Leveraging On-Board Diagnostics and Model-Based Design Methods for Root-Causing Body Control Systems/Software Issues

Paper #:
  • 2012-01-0931

Published:
  • 2012-04-16
DOI:
  • 10.4271/2012-01-0931
Citation:
Yang, J., Bauman, J., and Beydoun, A., "Leveraging On-Board Diagnostics and Model-Based Design Methods for Root-Causing Body Control Systems/Software Issues," SAE Technical Paper 2012-01-0931, 2012, doi:10.4271/2012-01-0931.
Pages:
8
Abstract:
Many automotive ECU system issues do not manifest themselves until later in the vehicle product development cycle, despite the extensive testing and stringent validations that the ECU may have gone through. When such a system-level issue is identified, engineers will traditionally rely on the available information collected from logged DTCs and memory dumps to root-cause the issue. They will then develop a solution that will either eliminate the defects in ECU or develop a robust design to mitigate the impact. However, engineers are faced with technical difficulties which include: (a) physical addresses for many RAM variables critical to find the root-cause are subject to change with various releases of software, (b) some variables “come and go” so it is challenging to find out how and when the undesired events happen, and (c) many variables that are needed to identify the root-cause are missing.In this work, we describe a method for identifying root-causes for systems and/or software issues in body control applications by leveraging the memory read diagnostic function. To combat the technical difficulties described above, we have developed a model-based design oriented practice in which critical variables are inserted and linked to a group of memory addresses independent of software releases so that the diagnostic memory read approach can always be utilized. In this method, we will need to define a set of counters and flag semaphores that will be incorporated into the software either during the model-based development phase or afterwards when using the auxiliary variables to diagnose software failures becomes needed. A suite of customized scripting tools can then be utilized to attach the semaphore variables, to collect the values of them and compare them with nominal and/or typical values to help identify the root-causes. Although implementing the signal mapping and auxiliary variables in the MATALB models will incur additional engineering efforts, we do observe the efforts will eventually pay off due to the advantages over the traditional methods not only in identifying and resolving issues but also in reducing the warranty costs for both OEMs and suppliers.
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