The knowledge of thermal behavior of combustion engines is extremely important e.g. to predict engine warm up or to calculate engine friction and finally to optimize fuel consumption. Typically, thermal engine behavior is modeled using look-up tables or semi-physical models to calculate the temperatures of structure, coolant and oil. Using look-up tables can result in inaccurate results due to interpolation and extrapolation; semi-physical modeling leads to high computation time.
This work introduces a new kind of model to calculate thermal behavior of combustion engines using an artificial neural network (ANN) which is highly accurate and extremely fast. The neural network is a multi-layered feed-forward network; it is trained by data generated with a validated semi-physical model. Output data of the ANN-based model are calculated with nonlinear transformation of input data and weighting of these transformations.
The relevant input data were defined and generated by performing a design of experiment (DoE) based on a semi-physical model. The training of the artificial neural network is described and the quality of the training is shown based on several statistical parameters.
The calculated and measured temperatures are compared for several engine operating points. Furthermore, the computation time is compared with the physical model.
Finally, the influence of variation of input data like engine torque, coolant volume flow or ambient temperature is investigated using the new model.