As a key technology of next-generation power battery, solid-state batteries can meet the full-scene, all-climate, and high-safety requirements of new energy vehicles. To support the high-quality development of the solid-state battery industry, it is essential to systematically sort out the main technical routes of solid-state batteries, as well as the policy support and development status of domestic and foreign enterprises. The common technical and cost-related problems in the industry should be identified. The development of China’s solid-state battery industry is confronted with challenges such as patent constraints, an incomplete standard system, and potential impacts on existing liquid-state battery industries. In the future, it is urgent to plan and coordinate efforts, mobilize industry forces, and take multiple measures to accelerate the technological breakthroughs and industrial application of solid-state batteries.

To enhance driving performance during drive mode transitions in battery electric vehicles, a controllable disengagement device is integrated between the front axle electric drive assembly and the differential, enabling timely switching between four-wheel-drive (4WD) and two-wheel-drive (2WD) modes. First, a dual-motor four-wheel-drive pure electric vehicle configuration incorporating the disengagement device is constructed, and the energy flow characteristics of 2WD and 4WD modes are analyzed. Second, mode transition conditions are designed based on driving scenario requirements, and a phased torque transfer control strategy is developed. Finally, seven categories of drivability evaluation scenarios are proposed, along with objective evaluation metrics. Experimental validation demonstrates that the proposed configuration significantly enhances the vehicle’s driving performance and ride comfort.

In response to China’s “Transportation Power” strategy and the urgent need for breakthroughs in core technologies of Intelligent Connected Vehicle (ICV), addressing key challenges such as insufficient regional collaborative innovation and delays in building industry-wide patent barriers, this paper examines the development status and relevant policies of environmental perception technology. A comprehensive patent analysis is conducted on Jiangsu Province from multiple dimensions, including application trends, geographical distribution, patent mapping, and examination cycles. Finally, policy recommendations are proposed to optimize the patent layout in Jiangsu’s ICV sector, aiming to enhance its industrial competitiveness and provide valuable insights for future development.

Light tactical vehicles are the backbone of rapid combat and rapid transfer on the modern battlefield. At present, there is a certain gap between the development of light tactical vehicles in China and that in foreign countries. In order to clarify this gap and promote the development of light tactical vehicles in China, the research progress of foreign mainstream light tactical vehicles is analyzed. It is found that the development of foreign light tactical vehicles has the characteristics of vehicle family, high mobility, high protection, electrification and intelligence. Based on this, the strategic suggestions for the development of light tactical vehicles in China are put forward in order to provide reference for reducing the disparity with the international advanced level.

When using the Random Forest (RF) algorithm to estimate the road adhesion coefficient, there are issues such as insufficient optimization of feature selection during model construction and insufficient diversity in the ensemble of decision trees. To address this issue, a method based on Particle Swarm Optimization (PSO) algorithm to improve RF is proposed, and the algorithmic process is presented. An RF model for estimating the road adhesion coefficient is established, and the PSO algorithm is used to optimize the parameter configuration of RF, including key factors such as the number of features of each tree and the number of trees, so as to enhance the diversity and generalization capabilities of the model. At last, a joint simulation model is built on the MATLAB/Simulink platform for experiments. The comparative experimental results show that the random forest road adhesion coefficient estimation method based on PSO-RF can overcome the limitations of the traditional RF methods, and both the estimation accuracy and stability have been significantly improved.

In order to improve the efficiency and quality of parking path planning, a study on intelligent driving parking path planning algorithms has been conducted. By reviewing the parking planning methods from recent domestic and international academic research, based on their characteristics the parking path planning algorithms are categorized into 5 types: graph search algorithms, sampling-based algorithms, intelligent algorithms, curve interpolation algorithms, and optimal control algorithms. The advantages and disadvantages of these 5 types of algorithms are analyzed, and the application of fusion algorithms in specific environments is explored. The rationality and effectiveness of existing solutions are evaluated in terms of planning efficiency and path curvature. Furthermore, future development trends in parking path planning algorithms are discussed. The study concludes the following: (1) Graph search algorithms offer the advantages of globally optimal paths and good real-time performance but suffer from poor path continuity and high complexity in high-dimensional spaces. (2) Sampling-based algorithms have the advantages of probabilistic completeness and high search efficiency in high-dimensional spaces but have large memory consumption, significant randomness, and cannot guarantee path curvature. (3) Intelligent algorithms have strong learning capabilities based on samples and strong iteration capabilities but have high training costs, poor dynamic adaptability, and poor real-time performance. (4) Curve interpolation algorithms based on optimization are easy to calculate but cannot guarantee the continuity of curvature. (5) Optimal control algorithms based on optimization can handle complex optimization problems but cannot guarantee timeliness and are prone to falling into local minima. Fusion algorithms through complementary advantages can better adapt to vehicle constraints and environmental constraints, achieving efficient and safe parking path planning.

