To address the adaptability bottlenecks of traditional automotive-grade chip mission profile frameworks in the context of high-voltage electrification and the large-scale deployment of wide-bandgap devices in new energy vehicles, this paper conducts a systematic review of the theoretical framework, standardization paradigms, load parameter discrepancies, reliability assessment models, and engineering application boundaries of automotive chip mission profiles, based on AEC-Q100 Rev-J. The fundamental differences in chip loading characteristics between conventional fuel vehicles and new energy vehicles are comparatively analyzed across multiple dimensions. The study reveals that the thermal load duration of chips in new energy vehicles is approximately 10 times longer than that in fuel vehicles, with operating voltages reaching 800 V and power device switching frequencies increasing by approximately 20 times. Traditional automotive chip reliability assessments are inadequate for new energy vehicles, failing to address multi-physics coupling, long-term aging, and the unique failure mechanisms of wide-bandgap devices. Currently, the reliability certification of automotive-grade chips is shifting from "general compliance demonstration" to "application suitability demonstration". In the future, constructing refined, dynamic, and multi-scale mission profiles, along with improving the suitability assessment system for wide-bandgap devices, will become critical directions requiring breakthrough in this field.

To clarify the current state of the automotive AI chip industry, address technological pain points, and identify the future development trends of the industry, while supporting the domestic automotive AI chip industry's independent and high-quality development, this focuses on the core application scenarios of AI chips in intelligent connected vehicles, such as smart driving perception, decision-making, control, and smart cockpit interaction. It systematically reviews the core requirements of intelligent connected vehicles for AI chips, including high computing power, low power consumption, high security, and high reliability. At the same time, it analyzes the technology layouts and product iterations of mainstream domestic and international automotive AI chip companies like Qualcomm, NVIDIA, Tesla, Horizon, and SiEngine Technology, and, based on the current industry situation, summarizes the main technical bottlenecks and solutions for automotive AI chips in areas like memory bandwidth and packaging processes. On this basis, it assesses the future development trends of automotive AI chips in terms of domain fusion, heterogeneous architecture integration, computing power growth, the implementation of computing-storage integration technology, and advanced packaging. The goal is to provide a reference and inspiration for the technological research and development, product iteration, industrial layout and relevant research of vehicle-mounted AI chips.

To address the rapid growth of data exchange volume in onboard computing chips driven by the rapid development of intelligent connected vehicles, while satisfying the high-level security protection requirements for in-vehicle data as the most valuable core asset of automotive manufacturers, the paper discusses the impact of current information security standards and algorithms on in-vehicle information security chips and analyzes the replacement path and development trend of national cryptographic algorithms in the field of in-vehicle chips. The results show that as relevant standards for in-vehicle chips are successively implemented, national cryptography level certification will become an industry norm, and in-vehicle information security chips that pass such certification will occupy the mainstream domestic market.

This study investigates the selection of SoC chips and platform architecture design for intelligent assisted driving systems. A systematic analysis is conducted on key factors affecting chip selection, including hardware interfaces, computing requirements, cost optimization, software ecosystems, functional safety, and supply chain risks. Based on this analysis, a low-cost, engineering-oriented intelligent driving system architecture centered on domestic intelligent driving SoC chips is proposed. The proposed architecture achieves an optimized balance between cost efficiency and resource utilization while ensuring system performance and reliability. It provides valuable references for the design of intelligent driving domain controllers, product development, and the localization substitution of automotive semiconductor solutions.

From the perspective of domestic patent applications, a patent analysis is conducted on the technological evolution of the perception module in intelligent driving, based on 2 branches: sensor hardware technology and multi-sensor fusion technology. The study finds that multi-sensor combinations of cameras, LiDAR, and millimeter-wave radar remain the mainstream configuration. Feature-level and decision-level fusion technologies, due to their lower computational resource demands, have become the preferred solutions for the current large-scale deployment of intelligent driving. In the future, performance improvements in perception systems will advance along 3 directions: self-supervised spatiotemporal alignment, lightweight deployment of end-to-end models, and robustness against extreme weather conditions.

Aiming at the low accuracy of State of Charge (SOC) estimation and poor adaptability of control strategies under dynamic working conditions for power batteries of new energy vehicles, a multi-model fusion SOC estimation framework integrating Kalman Filtering (KF), Particle Filtering (PF) and Long Short-Term Memory (LSTM) neural network is constructed. Dynamic charging and discharging management strategies based on genetic algorithm and particle swarm optimization are designed. A power battery test platform covering multiple temperatures and charge-discharge rates is built, and simulations and physical tests are carried out under standard UDDS and WLTC working conditions. The test results show that the root mean square error of SOC estimation of the proposed method is reduced to 0.013 5, the battery charge-discharge efficiency is increased by 9.3%, and the maximum discharge current is reduced by 23.7%. The proposed method can slow down battery capacity attenuation and reduce thermal runaway risk, which provides theoretical support and engineering reference for the optimization of battery management systems.

To address the issue of quantitative data during the concept phase, this paper proposes a quantitative safety goal assessment method based on the ISO 26262 standard. By establishing a mapping model between fault scenario dynamics and injury characteristics, simulations were conducted to quantitatively determine the Severity (S), Controllability (C), and Fault Tolerant Time Interval (FTTI). The results indicate that the FTTI increases with rising time headway but decreases as driver reaction time extends; moreover, high-speed conditions exhibit higher sensitivity to variations in these parameters. Without requiring physical prototypes, this method efficiently generates quantitative metrics through parameter configuration, thereby supporting the precise definition of safety goals in the concept phase.

To address the challenges posed by the rapid evolution of new energy vehicle (NEV) electrical systems toward higher voltages, higher power density, and greater integration, and to mitigate the critical impacts of in-network ripple on overall vehicle electrical safety, Electromagnetic Compatibility (EMC), and core component reliability, this study investigates a mass-produced NEV. Based on the ISO 21498-2: 2024 international standard and an extended testing protocol, a high-voltage ripple test system covering full operating conditions and multiple states was established. Through dynamic real-vehicle testing, the time-domain and frequency-domain characteristics, distribution patterns, and key influencing factors of the ripple were analyzed in depth. The results indicate that the safety factor level of the high-voltage in-network ripple for this vehicle model is high, verifying the excellent stability and robustness of the vehicle's high-voltage system. Furthermore, the study reveals the coupling mechanisms of operating conditions, battery State of Charge (SOC), power electronics switching actions, and system parasitic parameters on the ripple. Systematic ripple suppression and optimization schemes are proposed from 3 dimensions: control strategy, hardware design, and test verification. The research findings provide important theoretical basis and engineering practice references for the forward design, performance optimization, and standard improvement of NEV high-voltage systems.