The steer-by-wire technology is the key technology for the future development of pure electric vehicles, as the key execution system of the X-by-wire chassis, the mechanical connection is canceled, the steering operation is directly transmitted by electrical signals, the control mode is more flexible, the modular structure can be realized, the coupling and control freedom between the steering system and other subsystems of the chassis are improved. Therefore, the steer-by-wire system is an important part of the drive-by-wire chassis system. In order to provide a useful reference for R&D units and enterprises related to steer-by-wire technology, this paper uses patent analysis method to sort out key technical points in steering strategies, integrated systems and other aspects from the perspective of patent disclosure trend and patent technology composition. This paper also analyzes the development status of steer-by-wire technology of pure electric vehicles from macro and micro perspectives, proposes the key directions for future patent layout in the field of steer-by-wire for pure electric vehicle in China.

With the increasing development of braiding process and the continuous improvement of automation level, three-dimensional braided carbon fiber composite materials have become ideal candidates for the design and preparation of complex special-shaped components with main bearing capacity in the high-tech field of manufacturing. In order to further explore the application prospects of 3D braided carbon fiber composites in the automotive industry, the paper investigates the effects of structures, temperatures, load modes on the mechanical properties and failure mechanism of 3D braided composites and expounds the current research status and progress of 3D braided composites at home and abroad, which puts forward suggestions for their application development.

Due to the popularity of data acquisition tools, the whole life cycle of automobile’s “selection, purchase, use, management, repairment and replacement” will generate data, from which key information needs to be extracted to reflect market changes, competitive enterprise dynamics, technology trends. In this study, data processing processes and tools in the industry are investigated. Key technologies such as dimensionality reduce, data fusion and data governance are integrated by using the coupling relationship of various stages to connect data resources with each other through ID identification. It is proved the point that through the visualization platform and analysis report, data analysis results and expert wisdom can be aggregated to provide decision support for product planning and market strategy of automobile and parts enterprises, and better digitize the value chain of automobile.

At present, there are some problems in automobile manufacturing, such as fast update of function performance, difficult cost control and fast change of consumer aesthetic. In order to solve these problems, it is necessary to integrate the concept of synchronous product development in vehicle production process development. Through the use of digital and automated review processes, the aim is to shorten and improve the product development process, increase the efficiency of process review, closely integrate design and production at the initial stage of new product development. This paper systematically reviews the demand analysis and overall design of the digital process of synchronous development, classifies and summarizes the data with computer data structure, and summarizes 5 basic review models, focusing on the method of transforming review data into review model.

With the establishment of China’s dual carbon goals and the continuous tightening of regulations, commercial vehicles have also embarked on a path of transformation and upgrading. The domestic commercial vehicle market is gradually saturated, the subsidy policies for new energy commercial vehicles continue to decline. In order to study the development trends of new energy commercial vehicles in China, through literature review and investigation research, the development of commercial vehicles in China is summarized as follows: (1) The commercial vehicle market has shifted from incremental development to stock competition stage, (2) Overseas markets have become new growth poles, (3) The development of new energy commercial vehicles is shifting from policy driven to policy market dual driven, (4) In the short term, multiple technological routes will continue to coexist and evolve, (5) Innovative business models will usher in rapid development.

The fierce competition in the commercial vehicle market is often determined by the changing customer demands, which in turn determines the market position and development direction of the enterprise. This paper proposes a commercial vehicle customer demand analysis method based on data mining technologies. By processing and analyzing customer data, the implicit customer demand characteristics are mined, a customer demand model is established to provide decision-making basis for enterprise customer demand analysis. This paper first introduces the importance and status quo of commercial vehicle customer demand analysis, then details the application of data mining technologies in customer demand analysis, including data preprocessing, data mining algorithm selection and customer demand model establishment. By processing and analyzing experimental data, this paper establishes a commercial vehicle customer demand model, verifies and optimizes the model. The experimental results show that the commercial vehicle customer demand analysis method proposed in this paper can accurately mine customer demand characteristics and provide more objective and scientific decision-making basis for enterprises. This method can be widely used in customer demand analysis and product design in the commercial vehicle market.

Virtual Reality (VR) Technologies have developed rapidly in recent years and have been widely reflected in all walks of life. In order to better promote the informationization and intelligence of the automobile industry, this study discusses the application and development trend of VR technologies in the automobile industry. The results show that VR technologies have broad applications and prospects in automobile design, production, sales, safety training and entertainment. With the continuous progress of technology, future automobile enterprises will pay more attention to the research and applications of VR technologies.

In the study of stability control for intelligent vehicle, the changes of model parameters and external disturbances such as lateral wind and road adhesion coefficient, which may lead to instability and poor robust stability. In order to improve the riding stability of intelligent vehicles under random interference, a stability control strategy based on Active Front Steering（AFS） and Direct Yaw moment Control（DYC） coordinated control is proposed. Firstly, a two-degree-of-freedom dynamic model of the vehicle is established. Secondly, a front wheel active steering controller based on robust adaptive sliding mode control is designed by combining adaptive law and sliding mode control to suppress the disturbance in the vehicle running, which solves the robust control problem of nonlinear systems with time-varying parameters, uncertainty and unknown disturbance. The yaw moment controller based on genetic optimization fuzzy control is used to solve the additional yaw moment to restore the stable running of the vehicle. Based on Simulink and CarSim simulation platform, the stability control method is simulated under step disturbance, sinusoidal disturbance and mixed disturbance. The results show that the coordinated control significantly reduces the additional yaw torque output of the vehicle, which can be well inhibited interference and fully ensure riding stability of the vehicle.

In the environment that the passenger car industry is becoming increasingly prosperous, it is crucial for each automobile enterprise to accurately grasp the development direction of the industry and formulate suitable production goals. In this paper, historical sales, macroeconomic indicators and online search keyword data are selected as variables to establish a variety of sales prediction models for the overall passenger car market in order to improve the prediction accuracy of passenger car sales. Through comparative analysis, the Gradient Boosting Decision Tree (GDBT) algorithm model considering the above 3 variables has the best effect and its Mean Absolute Percentage Error (MAPE) is 10.35%. The model obtained in this paper can help automobile enterprises understand the development of market trends, make targeted production planning and provide a new reference model for the research of sales forecast.