Verification of Powertrain Simulation Models Using Machine Learning Methods

Sammanfattning: This thesis is providing an insight into the verification of a quasi-static simulation model based on the estimation of fuel consumption using machine learning methods. Traditional verification using real test data is not always available. Therefore, a methodology consisting of verification analysis based on estimation methods was developed together with an improving process of a quasi-static simulation model. The modelling of the simulation model mainly consists of designing and implementing a gear selection strategy together with the gearbox itself for a dual clutch transmission dedicated to hybrid application. The purpose of the simulation model is to replicate the fuel consumption behaviour of vehicle data provided from performed tests. To verify the simulation results, a so-called ranking model is developed. The ranking model estimates a fuel consumption reference for each time step of the WLTC homologation drive cycle using multiple linear regression. The results of the simulation model are verified, and a scoring system is used to indicate the performance of the simulation model, based on the correlation between estimated- and simulated data of the fuel consumption. The results show that multiple linear regression can be an appropriate approach to use as verification of simulation models. The normalised cross-correlation power is also examined and turns out to be a useful measure for correlation be-tween signals including a lag. The developed ranking model is a fast first step of evaluating a new vehicle configuration concept.