Hardware-in-the-Loop simulation technology, with its advantages of high safety, strong condition reproduction capability, and high testing efficiency, has become an indispensable core verification method in the development process of electric drive systems. In response to the high cost of traditional test benches and the difficulty in simulating extreme conditions, this paper has constructed an HIL test system for electric drive systems, enabling separate HIL testing of MCUs and TCUs, as well as combined HIL testing. Through typical conditions, transient dynamic responses, and extreme fault injection tests, the real-time performance, reliability, and stability of this HIL test system have been verified.

The article studied a type of copper-clad aluminum (aluminum alloy) conductor with properties intermediate between copper wires and aluminum wires. It introduced the manufacturing process, conductor parameters, and explored the feasibility of its application in the high-voltage wiring system of new energy vehicles. Through aging tests, vibration tests, environmental durability tests, and current temperature rise tests, this copper-clad aluminum clad conductor demonstrated excellent performance, providing a new solution for the selection of conductor materials for high-voltage wiring systems in new energy vehicles.

The functions of intelligent cockpits are becoming increasingly complex, while traditional manual testing suffers from low efficiency, poor consistency, and difficulty in traceability. This paper investigates automated production line testing technologies for head units, constructs a technical architecture covering cockpit automation testing and SOA interface services, proposes a "two-end, one-library" framework, and explores three key technologies: multimodal interaction modeling, interface semantic parsing, and cross-domain service orchestration. Verification through the "end-cloud integrated electrical intelligent verification platform", the special-item testing time for a single-vehicle head unit is reduced from 30 minutes of manual operation to 10 minutes, with significantly improved consistency and traceability.

The driving environment of heavy commercial cargo vehicles is complex and the working conditions are variable. Drivers are prone to fatigue and distraction during long-term operation, which poses risks. This paper proposes an intelligent cockpit driving state recognition and intervention mechanism based on multi-source perception fusion. By integrating multi-source perception data such as vehicle cameras, physiological sensors, and vehicle buses, a multi-source fusion recognition model based on CNN-LSTM is used to monitor the driver's attention, fatigue level, and dangerous driving behaviors in real time. The overall accuracy rate reaches 92.3%. On this basis, a dynamic adjustment mechanism for the intervention threshold at different levels is designed. According to the risk level (divided into four grades: 0-1.0), differentiated intervention measures such as audio-visual alerts, seat vibration, and active braking are taken. The false alarm rate is reduced to 5%. The research shows that multi-source data fusion technology can overcome the limitations of a single sensor, improve the recognition accuracy and robustness, provide technical support for the intelligent cockpit of heavy commercial cargo vehicles, and is of great significance for reducing the accident rate and ensuring transportation safety.

With the rapid development of intelligent connected vehicle technology, vehicles are no longer just means of transport; they have become mobile intelligent terminals that integrate environmental perception, computing and decision-making, and data interaction. Based on big data analysis, this paper studies the methods for extracting driving behavior characteristics, creates a multi-dimensional driving behavior evaluation system, and uses the LSTM-Attention deep learning model to predict driving risks. The model's risk warning effectiveness is verified using real road test data. Driving behavior analysis driven by big data can effectively identify abnormal driving patterns, significantly improve the predictive ability of active safety systems, and provide a theoretical basis and technical support for active safety management of intelligent connected vehicles.