Double-layer control architecture for motion and torque optimisation of autonomous electric vehicles - Angelo Coppola; Gianmaria De Tommasi; Carlo Motta; Alberto Petrillo; Stefania Santini (Transportation Research Interdisciplinary Perspectives, 2023)
Optimising energy autonomy for autonomous electric vehicles is a significant challenge in sustainable and environmentally friendly mobility. To this end, we propose a novel double-layer control architecture designed to drive the longitudinal motion of electric vehicles equipped with a regenerative braking system. The first layer resembles an Adaptive Cruise Control, while the second one is enabled during the braking phase and is devoted to the blending between the motor torque and the hydraulic one. This layer is synthesised as a Nonlinear Model Predictive Control strategy so as to maximise the efficiency of the regenerative system. The control architecture, by combining the two control strategies, allows to reduce the overall energy consumption for electric vehicles, while simultaneously ensuring a safer, more sustainable, and comfortable driving experience. Additionally, this combination is capable of compensating for any efficiency issues arising from external factors, such as different traffic conditions encountered by the vehicle. To evaluate the effectiveness of the proposed control architecture, extensive simulation analysis was carried out using the MiTraS virtual testing platform, considering two realistic driving scenarios. Results confirm the robustness and safety of the proposed control architecture in ensuring the tracking of the desired trajectory while optimising the energy consumption.