As a key component of the current automotive industry and global intelligence, intelligent vehicles play a crucial role in the entire intelligent system. To explore the key technologies for achieving autonomous driving in intelligent vehicles, researches on the lateral and longitudinal control methods of vehicles have been conducted. By utilizing the vehicle’s kinematic, dynamic models, and tire models, the lateral and longitudinal control of intelligent vehicles is analyzed. A decoupling strategy is adopted to simplify control complexity, ensuring that the vehicle travels safely along the predetermined path. To guarantee the stability of the vehicle in motion, research on longitudinal control is strengthened to optimize the management of vehicle speed. The study of vehicle coupling control aims to overcome the problems caused by vehicle coupling effects, optimizing the precision of lateral and longitudinal control. The advantages and disadvantages of various related control algorithms are summarized, and future development directions for the lateral and longitudinal control of intelligent vehicles are proposed, including deepening the research on lateral control algorithms to enhance safety, optimizing longitudinal control to ensure motion stability, and improving the precision of coupling control to enhance overall control effectiveness.

The intelligent cockpit is a crucial component of automotive intelligence, where multimodal interaction serves as a core function. To explore how AI large models enable smart cockpits to achieve multimodal interaction, this paper leverages the learning and generalization capabilities of AI large models to analyze the technical framework and key technologies of multimodal interaction. It evaluates application cases from leading technology companies and automakers, including Baidu, Huawei, Tencent, and iFlytek, while also undertaking a comparative analysis of their performance. The findings reveal that the incorporation of AI large models into multimodal interaction has markedly enhanced task processing efficiency and precision, thereby enriching the human-machine interaction within intelligent cockpits. In conclusion, this paper explores the challenges and future directions for the application of AI large models in intelligent cockpits, offering valuable insights for the ongoing advancement and integration of AI technology in this domain.

As the development of new energy vehicles deepens, hybrid vehicles have emerged as a relatively rapidly expanding market segment, and Original Equipment Manufactures(OEMs) have augmented their investment in the research and development of hybrid vehicles and hybrid drive units. To develop hybrid drive units more efficiently,the main concept and classification of hybrid vehicles and hybrid drive system are introduced from component composition, structure and motor position.Based on the drive system types of hybrid models in the China market in 2024, the application status of different configuration of hybrid drive systems is described in detail.And based on the development history of hybrid drive units of automobile companies such as Toyota, Honda, General Motors, SAIC and BYD, the iterative process of the hybrid drive unit is systematically sorted out, and the future development trend of the hybrid drive unit is ultimately prospected, so as to provide a reference for enterprises to develop hybrid drive units with independent intellectual property rights.

This study aims to identify the optimal strategy for vehicle platform architecture. It elucidates the concept of platform architecture, defines the critical dimension L114, and presents a platform expansion dimension chain based on L114 for a domestic automotive manufacturer. The specific strategies for the passenger compartment structure of the platform architecture are also analyzed. Comparative analysis of platform universalization rates between L113 and L114 consistent dimensions reveals a similarity in the rates. However, when integrated with whole vehicle safety collision strategy, the L114 consistent dimension strategy emerges as superior. This strategy allows platform models to meet the collision strategy of high models, thereby fulfilling side impact requirements for other models simultaneously, significantly reducing platform development costs.

In order to improve the accuracy of the over-expansion cycle engine air charge model, improve engine performance and improve the dynamic condition mixture control of the vehicle, it is necessary to accurately control the phase of the engine’s intake valve. In this paper, the influence of the Intake Valve Open(IVO) timing and Intake Valve Close(IVC) timing on air charge model is analyzed, the influence of valve phase deviation on the accuracy of the air charge model is analyzed theoretically, and the influence of different valve phase deviations on the accuracy of the air charge model is verified by experiments, in order to meet the accuracy requirements of the air charge model deviation of no more than ±5%, the intake valve phase deviation needs to be controlled within the range of ±1.5 °CA. The influence of the intake phase deviation on the air charge model is corrected by controlling the phase accuracy of the assembly machinery and adding the Electronic Control Unit(ECU) phase deviation adaptive model, and the effect of phase deviation control is verified by experiments.

With the rapid development of intelligent networking and electrification in automobiles, the electromagnetic environment inside and outside vehicles is becoming increasingly complex. To overcome the limitations of traditional vehicle Electromagnetic Compatibility (EMC) laboratory testing methods in comprehensively validating actual operating conditions, a method for evaluating the adaptability of vehicle complex electromagnetic environments based on user scenarios is proposed. It includes self-compatibility testing, static testing, conventional functional dynamic testing, intelligent networked dynamic testing, and test result evaluation. Through actual testing of a certain vehicle model, the results show the effectiveness of this method in resisting interference in complex electromagnetic environments of automobiles in user scenarios, providing support for improving the overall EMC performance of the vehicles.

In response to the need for realism, diversity, and representation of automotive use scenarios in virtual scenes during automotive design reviews, this research is based on High Dynamic Range (HDR) image capture technology. This paper introduces the current application status of HDR images in automotive design reviews. It combines the years of research achievements by the China FAW Group’s visual design team in the field of real-time rendering to propose a comprehensive theoretical and technical scheme for capturing and producing high-quality digital scenes specifically for automotive reviews. The results show that by independently mastering HDR image capture technology, the design team can tailor realistic digital scenes according to design review needs, significantly enhancing the realism and diversity of virtual environments in design reviews. Additionally, it vividly represents automotive usage scenarios, effectively improving the efficiency of design reviews.

In view of the dilemma of traditional car sharing business development which faces heavy assets, heavy operation and single source of income, through the analysis of the characteristics of the car rental market, with the current situation of the development of vehicle autonomous driving and vehicle-infrastructure cooperative technology, combined with the planned characteristics of the vehicle daily rental market segment, this paper proposes a scheme of intelligent networked car sharing platform, by sorting out the platform services and roles, dividing the factory, vehicle source users By sorting out the platform services and roles, 6 service roles are divided: factory, vehicle source user, smart parking lot, contracted driver, daily rental and time-share end-user, corresponding to 3 service links of vehicle source flow, daily rental and time-share; based on vehicle-road cooperation network and vehicle following technology, vehicle dispatching cost is reduced as much as possible. While breaking through the dilemma of the traditional car-sharing business, the intelligent connected vehicle sharing platform solution proposed in this paper is conducive to the promotion of the early-stage technology and the popularization of the “vehicle-electricity separation” model.

In order to study the carbon footprint of power battery in its whole life cycle, an easy-to-operate carbon footprint evaluation system is designed and developed. Based on the Process-Based Life Cycle Assessment(PLCA) method, the carbon footprint evaluation system is designed and developed with Python and Django Framework. Finally, the carbon footprint of lithium iron phosphate(LFP) battery is quantitatively analyzed by using the system. The results show that throughout the entire life cycle of power batteries, the highest carbon emissions occur during the raw material preparation stage, reaching 3 950.098 7 kg CO₂e. The carbon emissions during the transportation phase are the lowest, with 45.661 kg CO₂e.

In order to meet the evolving technological innovations and market demands of the automotive industry, this paper explores the application of knowledge analysis and management model in the field of passenger vehicle labeling so as to improve the development efficiency and resource utilization of passenger vehicle common parts. By constructing the knowledge reuse library and validation model, a new strategy of task-driven common parts knowledge management is proposed. The knowledge analysis management model provides an innovative method for the efficient development of passenger vehicle common parts. The results show that the model can effectively promote knowledge sharing, shorten the development cycle, reduce the cost and enhance the competitiveness of enterprises.