To meet the demand for precise environmental perception in intelligent driving, this paper focuses on the application and development of intelligent driving environment perception sensors, analyzing their foundational position in system architecture. It discusses the working mechanisms, application scenarios, and advantages and disadvantages of various sensors such as visual sensors, LiDAR, millimeter-wave radar, and ultrasonic radar, analyzing the impact of multi-sensor fusion on improving perception accuracy and autonomous driving reliability. In the future, environmental perception sensor technology for intelligent vehicles will focus on 6 major directions: new sensing materials, intelligent adaptability, energy-efficient design, fault diagnosis, real-time calibration, and environmental impact suppression. Sensors will continue to evolve towards miniaturization, integration, and adaptive low-power design. Breakthroughs will be made in areas such as high-precision vehicle cameras, solid-state LiDAR, and innovative integration of ultrasonic radar, aiming to enhance sensing accuracy, real-time performance, and reliability to meet the increasingly complex demands of driving environments.

To address the issues of poor tracking accuracy and driving stability caused by uncertain model parameters, modeling errors, and external disturbances, this paper proposes a motion control method for intelligent vehicles based on horizontal and longitudinal dual preview PID compensation. Firstly, an LQR（Linear Quadratic Regulator lateral motion controller is established using a path tracking error model to address angle compensation. This is achieved through a lateral previewing PID controller, utilizing the lateral deviation of the preview point as input. Secondly, a longitudinal motion controller is designed based on model-independent fuzzy theory to track vehicle speed. A previewing PID controller is established to compensate for vehicle speed using the longitudinal deviation of the preview point as input. Finally, the proposed method is validated through simulation under with uniform accelerated double lane shift. Simulation results demonstrate that the intelligent vehicle motion controller based on horizontal and longitudinal dual preview PID compensation achieves higher tracking accuracy and driving stability under conditions of high speed and large curvature during double lane shifts.

Aiming at the Electromagnetic Interference (EMI) caused by DC/DC converter in switching power supply, the electromagnetic interference source of DC/DC converter is proposed and analyzed, and the EMI propagation path generated by the converter is constructed. The conducted interference under the influence of pure circuit and Printed Circuit Board (PCB) is compared and analyzed, and the measures of adding Π filter to suppress the electromagnetic interference of DC/DC converters are proposed, and the simulation of CST software is used to verify the suppression results. The results show that the suppression method proposed in this paper can effectively suppress the electromagnetic interference of switching power supply.

To systematically summarize the research status of multi-source fusion environmental perception technology for intelligent vehicles, this paper compares and analyzes the principles and characteristics of various sensors including cameras, Light Detection and Ranging (LiDAR), and millimeter-wave radar. The environmental perception technologies based on single-sensor approaches (such as camera-based object detection and LiDAR point cloud processing) and multi-sensor fusion strategies (data-level, feature-level, and decision-level) are reviewed with their technical bottlenecks and challenges. Typical algorithm cases are also discussed to explore their application effectiveness. The research findings indicate that: single sensors exhibit inherent limitations, such as cameras’ dependency on illumination conditions and LiDAR’s high cost with insufficient semantic information acquisition capability, as well as multi-sensor fusion technology significantly enhances environmental perception robustness through complementary advantages, yet challenges like data heterogeneity and insufficient real-time performance still remain unresolved. To meet the perception demands of complex scenarios, future development will focus on intelligent multi-modal fusion algorithms, cost-effective sensor integration, and V2X collaborative perception technologies.