Cause-Effect Chain-Based Diagnosis of Automotive Onboard Energy Systems

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. The method, coupled with graph reduction techniques, was 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 for each diagnostic trouble code entry, with the possibility of automating the interpretation process. The interview study confirmed the applicability and efficiency of the approach. Conclusion: This research introduces a method to identify the root causes of faults in automotive electrical power systems, thereby improving the efficiency and accuracy of vehicle diagnostics.