning torque, and an active RTC sliding mode control strategy is developed accordingly. Hardware-in-the-loop simulation results indicate that the proposed active return state determination mechanism can accurately switch system states, the return speed reference model exhibits high accuracy, and the sliding mode control strategy effectively ensures that the steering wheel reliably follows the reference return speed.

Based on the experimental validation of a single-cell heat generation model, this paper proposes a thermal management battery module for a liquid-cooled system integrated with phase change material (PCM). The effects of the number of cooling channels, flow rate, cold channel arrangement and cooling plate thickness on the maximum temperature and temperature uniformity of the battery pack are quantitatively studied by using numerical simulation methods. The results show that at the discharge rate of 4 C and 35 ℃, altering the coolant flow rate in three cooling channels can greatly affect the maximum temperature and the maximum temperature difference of the battery module. Furthermore, once the coolant flow rate exceeds 0.2 m/s, the heat dissipation performance of the battery module does not show significant improvement. With the same number of cooling channels, the best temperature uniformity between battery packs and along the axial direction is achieved with a staggered distribution of coolant inlets and outlets. When the cooling flow rate and the number of cooling channels remain constant, increasing the thickness of the cooling plate can reduce both the maximum and minimum temperatures of the battery module. However, once the thickness reaches 8 mm with three cooling channels, further changes in temperature become negligible.

To reduce the impact on the vehicle and minimize brake force fluctuations during mode transitions in the electro-hydraulic composite braking system, a control strategy for electro-hydraulic composite braking has been proposed, focusing on dual-motor driven electric vehicles with both front and rear wheel drive. This strategy includes a wheel cylinder pressure following control approach and a motor compensation control approach. The wheel cylinder pressure control, activated during hydraulic brake intervention, utilizes H_{\mathrm{\infty }} robust control to enable the hydraulic braking system to swiftly and precisely manage the magnitude of braking force. As a result, the braking system is stabilized, ensuring reliable vehicle control. To enhance braking comfort, a fuzzy PID-based motor compensation control strategy is employed during the intervention or withdrawal of hydraulic and regenerative brakes. This strategy reduces the impact on the composite braking system caused by variations in system response. The simulation conducted on the Simulink-AMESim-CarSim platform has verified that the hydraulic braking system can rapidly and accurately follow the target braking force. Furthermore, the results show that compared to an uncontrolled situation, the fluctuation in braking force is reduced by 90% and the shock is reduced by 74%, thereby significantly improving braking smoothness.

Lightweight alloy wheels reduce vehicle weight, enhance fuel efficiency, and improve power performance, braking efficiency, and suspension system responsiveness. Through simulated impact testing of lightweight alloy wheels, engineers can gain a more comprehensive understanding of their performance under various road conditions, and conduct corresponding design optimizations to enhance safety and durability. This paper systematically reviews the current state of research on 13degree and 90degree impact simulations of wheels, both domestically and internationally. From the perspectives of simulation efficiency, accuracy, and convergence, it discusses the influence of tire models, tire pressure models, and contact properties on the 90degree impact simulations. The paper also introduces the application of automated simulation and deep learning technologies in wheel impact simulation research. The combination of these technologies help achieve standardization, normalization, automation, and intelligence in impact simulations.

A geometric obstacle avoidance model is proposed for traffic environments with dynamic obstacles, which can describe the relationship between vehicle and obstacle movements. By decomposing the spatial distance between the vehicle and the obstacle into two directional components and incorporating their relative speed, three key elements are obtained. Based on these elements, an improved obstacle avoidance model is developed. Using the Model Predictive Control (MPC) principle, the discrete vehicle kinematics model is employed as the predictive model. The objective function and constraints are constructed by adopting the Frenet coordinate system and considering factors such as road boundaries, the vehicle's mechanical structure, driving safety and comfort. Finally, a nonlinear programming problem is established and solved. In this paper, the SF5 is used as the experimental vehicle, with hardware and sensors installed to build an autonomous driving platform. A trajectory planning algorithm was deployed on a ROS and Matlab/Simulinkbased software platform for realworld vehicle testing. The results show that this method not only ensures smooth obstacle avoidance, but also produces a reasonable and comfortable driving path.

To improve the safety and comfort of autonomous vehicles during lane changes, the proposed approach incorporates the impact of lanechanging on local traffic flow and introduces a lanechanging inertia factor based on traditional decisionmaking models. To overcome the limitations of decoupled longitudinal and lateral trajectory planning, a joint constraint planning approach is proposed. Using dynamic programming and quadratic programming algorithms, the current lateral trajectory curvature is adjusted based on the longitudinal constraints from the previous frame. In the longitudinal planning process, key obstacles are filtered based on the current lateral planning results, and curvaturebased speed constraints are

To improve the traffic flow efficiency of vehicleroad cooperative adaptive cruise vehicles at signalized intersections, a model predictive control algorithm considering traffic signal status is proposed. When following a vehicle through an intersection, the vehicle first uses V2X to obtain information about the traffic signal, including its position, color and countdown timer. The vehicle's longitudinal kinematics model is used to link the acceleration request from the controller to the vehicle's future state. An objective function considering both the vehiclefollowing state and the traffic signal is designed. Finally, the output acceleration request is calculated through nonlinear optimization, simulating the behavior of human drivers when following other vehicles through signalized intersections. The simulation results show that compared with ACC vehicles, the vehicle using this algorithm achieves higher traffic efficiency while maintaining safety when passing through signalized intersections. This research provides a theoretical basis for prioritizing efficiency in the design of CACC vehicle control algorithms, and offers practical insights for developing an efficiency mode in systems that include both economic and efficiency modes.