Assessing Environmental Influences on Flash Storage for Vehicle Computing: A Quantitative and Analytical Investigation
As automotive technology advances, ensuring efficient and reliable vehicle storage systems becomes increasingly important to vehicle edges. Environmental factors, like extreme cold or intense heat, can greatly affect how well these critical components function. In this paper, we study the effects of different temperatures on flash-based vehicle storage systems, especially how these conditions impact data storage workloads, machine learning workloads, and vehicle edge computing by analyzing the read and write performance of car flash memory. Our approach combines environmental simulations, performance testing, and data analysis to examine how temperature changes affect the performance and reliability of vehicle storage. By testing conditions from standard room temperatures to extreme heat, this study explores how such environments influence the speed, dependability, and overall functionality of flash memory in automotive systems. The results show detailed relationships between temperature changes and the speed (throughput) and delay (latency) in flash storage, identifying areas where these systems may be vulnerable or where improvements could be made. Understanding these dynamics is essential for improving the durability and flexibility of automotive storage systems and vehicle edges in various environmental conditions. We have refined our conclusions to note that while our findings provide insights into temperature-related performance shifts, they represent one piece of a broader set of design considerations for engineers and manufacturers. Rather than offering definitive guidance for policymakers, our findings primarily help illustrate potential thermal vulnerabilities, informing ongoing work toward more robust and reliable vehicle storage systems. As the automotive industry continues to innovate, this study offers an initial foundation for future developments in vehicle storage technology.
Authors
- Qing Yang (ORCID: https://orcid.org/0000-0002-0683-5848)
- Feng‐Yu Wang (ORCID: https://orcid.org/0000-0003-0950-1672)
- Song Fu (ORCID: https://orcid.org/0000-0002-7705-0829)
- Ying He (ORCID: https://orcid.org/0009-0000-6456-4997)
- Donger Chen (ORCID: https://orcid.org/0009-0007-6238-7529)
- Isabella Xu
- Ted Tsao
Institutions
- University of North Texas (US)
Publication Details
- Journal
- Algorithms
- Published
- 2025-05-05
- DOI
- https://doi.org/10.3390/a18050269
- Primary Topic
- Vehicle emissions and performance
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation