Driving Events Identification and Operational Parameters Correlation based on the Analysis of OBD-II Timeseries

On board diagnostics, OBD-II, allowsmonitoring and understanding of the engine operations through continuous access to engine sensors, detection and diagnosis of errors. In this work, we select a set of OBD-II parameters, Short-Term Fuel Trim, Manifold Absolute Pressure, Absolute Throttle Position, Revolutions Per Minute, Calculated Engine Load, Engine Coolant temperature, Vehicle Speed, Catalytic Converter Temperature, to create a set of driving timeseries. A subset of the values belongs to an existing OBD-II dataset with automatic transmission, while the other subset has been retrieved from scratch, using OBD-II, with manual transmission and during characterized driving conditions (cruising, idle and accelerations). We have designed and implemented a set of rules, to recognise three driving events, i.e., idle, gear change, and accelerations in both manual and automatic transmission. The frequency of these events in combination with the parameter values have led to the identification of driving style differences and the impact in fuel consumption. In addition, we have investigated the correlation among the (OBD-II) driving operational parameters during the three driving modes (idle, cruising and acceleration) using the catch22 timeseries analysis framework. The implemented mechanisms are extensible, in terms of considered vehicles, for constant parameter monitoring and cloud-based storing, paving the way for transparent engine status, service maintenance history and other added value services.