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Graphical models are statistical models supported on a graph structure: nodes represent random variables, and missing edges represent probabilistic relationship of conditional independence. This makes them suited to model the behavior of complex systems that are difficult to model through mathematical equations. In this work, this possibility is exploited in a context of diagnostics and fault detection. Specifically, the fault detection problem is reduced to the evaluation of a conditional probability. The relevant conditional distribution is derived from the analysis of a suitable graphical model taking advantage of the so‐called Markov properties.As a case study, we consider diagnostics of a hybrid bus, characterized by the combined use of a diesel engine and an electrical engine. The aim of the study is to verify the correct operation of the electrical system, in particular the status of the battery. Copyright © 2014 John Wiley & Sons, Ltd.

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Nowadays considerable resources have been invested on low emission passenger vehicle both for private and public transportation. A feasible solution for urban buses is a full electrical traction system fed by supercapacitor that can be recharged at each bus stop while people are getting on and off. Such vehicle covers a short distance between consecutive stops, usually less than half a kilometer. An energy storage system able to provide high power peaks and small amount of energy is required. For these reasons, supercapacitors, which are capable of fast charging during bus stops, appear the most appropriate storage devices [1]. In order to consider the worst operating condition for the bus (like traffic jam of higher distance to be covered), a conventional battery is also installed, getting an hybrid energy storage system. An energy management function, able to manage the energy storage system, has been developed and validated by means of a numerical simulation model.