Abstract
The purpose of this work is creating of a 0D/1D system simulation model that can predict thermal condition of the electric truck propulsion system with high accuracy alongside with faster-than-real-time calculation speed. The heat release distributions at different operation points are calculated and estimated by physical assumptions. Heat transfers between different components, fluids and ambient of the investigated system are identified and validated from experimental and available data from literature. The electric drive unit combines electric motor, transmission, inverter, coolant and lubrication system. The above-mentioned method achieves a good prediction accuracy for temperatures of the driving system.

1 Motivation
Increase of efficiency of traction propulsion system in the electric vehicles is one of the main items of research and development due to its significant effect to the vehicles range. Thermal simulation of the vehicle’s propulsion can help to find optimal cooling and operational conditions, adjust the system parameters accordingly. Real time capable thermal simulation models can help with electric vehicle traction system monitoring. Contribution to coolant temperature increase by the traction inverter can be calculated by application of neuronal network, trained from real vehicle mission profile data. In this work, parametric modeling approach has been applied, to be as close as possible to physics. On the other hand, simplification of the model required approximation of system nonlinearities by characteristic functions to achieve accuracy targets and keep the acceptable simulation speed.

2 Methods
The main components of the model : equations for the power distribution calculations, solid parts for the inverter and motor modeling, flow channels for coolant and oil flow modeling and monitors. The model calibration consists of a few steps. Firstly, test data are analyzed. Secondly, steady calibration are held: Inverter, heat exchanger and motor are calibrated. In the last step transient calibration and model validation are executed.

4 Results and discussion
The model is calibrated for steady operation points (train / validation distribution is 70/30). Calibration targets: minimum errors of solid, oil and coolant temperatures prediction. After good steady results achieving, transient calibration and final fitting are held.

5 Conclusions
In this paper, the heat contribution of the electric motor, transmission, inverter to the coolant and lubrication system has been modeled applying a semi-physical method. Model has been identified from transient and steady state experimental data. Validation by transient cycle shows good model accuracy as well as acceptable computation speed. Model contributes to design and optimization of thermal management system of the vehicle.