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Cause-Effect Chain-Based Diagnosis of Automotive On-Board Energy Systems
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2025
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Gesellschaft für Informatik, Bonn
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Context: Vehicle diagnostics are critical tools for identifying, locating, and resolving automobile faults. However, the increasing connectivity within vehicles poses challenges to seamless diagnostic processes. Aim: This paper aims to improve the rectification of faults following a diagnostic trouble code entry in a vehicle’s electrical power system. Method: The approach involves designing a graph based on the cause-effect chain from the ‘flexible Energy and Power Management’ (fEPM) detailed model, defining areas for each signal to identify potential causes for diagnostic trouble code entries using simulated signal traces. This method and graph reduction techniques were evaluated through an interview study with engineers who provided feedback on its practical applicability and efficacy in real-world scenarios. Results: The application of this method results in a clear fault image, graphically representing the origin of the diagnostic trouble code entry. This reduced graph can be interpreted comprehensively for each component and each diagnostic trouble code entry, possibly automating the interpretation process. The interview study confirmed the applicability and efficiency of the approach. Conclusion: This research presents a method for identifying the root causes of faults in automotive