Risk Assessment Approach for EM Resilience in Complex Systems Using Bayesian Networks

Abstract

Current trends in the automotive industry are reshaping the architectures and electromagnetic characteristics of road vehicles. Increasing electrification and connectivity are enabling considerable packaging flexibility and leading to radically different electromagnetic environments. At the same time, increasing automation of driving functions will require unprecedented levels of system dependability. However, existing EMC engineering processes were developed in a very different world of low system complexity and incremental technological development. In order to adapt to rising system complexity and the increasingly rapid pace of technological change, it is considered that a more agile risk-based approach is better suited to ensure the electromagnetic resilience of future vehicles and other complex systems. This paper outlines a Bayesian network approach that allows the combination of both technical and nontechnical aspects in assessing the likelihood of issues that could lead to system-level risks. This approach could be used from the earliest stages of product development, where the detailed information required to undertake detailed risk assessment is generally unavailable.

On-Site Automotive Environment Measurements for a Risk-based EMC Approach

“…The investigation concentrates on the effects of the continuously altered temporal and spatial properties of the automotive EME. Additionally, further use of the results via probabilistic tools such as e.g., Bayesian Networks [30] is proposed. Such an application can output the probability of occurrence of various incidents and can further help to minimize the risk of EMI occurring in a real dynamic EME.…”

Study of Random Field Coupling onto a Scooter following the Risk-based EMC Approach

“…Risk-based EMC analysis deviates from the strict standardized techniques [10] as it incorporates other factors as e.g. electromagnetic risks regarding safety aspects [11]. It accounts for unpredicted parameters as well as it deals with real conditions.…”