Flexible predictive hybrid powertrain management with V2X information

Abstract—Using knowledge of the future route and its topology is known to offer substantial fuel savings, and this is even more true for hybrid electric vehicles, as the battery use can be planned in advance, for instance to take into account coming slopes. However, traffic or other environmental conditions can force to deviate from the initial planning making it no longer optimal. In this paper, we propose a flexible double layer approach for energy management of hybrid vehicles able to cope with traffic changes. First, before departure, an expected optimal speed and powertrain state reference is computed on a cloud and sent to an on-board controller. Simple, route-specific engine on/off rules are extracted by the controller and used for an on-board fast convex optimization, which can be conducted frequently along the drive, adapting the references to take into account changes of traffic conditions over longer sections of the route as communicated by V2X. Abrupt disturbances are handled by a lower level Model Predictive Control (MPC). If the condition changes are very substantial, so that the empirical on/off rule seems questionable, the cloud can be asked to perform a full optimization again.