Linjie Ren

With the growing consumer demand for enhanced driving dynamics in vehicles, optimizing powertrain configurations to balance performance, energy efficiency, and cost has become a critical challenge. Traditional internal combustion engine vehicles (ICEVs) suffer from significant energy consumption and cost penalties when improving acceleration performance. This study systematically evaluates the trade-offs between dynamic performance, energy consumption, and direct manufacturing costs across six powertrain configurations: ICEV, 48 V mild hybrid (48 V), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), range-extended electric vehicle (REV), and battery electric vehicle (BEV). By developing a comprehensive parameterized model, we quantify the impacts of acceleration improvement on vehicle mass, energy consumption, and costs. Key findings reveal that electrified powertrains (PHEV, REV, BEV) exhibit superior cost-effectiveness and energy efficiency. For instance, improving 0–100 km/h acceleration time from 9 to 5 s reduces direct manufacturing costs by only 5.72% for BEV versus 13.38% for ICEV, while PHEV achieves a balanced compromise with 3.40% lower fuel consumption and 10.43% cost increase compared to conventional counterparts. Mechanistic analysis attributes these advantages to higher power density of electric motors and simplified energy transmission in electrified systems. This work provides data-driven insights for consumers and automakers to prioritize powertrain technologies under dynamic performance requirements, highlighting PHEV with driving range of 50 km as the optimal choice for harmonizing driving experience, energy economy, and affordability. The results of this study assist automakers in optimizing the technology pathways of vehicle powertrain, within the consumer demand for dynamic performance. This plays a crucial role in advancing the automotive industry’s overall fuel consumption and energy consumption, thereby contributing to sustainable development.

The increasing quantity and complexity of code in vehicles have imposed a heavy burden on traditional integration testing methods. However, applying new testing methods, such as eliminating redundant test cases, prioritizing based on risk, and optimizing test matching, requires the testing team to possess sufficient information, which entails communication and time costs. In this study, an information management framework is developed for the integration testing phase of automotive software to assess the importance and acquisition difficulty of specific information. The framework mainly includes three core parts: (1) classifying 37 types of test-related information into five hierarchical levels (requirement level, architecture level, function level, component level, and source code level) based on risk theory; (2) designing a scale to evaluate the difficulty of information usage from three dimensions (acquisition, transmission, and evaluation); (3) providing an operational guide for integration testing departments to match information with testing strategies. This framework assists enterprises in making wiser decisions regarding testing methods and provides guidance for future collaboration between original equipment manufacturers and suppliers.

Integration compatibility has emerged as a prominent challenge in the development of digital vehicles. This paper provides a comprehensive review of previous research on automotive integration compatibility, categorizing the relevant challenges into three main categories: technical, organizational, and methodological. Furthermore, the paper distinguishes between challenges encountered during the initial integration phase and those encountered during continuous integration according to the nodes of the start of production. By engaging in discussions with internal experts from an original equipment manufacturer (OEM) and other related enterprises regarding these challenges, the paper identifies the most pressing issues that require novel solutions, which are the current practical pain points of OEMs, thereby providing directions for future research.