The driving environment of intelligent vehicles often has high uncertainty and complexity, which can lead to accidents and injuries to passengers. In order to improve the safety of intelligent vehicles, three major research methods are currently used to evaluate driving risks, including deterministic methods, probabilistic methods, and machine learning methods. Deterministic methods are traditional binary prediction methods, probabilistic methods can model various uncertainty, and machine learning methods can automatically learn driving behavior, making more accurate assessments of the risk of driving. Future research should combine the advantages of the three approaches to develop safer and more reliable autonomous driving systems.

Through the comparative analysis of cell base materials, various cell integration technologies, and lightweight battery housing solutions, the technical paths for battery density enhancement are elaborated. The improvement in energy density of individual battery cells heavily relies on significant breakthroughs in basic material science. In the post-lithium-ion era, cell densities are expected to reach 1200 W·h/kg, while in the short term, semi-solid battery technology with a cell density of 360 W·h/kg is anticipated to be the first to achieve mass production, enabling electric vehicles with longer driving ranges and higher energy efficiency. Another key technology is to improve cell integration efficiency. Innovative solutions such as Cell-to-Pack (CTP), Cell-to-Chassis (CTC), and Cell-to-Body (CTB) are anticipated to increase cell integration rates to 90% and space utilization to 70%, breaking traditional design limitations and significantly enhancing battery pack energy density. The lightweight design of battery housings is also essential. Lightweight housing design like aluminum alloy extruded profiles, aluminum alloy integrated die-casting, ultra-high-strength steel rolling, and carbon fiber composite materials molding can effectively reduce the overall weight of battery while ensuring performance, thus improving energy density.

Safety analysis is an integral part of the automotive development process, as the complexity of automated driving systems increases, traditional safety analysis methods are facing challenges. Firstly, the advantages and disadvantages of traditional analysis methods, such as Fault Tree Analysis (FTA), Failure Modes and Effect Analysis (FMEA), and Hazard and Operability (HAZOP), are compared with the System Theoretic Process Analysis (STPA), especially the advantages of STPA for the safety analysis of automated driving systems. Secondly, the current status of STPA applications in essential areas, such as Functional Safety, Safety of the Intended Functionality (SOTIF), Cyber Security, and Human Machine Interface (HMI), are discussed in detail. Finally, the application of STPA in automated driving is prospected from the perspectives of expanding the STPA analysis, integration of analysis and verification, and extending application areas.

This paper analyzes the support policies of the fuel cell vehicle industry in the United States, Japan, South Korea, European Union and other regions, and summarizes the support policies represented by the fuel cell vehicle demonstration policy in China. By analyzing and learning from the global fuel cell vehicle policy, combined with the development status of China's fuel cell vehicle industry, this paper provides a reference for further optimizing the top-level policy design and efficiently supporting and guiding the development of fuel cell vehicle related industries in the future.

The ownership of electric vehicle(EV) is rapidly increasing, and charging issues caused by grid abnormal power are consequently emerging. EV adaptability testing has become a hot technology urgently needed in the industry. Grid adaptability testing technology can be applied to vehicle charging systems or AC charging piles, DC charging piles, in vehicle chargers, and high integration charging assemblies to improve the development quality of charging ecological products. To deeply adapt to the demands for charging of users in different scenarios, the automotive industry needs to comprehensively analyze the differences in global power grid systems, sort out the principles of abnormal power grids, and develop testing plans covering user charging conditions in terms of power grid systems, power grid drop, power grid steep rise, and power grid harmonics. Electric vehicle charging products should be fully validated before launch to avoid potential abnormal charging problems about the power grid.

In order to improve user satisfaction, design quality and development efficiency of intelligent cockpit products, and ensure the attractiveness and user stickiness of intelligent cokpit products, this paper elaborates on the automobile HMI system from 3 perspectives: innovation, quality and iteration. An innovative and user-centered automotive HMI design system has been established, which includes a collaborative innovation design system, quality management system such as design self-inspection, consistency testing, and problem management, as well as an HMI self-evolution system. This enables symbiotic and co-growing relationships with users.

The quality problems of new energy vehicles cover a wide range, involve multiple sectors, and demand high technical standards. In order to prevent direct intervention from related sectors when problems arise and address the problems of unclear specialization in the traditional problem management process, low efficiency in problem-solving, lengthy resolution time, and incomplete problem resolution, a system-level problem management method for new energy vehicles is proposed. It involves problem positioning, problem process control, problem summary and accumulation, and problem recurrence preventing. Moreover, it innovatively introduces digital tools and methods into the problem management process. This research shows that this method can improve the efficiency of problem-solving, enhance the depth of problem management, facilitate effective experience accumulation, and strongly support the rapid iteration and technological upgrading of new energy vehicle products.

In order to overcome the difficulties that quite a few patent value evaluation systems exist but difficult to implement and the enterprises are not able to achieve effective hierarchical management, this paper expounds a management scheme based on the actual life cycle of patents, and formulates a hierarchical management strategy of the whole life cycle of patents that is more in line with market demand and enterprise strategy by interpreting the characteristics and influencing factors of high-value patents. Moreover, a 12-digit code is formed through the multi-dimensional evaluation of each patent proposal before, during and after the application. Last but not least, this patent management operation mode is determined according to the coding results, so as to ensure the patents commercial value.

At present, the vast majority of intelligent surface products are made of a single material: plastic, which typically integrated capacitive touch and vibration feedback technologies. In order to meet the needs of users for appearance design and digital experience, various technologies such as capacitive touch, sound feedback, vibration feedback, and pressure sensing are utilized. By covering the surface with 4 materials: plastic, wood, metal and fabric, this paper explores various tactile sensations from vibration sources and surface properties of materials. This exploration aims to enhance the material richness and user experience in intelligent cockpit design.

Digital intelligent transformation serves as the core driving force for the transformation, upgrading and high-quality development of the automotive industry. To address the issues of insufficient collaboration, low efficiency, lack of data governance, and difficulty in knowledge reuse in the R&D field, FAW actively developes the “FAW Digital Twin” to achieve 100% business digital twinning. Moreover, FAW also actively engages in business digital intelligence transformation, outlining a comprehensive methodology for business digital twinning and independently developing a cloud-native, modularized, and service-oriented designer workbench. The operationalization of the digital twinning in R&D business has significantly improved the research and development efficiency and quality. The platform collaboration efficiency has enhanced one time, the average design change period has been shortened by half, and the average problem resolution period has been reduced by 30.69%.