Due to the high mobility of vehicles and the Doppler effect, the applications of autonomous driving face a great challenge. Moreover, with the massive number of vehicles connected to networks, existing network resources are experiencing bottlenecks. To overcome these urgent issues, this work aims to study the physical layer security (PLS) for the reconfigurable intelligent surfaces (RIS) assisted Vehicle-to-Vehicle (V2V) communications, considering different eavesdropper locations. We introduce two different eavesdropping models: the one is eavesdropper located near to the source vehicle, who will be wiretapped by the direct downlink; the other one is that eavesdropper is located near the receiving vehicle and in the beamforming region, hence it will wiretap the information from the reflected link of RIS. Specifically, the upper bound for security capacity and the approximate security outage probability (SOP) of such two scenarios are respectively derived, under the double Rayleigh fading channels. By means of the numerical and simulation results, it shows that the security performance of the PLS of RIS-assisted communications is significantly improved with increasing of the transmit power, distance between nodes, number of RIS elements and secrecy capacity threshold. The analysis underscores the potential of optimizing RIS’s location and size to significantly bolster the wireless communication quality in Internet-of-Vehicles (IoV) environments. This integration not only mitigates the challenges posed by high mobility and Doppler effects but also effectively counters eavesdropping attempts, ensuring robust security in IoV networks.

Battery voltage plateau characteristics are crucial for designing and controlling battery management systems. Utilising the plateau period attributes to their fullest extent can enable optimal battery control, enhance battery performance, and prolong battery lifespan. This research aimed to investigate the performance of cylindrical ternary lithium batteries at various discharge rates, focusing on the variations in terminal voltage, capacity, and temperature. The battery performance at different discharge rates was meticulously examined through cyclic charge/discharge experiments. The convexity of the voltage curve was used to analyse the voltage plateau characteristics at different rates. The findings revealed significant differences in battery performance under varying discharge rates. Higher discharge rates resulted in shorter discharge times and lower battery voltages at corresponding residual capacities. The discharge time, capacity, and voltage during the plateau phase decreased as the discharge rate increased. At discharge rates of 1 C, 3 C, 5 C, 7 C, 9 C, and 11 C, the proportion of discharged battery capacity ranged from 86.45% to 78.42%. At the same time, voltage and temperature variations during the plateau period decreased significantly compared to those before and after discharge. This research provides a crucial reference point for advancing battery design and thermal management systems.

To enhance the accuracy and robustness of lithium-ion battery state-of-health (SOH) prediction, this study proposes a dual-mode intelligent optimization BP neural network model (AO–AVOA–BP) which integrates the Aquila Optimizer (AO) and the African Vulture Optimization Algorithm (AVOA). The model leverages the global search capabilities of AO and the local exploitation strengths of AVOA to achieve efficient and collaborative optimization of network parameters. In terms of feature construction, eight key health indicators are extracted from voltage, current, and temperature signals during the charging phase, and the optimal input set is selected using gray relational analysis. Experimental results demonstrate that the AO–AVOA–BP model significantly outperforms traditional BP and other improved models on both the NASA and CALCE datasets, with MAE, RMSE, and MAPE maintained within 0.0087, 0.0115, and 1.095%, respectively, indicating outstanding prediction accuracy and strong generalization performance. The proposed method demonstrates strong generalization capability and engineering adaptability, providing reliable support for lifetime prediction and safety warning in battery management systems (BMS). Moreover, it shows great potential for wide application in the health management of electric vehicles and energy storage systems.

In order to further improve the power and fuel economy of hybrid vehicles, this paper proposes a method of hybrid vehicle powertrain matching by combining orthogonal tests with Cruise software, supplemented by the control strategy formulation of critical components of the whole vehicle on the MATLAB/Simulink platform. Considering the influence of vehicle engine, electric motor, battery and overall mass on the powertrain design, the L₉(3⁴)-type orthogonal table is selected for the orthogonal test design. After verifying the feasibility and accuracy of each design solution of the powertrain, the different design solutions are simulated for power and economic performance. Finally, the best performance indicators of the vehicle are as follows: the maximum speed is 183.35 km/h, the 0–100 km/h acceleration time is 6.87 s, and the maximum degree of climbing is 39.65 percent. The fuel consumption of 100 km is 3.47 L. The optimal solution was compared with the third-generation Harvard H6 and AITO M5 in terms of fuel saving and emission reduction, and it was found that for every 15,000 km driven, it is expected to save 469.5 L of fuel and 109.5 L of CO₂, respectively, which can reduce fuel use and emission by about 1051.21 kg and 245.17 kg CO₂, respectively. This simulation experiment can reduce the workload of traditional power system matching. It can provide ideas for power system matching and optimization for Corun CHS Technology Co., Ltd. (Foshan City, Guangdong Province, China) and offer a certain degree of reference for hybrid vehicle power system design and simulation.

The global energy demand in the world continues to grow and environmental pollution caused by fossil fuels becomes increasingly serious, the development and utilization of new energy sources has become a hot topic of global concern. The development and utilization of new energy sources is one such possible solution, which can provide clean, renewable energy and thus reduce the impact on the environment. Hydrogen, as a clean, productive, carbon-free secondary energy source, has the potential to be used as a fuel and essential substance for different fields such as heating, transportation, industry, and power generation. As the international community strives to achieve a shift toward a more eco-conscious and sustainable future, hydrogen has received extensive research and attention due to its abundant resources and environmentally friendly properties. The main objective of this study was to describe and comparatively analyze the efficient production, accumulation, distribution and storage of hydrogen. Today the production of hydrogen is centered on the creation or extraction of hydrogen from primary energy sources. The accumulation of hydrogen involves the preservation of surplus hydrogen for subsequent utilization. The distribution of hydrogen encompasses the conveyance and delivery of hydrogen. The storage systems for hydrogen pertain to the technologies and infrastructure employed to retain hydrogen for future deployment. In complex, these constituents establish a comprehensive hydrogen value chain that facilitates the generation, preservation, and distribution of hydrogen as a sustainable and environmentally friendly energy solution. Keywords: comparative analysis, hydrogen, production, accumulation, distribution, storage systems.