Development of an Adaptive Model Predictive Control for platooning safety in Battery Electric Vehicles

Abstract

Nowadays, the continuous improvement of transportation system technologies offers several opportunities that are being exploited for the improvement of safety and comfort in passenger vehicles. For example, Adaptive Cruise Control (ACC) could provide smooth traffic flow and collision prevention. In addition, Vehicle-to-Vehicle (V2V) communication can be leveraged in the predecessor vehicle tracking model to achieve further safety and comfort improvements by ensuring quick response to critical events. In this work, firstly an Adaptive Model Predictive Control is developed to manage the Cooperative ACC scenario of two vehicles; in a second phase, safety analysis is performed during a cut-in maneuver, extending the number of vehicles to four. The efficiency of the proposed methodology has been verified in different driving scenarios, such as different cruising speeds, heavy acceleration and aggressive deceleration. In addition, the controller was validated by considering various speed profiles of the leading vehicle, including a real driving cycle obtained using a random driving cycle generator software. The obtained results show that the proposed control strategy is able to respond quickly to unexpected maneuvers and avoid collisions between fleet vehicles, while still ensuring a minimum safety distance in the considered driving scenarios.

Objectives

Development of an MPC controller to implement Cooperative Adaptive Cruise Control on an electric vehicle in realistic driving situations, ensuring the safety of passengers.

Research methodology

In a first phase of the activity, a bibliographic analysis was carried out on the Model Predictive Control and ADAS technologies to study the state of the art. Next, the model of a full electric vehicle was developed with MATLAB / Simulink. Finally, the design of the MPC controller was defined with MATLAB / Simulink to drive an autonomous vehicle equipped with Cooperative Adaptive Cruise Control and tests on the application of the model were made to a fleet of 4 vehicles also considering a cut-in maneuver.

Conclusions

The results obtained showed a good ability of the controller in implementing the CACC on fleet vehicles and in ensuring safety against sudden maneuvers such as the cut-in maneuver.

Future developments

Integration of lateral dynamics of the vehicle to simulate more complete and complex driving situations.