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.

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.

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.

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.

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.

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.

To address the challenge of determining control point locations in the cubic Bspline curve algorithm for intelligent vehicle lanechange trajectory planning, an optimization method based on NSGAII was proposed. Lanechanging trajectories for intelligent vehicles were planned using cubic Bspline curves. Under low, medium, and highspeed conditions, the NSGAII multiobjective optimization algorithm was applied to optimize the control point positions of these trajectories. The optimization focused on two key objectives, i.e. minimizing the length of lanechanging trajectories and reducing the average curvature of the trajectories. To verify the feasibility of the optimized trajectory, both simulations and realvehicle tests were conducted. The results show that the mean curvature and trajectory length are reduced after optimization under three different speed conditions. Specifically, the longitudinal displacement and mean curvature are reduced by 12.5% and 12%, 12.5% and 40%, 8.3% and 15.4% for low, medium and high speeds, respectively. In the cosimulation scenario, the optimized trajectory tracking shows a maximum lateral error of less than 0.1 m under low and medium speeds of 10 m/s and 20 m/s, respectively. At high speed of 30 m/s, the maximum lateral error remains below 0.3 m. In the real vehicle tests, the maximum lateral error before optimization is approximately 0.5 m. After optimization, this error is reduced to under 0.4 m, reflecting an improvement of over 20%.

The refrigeration performance of the airconditioning system in heavy commercial vehicles is severely constrained by specific driving conditions and the engine compartment layout. The thermalflow field model and AC cooling system model of a heavyduty truck are built using Star CCM+ and AMESim, respectively. The refrigeration performance is analyzed through simulations under hightemperature idling and hightorque climbing conditions. Experimental validation is conducted in an environmental wind tunnel. The effects of compressor speed ratio, condenser inlet air temperature and flow rate, and blower speed on the refrigeration performance are investigated. The simulation results of the air conditioning system are in good agreement with the bench test results, with a maximum error of 4.7%. At 5 000 r/min, the blower can provide an air flow rate of 470 m³/h for the air conditioning duct. Under the idle condition, the average flow rate on the inlet side of the condenser is 2.77 m/s. However, thermal reverse flow in the engine compartment severely affects the COP and the high and low pressures of the air conditioning system. For the optimized and base versions of the Btype vehicle, the system COP decreases by 8.3% and 15.8%, respectively, compared to the Atype vehicle.

To improve the olfactory experience satisfaction of intelligent cockpit vehicle fragrance users, and to find out the preferred fragrance categories of target user groups, this paper proposes a method for assessing vehicle fragrance preferences based on users' physiological signals. An experimental setup was created to assess the olfactory preferences of vehicle fragrance users, utilizing a smell experience tester as the odorgenerating device. Three commonly used vehicle fragrances, i. e., mint, jasmine and orange, were selected as the test samples. Thirtytwo participants were recruited for the test, and the changes in skin conductivity, pulse and respiration were measured and recorded by using the ErgoLAB multichannel physiological monitoring system. After the initial data processing on the ErgoLAB humancomputer interaction platform, users' subjective preference data were collected by using a semantic difference scale. A comprehensive evaluation model based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was then established to analyze the objective data, and correlation analysis was conducted using Spearman's correlation coefficient to validate the findings. The results show a significant positive correlation between the comprehensive scores of objective physiological data and users' subjective preference ratings, with mint fragrance being the most satisfying to users.

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.