The strategic objectives of "carbon peaking" and "carbon neutrality" have brought significant challenges for reducing carbon emissions from passenger cars. Therefore, formulating a technical route for carbon reduction in passenger vehicles is a crucial task for the future sustainable development of the automotive industry. Firstly, it involves analyzing data on new passenger car production, stock and scrap rate up to 2060, as well as carbon intensity data of electricity industry. Subsequently, the carbon emission data from various power sources for passenger cars is compiled. An analysis model is then established, along with evaluation dimensions and indicators, to assess passenger cars with different power sources. Finally, the analysis focuses on the sensitivity of individual power technologies in reducing carbon emissions. The findings indicate that pure electric vehicles (EVs), plug-in hybrids (PHEVs), and hybrid electric vehicles (HEVs) play a pivotal role in rapidly reducing carbon emissions to achieve "carbon peaking". Additionally, carbon-neutral fuel power and fuel cell vehicle technologies demonstrate advantages in achieving "carbon neutrality". In conclusion, achieving "carbon peaking" and "carbon neutrality" in powertrains requires a combination of multi-source technology pathways within specific timeframes for implementation. Furthermore, transitioning policies and regulations from "double credit" to"carbon credit" based on technology neutrality is essential to realize the dual-carbon technical route for passenger cars.

To address the lack of effective monitoring and fault tolerance mechanisms in the service-oriented AUTOSAR Adaptive Platform (AP) and to ensure high stability and safety of the software system in case of faults, this study takes the automotive basic software platform AP as the research object. A complete monitoring and fault tolerance mechanism is designed through the Automated Valet Parking (AVP) software system. By analyzing traditional software architectures, other service-oriented architecture monitoring solutions, and the characteristics of AP, a monitoring scheme for AP is designed to supervise the platform infrastructure (processors, networks, memory) and service states (response times). A data display module developed with Qt, using the LT protocol, implements the collection and display of real-time monitoring data. Furthermore, a service call chain tracing method supporting the SOME/IP protocol is proposed, enabling analysis of complex service call relationships within service-oriented architectures. The results indicate that the scheme can monitor and analyze service failures within the AVP software system and implement fault tolerance mechanisms in case of failures, thus enhancing system reliability.

To adapt to new trend of software-defined vehicle development and promote transformation and upgrading of automotive electronic and electrical architecture, this paper delineates the pivotal factors driving software-defined vehicle development and investigates the impending evolution of vehicle electronic and electrical hardware architecture. The paper examines the conceptual framework, hierarchical structure, and deployment strategies of Service-Oriented Architecture (SOA). Building upon these insights, a holistic technical framework for software-defined vehicle is developed, and the study delves into novel development methodologies and potential application domains. This research offers a technical blueprint for the design of future automotive electronic and electrical architectures.

In order to overcome the challenges of traffic sign detection and recognition, such as small targets, diverse sizes, difficulty in obtaining relevant feature information, and susceptibility to complex background interference, traffic signs are detected and recognized. a deep learning method for traffic sign detection and recognition is proposed based on the YOLOv5s network. Addressing the issue of current traffic sign recognition algorithms struggling to identify small targets in complex backgrounds, we have integrated a shuffle attention mechanism into the C3 layer at the end of the YOLOv5s backbone network. This integration introduces a traffic sign detection and recognition algorithm that relies on an attention mechanism. This enhances the ability to focus on key areas and effectively eliminates background noise interference. To address the limitations of feature fusion in current object detection algorithms when dealing with traffic signs of varying sizes in images, we propose a weighted feature fusion network algorithm. This algorithm performs weighted fusion of shallow feature maps containing rich semantic information in the backbone network with medium and large target detection layers, enhancing the fusion ability of multi-size features. Experimental results on the traffic sign detection dataset CCTSDB 2021 show that the enhanced algorithm achieved a 0.5 percentage points increase in precision, a 3.6 percentage points increase in recall, and an average precision improvement of 2.8 percentage points compared to the original YOLOv5s method. Additionally, the detection speed reached 123.46 frame/s. Therefore, the proposed algorithm effectively enhances the accuracy of traffic sign detection and recognition while maintaining a original detection speed.

Fuel cell vehicle (FCV) is an important direction for the sustainable development of the automotive industry in the future. However, existing test and evaluation standards for FCVs have not thoroughly examined the types of faults and their classification. There is a lack of a unified fault grading classification scheme. To solve this issue, a comprehensive set of grading classification evaluation indicators for FCV faults is proposed to standardize the levels of related faults. The focus is on researching methods for classifying FCV faults in the case of lack of data. Based on factor analysis and fuzzy set theory, a fault mode classification evaluation method for FCVs under data scarcity conditions is proposed, offering guidance for the classification of FCV fault levels.

Taking wet clutch as the research object, through theoretical calculation of coupling steel temperature of clutch, the clutch torsional and convective heat transfer coefficients are estimated by establishing the clutch torsional thermodynamic model. The main factors affecting the accuracy of calculation are extracted, and a physical model for dynamic measurement of steel sheet temperature is developed for key factors. Based on the measured results, the distribution law of axial temperature field of different steel plates is analyzed, the cooling oil passage of the clutch is optimized, the maximum temperature difference of different steel plates is reduced by 74.6%, which effectively improves the transmission torque of the clutch. At the same time, the clutch temperature model in-depth calibration under different oil temperature, torque and speed difference is carried out, which realizes the precise torque transfer ability of the transfer case while still secures the function of reliable thermal protection function.

The integrated die-casting technology for new energy vehicles has led the innovation in vehicle manufacturing processes. To address the dual objectives of vehicle lightweighting and cost control in new energy vehicles, the crucial role of aluminum alloy materials in the process of automobile lightweighting is elaborated. It dissects the principles, benefits, and drawbacks of the integrated die-casting process, alongside its current adoption in both domestic and international contexts. The paper also delves into the technological challenges in material development and process innovation within integrated die-casting. Additionally, feasible suggestions for the development and application of integrated die-casting technology are put forward, aiming to promote the widespread application of aluminum alloy materials in automobile manufacturing and provide powerful references for the lightweight, energy-saving, and environmentally friendly development of the autombile industry.

Focusing on the application of the non-metallic painting process of automotive exterior parts, this paper introduces the principle of the non-metallic painting process, the constitute of the non-metallic painting production line, the classification of paint materials and the development trend of the non-metallic painting process. The non-metallic painting process is a common surface treatment method for non-metallic parts. With the help of automatic painting robots, the surface of exterior parts is treated according to design requirements. The functions of paint are mainly divided into 3 categories: protection, decoration and function. The functional requirements of exterior decoration parts are stricter. In addition to meeting the thermodynamic performance, they also need to have excellent UV resistance and corrosion resistance. The research trend of paint materials is to study a new type of paint integrating environmental protection, decoration and performance in use. The development direction of the non-metallic painting process refers to intelligent and environmentally friendly.