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<div class="section abstract"><div class="htmlview paragraph">The automotive industry makes extensive use of virtual models to increase efficiency during the development stage. The complexity of such virtual models increases with the complexity of the process that they describe, and thus new methods for their development are constantly evaluated. Among many others, data-driven techniques and machine learning offer promising results, creating deep neural networks that map complex input-output relations. This work aims at comparing the performance of two different neural network architectures for the estimation of the engine state and emissions (flow fuel, NOx and soot). More specifically, Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) will be evaluated in terms of performance, using different techniques to increase the model generalization. During the learning stage data from different engine cycles are fed to the neural networks. In order to evaluate the generalization of the model, the networks are tested over new, previously unseen, engine cycles. Results show that our models over-perform other state-of-the-art models, the best performance was found for the LSTM model with 2.40%, 2.80% and 18.19% error for flow fuel, NOx and soot sensor respectively.</div></div>

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