Analysis of Real-Driving Data Variability for Connected Vehicle Diagnostics. Issue 24 (2022) - Barbier, Alvin; Salavert, José Miguel; Palau, Carlos E.; Guardiola, Carlos
Abstract: Connected vehicle paradigm allows the systematic recording of data, which may be made available for both on-board and cloud diagnostics functions. However, real-driving conditions may be highly dynamic, making the application of diagnostic methods cumbersome. This article analyzes the variability of real-world data coming from a mild hybrid vehicle at various levels (i.e., vehicle, powertrain and engine cycle). The results show that although non-steady, real-driving conditions can exhibit situations that could be leveraged to characterize the nominal operation of the vehicle over time and therefore ease the detection of faulty operation.