Comparative Evaluation of Various Generations of Controller Area Network Based on Timing AnalysisShow others and affiliations
2023 (English)In: IEEE Int. Conf. Emerging Technol. Factory Autom., ETFA, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]
This paper performs a comparative evaluation of various generations of Controller Area Network (CAN), including the classical CAN, CAN Flexible Data-Rate (FD), and CAN Extra Long (XL). We utilize response-time analysis for the evaluation. In this regard, we identify that the state of the art lacks the response-time analysis for CAN XL. Hence, we discuss the worst-case transmission times calculations for CAN XL frames and incorporate them to the existing analysis for CAN to support response-time analysis of CAN XL frames. Using the extended analysis, we perform a comparative evaluation of the three generations of CAN by analyzing an automotive industrial use case. In crux, we show that using CAN FD is more advantageous than the classical CAN and CAN XL when using frames with payloads of up to 8 bytes, despite the fact that CAN XL supports higher bit rates. For frames with 12-64 bytes payloads, CAN FD performs better than CAN XL when running at the same bit rate, but CAN XL performs better when running at a higher bit rate. Additionally, we discovered that CAN XL performs better than the classical CAN and CAN FD when the frame payload is over 64 bytes, even if it runs at the same or higher bit rates than CAN FD.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023.
Keywords [en]
automotive, CAN FD, CAN XL, Controller Area Network, Control system synthesis, Controllers, Finite difference method, Process control, Automotives, Classical controllers, Comparative evaluations, Controller area network flexible data-rate, Controller area network XL, Controller-area network, Data-rate, Response-time analysis, Timing circuits
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-64693DOI: 10.1109/ETFA54631.2023.10275549Scopus ID: 2-s2.0-85175488822ISBN: 9798350339918 (print)OAI: oai:DiVA.org:mdh-64693DiVA, id: diva2:1810950
Conference
IEEE International Conference on Emerging Technologies and Factory Automation, ETFA
2023-11-092023-11-092024-10-02Bibliographically approved
In thesis