A Machine Learning Based Tool to Estimate Coolant Engine Temperature Based on Motorcycle Riding Data

Federico Pennino, Davide Sette, David Attisano, Maurizio Gabbrielli

Abstract: In the automotive environment, understanding the thermal behavior of the engine is crucial: a fault in measuring the temperature can reduce reliability and poor fuel efficiency. Internal combustion engine temperature is usually measured using a device called coolant temperature sensor. The sensor may fail, and having an indirect system to get that value can avoid unpleasant conditions. This work addresses a novel solution to estimate the coolant temperature based on machine learning models trained using riding data. This solution can back up the physical sensor and monitor abnormal behavior. We also focused on developing a virtual sensor that would behave well in case of thermal shock, which is essential for ensuring safety, improving the engine work, and making the components last longer. We showed how an approach based on the prediction of the temperature delta is better for dealing with this kind of phenomenon. We obtained an RMSE of 2.79°C training a Long short-term memory (LSTM) on more than 1060 $h$ recorded from Ducati Multistrada V4.