A PCGL-based data loading algorithm for electrical vehicle time-triggered CAN

Q4 Business, Management and Accounting

International Journal of Mobile Network Design and Innovation Pub Date : 2018-03-05 DOI:10.1504/IJMNDI.2018.10011188

Liu Yingji, Shu-ju Wang, Chenglong Ding, Yao Yu, Xiao Hongwen, Jie Xia

引用次数: 0

Abstract

In this paper, a period correlative group loading (PCGL)-based algorithm is proposed specifically for the real-time communication of random messages in electrical vehicle TTCAN networks. By compressing the bandwidth radio of the synchronous phase, the real-time response of event-triggered messages is accelerated. The PCGL-based scheduling approach will be detailed and described. The proposed method is tested on the scheduling for SAE electrical vehicle message standard and the results show that, on the premise of guaranteeing the real-time efficiency of time-triggered messages, the real-time efficiency of event-triggered messages is significantly improved.

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基于pcgl的电动汽车时间触发CAN数据加载算法

针对电动汽车TTCAN网络中随机报文的实时通信问题，提出了一种基于周期相关群加载(PCGL)的算法。通过压缩同步相位的带宽无线电，加速了事件触发消息的实时响应。将详细描述基于pcgl的调度方法。在SAE电动汽车消息标准调度上进行了测试，结果表明，在保证时间触发消息实时性的前提下，事件触发消息的实时性得到了显著提高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Mobile Network Design and Innovation Business, Management and Accounting-Management Information Systems

CiteScore

0.30

自引率

0.00%

发文量

期刊介绍： The IJMNDI addresses the state-of-the-art in computerisation for the deployment and operation of current and future wireless networks. Following the trend in many other engineering disciplines, intelligent and automatic computer software has become the critical factor for obtaining high performance network solutions that meet the objectives of both the network subscriber and operator. Characteristically, high performance and innovative techniques are required to address computationally intensive radio engineering planning problems while providing optimised solutions and knowledge which will enhance the deployment and operation of expensive wireless resources.