Validation of Worst-Case and Statistical Models for an Automotive EMC Expert System

Daryl G. Beetner, Missouri University of Science and Technology
Haixiao Weng
Meilin Wu
Todd H. Hubing, Missouri University of Science and Technology

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Previous papers have presented algorithms for an EMC expert system used to predict potential electromagnetic compatibility problems in a vehicle early in the design process. Here, the accuracy of inductive and capacitive coupling algorithms are verified through representative measurements of crosstalk within an automobile. Worst-case estimates used by the algorithms are compared to measured values and are compared to values estimated using statistical methods. The worst-case algorithms performed well up to 10-20 MHz, but overestimated measured results by several dB in some cases and up to 10-15 dB in others. An approximate statistical variation of the current expert system algorithms also worked well and can help avoid overestimation of problems; however, worst-case estimates better ensure that problems will not be missed, especially in the absence of complete system information.