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 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.

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 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 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.

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.

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.

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.

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 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.

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.