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SUPPORTING TIMING ANALYSIS OF THE NEXT-GENERATION CONTROLLER AREA NETWORK
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
2023 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Controller Area Network (CAN) is a communication bus designed in the 90s to make simple androbust in-vehicle networks. However, as vehicles are becoming more complex, higher performanceCAN protocols were introduced to manage the expanding volumes of real-time data and more strictsafety requirements. This resulted in the development of next-generation CAN protocols, i.e. CANwith Flexible Data rate (CAN FD) and CAN Extra Long (CAN XL). Response-Time Analysis(RTA) of CAN was developed as a tool to verify if all messages on the CAN bus meet their timingrequirements, i.e. meet their deadlines. Existing RTA is revisited and its applicability on CAN XLmessages is analyzed in the thesis. RTA with priority-based queueing policy, but also RTA whichconsiders different buffer limitations is also revisited. This thesis developed an analytical modelfor Worst-Case Transmission Time (WCTT) calculations for CAN XL which is a prerequisite forits RTA. Finally, MPS-CAN Analyzer and industrial tool suit Rubus-ICE are extended to supportRTA of both CAN FD and CAN XL. The extended tools were used to implement two use casesconsisting of the number of CAN, CAN FD and CAN XL messages. Several experiments wereconducted to compare the performance of different CAN generations in terms of response times ofthe messages. The results indicate that larger data payloads should be sent using next-generationCAN protocol with their bit-rate switching option enabled. Nodes implementing abortable and nonabortablebuffers were also analyzed in the two experiments. Small differences in the response timesof the messages were noticed when buffer limitations are considered.

Place, publisher, year, edition, pages
2023. , p. 50
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:mdh:diva-62855OAI: oai:DiVA.org:mdh-62855DiVA, id: diva2:1762393
External cooperation
Arcticus Systems AB
Subject / course
Computer Science
Supervisors
Examiners
Available from: 2023-06-16 Created: 2023-06-02 Last updated: 2023-06-16Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Language
  • de-DE
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More languages
Output format
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