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基于分区架构的车载网络中端到端延迟与线束分析

Analysis of E2E Delay and Wiring Harness in In-Vehicle Network with Zonal Architecture.

作者信息

Park Chulsun, Cui Chengyu, Park Sungkwon

机构信息

Department of Electronic and Computer Engineering, Hanyang University, Seoul 04763, Republic of Korea.

出版信息

Sensors (Basel). 2024 May 20;24(10):3248. doi: 10.3390/s24103248.

DOI:10.3390/s24103248
PMID:38794101
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11125951/
Abstract

With recent advances in vehicle technologies, in-vehicle networks (IVNs) and wiring harnesses are becoming increasingly complex. To solve these challenges, the automotive industry has adopted a new zonal-based IVN architecture (ZIA) that connects electronic control units (ECUs) according to their physical locations. In this paper, we evaluate how the number of zones in the ZIA affects the end-to-end (E2E) delay and the characteristics of the wiring harnesses. We evaluate the impact of the number of zones on E2E delay through the OMNeT++ network simulator. In addition, we theoretically predict and analyze the impact of the number of zones on the wiring harnesses. Specifically, we use an asymptotic approach to analyze the total length and weight evolution of the wiring harnesses in ZIAs with 2, 4, 6, 8, and 10 zones by incrementally increasing the number of ECUs. We find that as the number of zones increases, the E2E delay increases, but the total length and weight of the wiring harnesses decreases. These results confirm that the ZIA effectively uses the wiring harnesses and mitigates network complexity within the vehicle.

摘要

随着车辆技术的最新进展，车载网络（IVN）和线束正变得越来越复杂。为应对这些挑战，汽车行业采用了一种新的基于区域的车载网络架构（ZIA），该架构根据电子控制单元（ECU）的物理位置连接它们。在本文中，我们评估了ZIA中的区域数量如何影响端到端（E2E）延迟和线束的特性。我们通过OMNeT++网络模拟器评估区域数量对E2E延迟的影响。此外，我们从理论上预测并分析了区域数量对线束的影响。具体而言，我们采用一种渐近方法，通过逐步增加ECU的数量，分析具有2、4、6、8和10个区域的ZIA中线束的总长度和重量变化。我们发现，随着区域数量的增加，E2E延迟增加，但线束的总长度和重量减少。这些结果证实，ZIA有效地利用了线束，并减轻了车辆内部的网络复杂性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffd5/11125951/2de7f09c5be4/sensors-24-03248-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffd5/11125951/2de7f09c5be4/sensors-24-03248-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffd5/11125951/902a54b0c9b8/sensors-24-03248-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffd5/11125951/bb30157b7215/sensors-24-03248-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffd5/11125951/f3e805a44c69/sensors-24-03248-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffd5/11125951/ecf7f8335506/sensors-24-03248-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffd5/11125951/29222fb659d4/sensors-24-03248-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffd5/11125951/2de7f09c5be4/sensors-24-03248-g013.jpg

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本文引用的文献

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2
In-Vehicle Communication Cyber Security: Challenges and Solutions.车载通信网络信息安全：挑战与应对措施。
Sensors (Basel). 2022 Sep 3;22(17):6679. doi: 10.3390/s22176679.
3
Time-Sensitive Network (TSN) Experiment in Sensor-Based Integrated Environment for Autonomous Driving.基于传感器的自动驾驶综合环境中的时间敏感网络（TSN）实验。
Sensors (Basel). 2019 Mar 5;19(5):1111. doi: 10.3390/s19051111.