Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication

Document Type : Original Article

Author

Automobile & Rail Transportation School, Tianjin Sino-German University of Applied Sciences, Tianjin, China

Abstract

With the escalating integration of Electronic Control Units (ECUs) in contemporary vehicles, the intricacy of vehicle networks is incessantly advancing. Diagnostic communication, as a pivotal facet within these networks, grapples with protracted development cycles and heightened intricacies. In a bid to augment software reusability and portability, this study meticulously scrutinized pertinent research and proffered an electric vehicle fault diagnosis system predicated on the Controller Area Network (CAN) bus, leveraging the diagnostic communication architecture advocated by the AUTOSAR standard. The integration of AUTOSAR seeks to pioneer an innovative software development paradigm for automotive fault diagnosis systems, thereby remedying extant limitations. The communication and diagnostic module of this study were instantiated using AUTOSAR, thereby obviating the necessity for developers to immerse themselves in hardware intricacies and communication implementations. This allows developers to focalize their efforts on crafting software features for fault diagnosis. Empirical results illustrate that the single-core CPU utilization rate of the proposed method in this article stands at 40.68%, with a fault detection time of 0.0217. The success rate of fault detection is 98.70%, indicating an increase of 12.97% and 8.98% when compared to the CAN bus and structural analysis methods, respectively. Testing indicators are significantly mitigated, yielding more precise fault detection outcomes. The exploration of this avant-garde software development methodology in automotive electronic products markedly amplifies the efficiency of automotive troubleshooting system software, underscoring its potential for academic contribution and application in real-world scenarios.

Graphical Abstract

Framework of Electric Vehicle Fault Diagnosis System Based on Diagnostic Communication

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References

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International Journal of Engineering
Volume 37, Issue 6
TRANSACTIONS C: Aspects
June 2024
Pages 1194-1207

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