The Virtual Boosted DISI Engine Model Development Based on Artificial Neural Networks 2022-01-0383

To efficiently reduce the required experimental data and improve the prediction accuracy, a virtual engine model has been built by integrating an artificial neural network (ANN) system consisting of multiple subnets with the genetic algorithm (GA). The GA algorithm could reduce the risk of local minima and lead to a more efficient training process. The engine model has been adopted to predict the combustion phases (including CA10, CA50 and CA90), exhaust gas temperature, brake specific fuel consumption rate (b_e) and engine emissions which are un-burnt hydrocarbon (UBHC), NOx and CO. The results are then compared with the experimental data from around 5000 operating points of a boosted DISI engine running at universal performance map and conditions with various valve timing configurations. The mean absolute errors of combustion phases are all below 1.0 crank angle degree. The averaged errors of the exhaust gas temperature and b_e are 10.1 K and 1.1%, respectively. The averaged relative errors of UBHC, NOx and CO are close to 2.67%, 2.49% and 3.84%, respectively. Furthermore, the predicted results and experimental data show satisfying similar trends in terms of the CA 50 and combustion duration under different crank speed and brake specific fuel consumption rate under different VVT configurations. With a 2.2 GHz single-core processor, the turnover time for one single engine cycle calculation using the virtual engine model is less than one-tenth of the real wall time. This illustrates the potential of the proposed model for hardware in the loop (HiL) system and the virtual real drive emission (RDE) development.