Oil Quality Sensor (OQS) Sensor performance testing and modelling

Abstract

Engine oil condition in Volvo Group heavy-duty field trucks is currently assessed through fixed interval oil changes supplemented by laboratory sample analysis, a process that takes up to four weeks and cannot detect real time contamination events such as water contamination or fuel dilution before damage occurs. This thesis investigates whether the TE Connectivity FPS2800 Oil Property Sensor measuring viscosity, density, dielectric constant, and electrical resistivity simultaneously is capable of replacing this process with real-time oil monitoring for Volvo VDS-5 engine oil. The work was carried out in four phases starting with the sensor’s temperature dependent properties which were validated on a oil test rig resulting in three of the four proposed models (viscosity, density, dielectric constant) held in line for VDS-5 oil, while resistivity required a corrected Arrhenius cubic model to replace the originally proposed linear one. Fuel dilution models were calibrated at 3%, 6% and 9% and water contamination at 1% and 2% concentrations. further on to second phase where the validated models were applied to three months of CAN-bus data from a Volvo field test vehicle by building a pipeline on Volvo’s Data Science Lab. Moving to third phase where the resulting oil condition estimates were validated against Volvo’s laboratory reference data which showed agreement for soot, water contamination and oxidation. Finally the fourth phase where the pipeline was deployed end to end with a Power BI dashboard for research and development team. The results show that the FPS2800, calibrated specifically for VDS-5 oil, can deliver oil condition estimates comparable in accuracy to laboratory analysis for the most critical parameters, furthermore, estimated prototype cost was estimated and business case was made for the sensor. Remaining gaps like oxidation and soot rig calibration are identified as future work ahead of production deployment.