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Electric motors and Internal Combustion Engine test benches allow for testing, under various conditions, the behavior of the Electric Vehicles and they are essential in the automotive field development. In this paper, we introduce the state-of-art of the control algorithms and their validation over different test runs, on the standard driving cycles. Ad-hoc simulations and test benches designed for the issue offer a significant opportunity to reduce costs. The modelling of automotive systems is a non-trivial problem due to the non-linearities. The paper presents a linearized solution and an adaptive control scheme to improve model performances starting from real data, and also discusses the behavior of the proposed system and the control law reporting emissions of the considered virtual vehicle when compared with the target emission regulations.

The adoption of Engine-in-the-loop technology shows real behaviour. This study presents a test runs simulation platform with real engine data. In addition, a test bench model is a demand approach that offers a significant potential to provide an excellent reproducibility of test runs. The platform includes the data integration to upgrade tests run and a comparison with previous results using the advancing control techniques designed. The dynamometer system presents significantly non-linearity. The adaptive control approach, integrated into the Model Predictive Control on the vehicle, allows increasing the tests run performance. The results show how the real data can improve performance and the validation of the system integrating the updated driving cycle and maintaining EiL approach. The conclusion showed the significant benefits regarding the control methods used.

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