Use of Convex Optimization to Increase Energy Efficiency of Plug-in Hybrid Vehicles

We investigate the method of convex optimization to improve the efficiency and resource utilization of Plug-in Hybrid Electric Vehicles (HEVs). Our optimization uses information about a known or estimated vehicle route to predict energy demands and maximally use grid-sourced electricity and minimally use petroleum resources for a given route. Our convex optimization method uses a simplified car model to find the optimal strategy over the whole route, which allows for re-optimization on the fly as updated route information becomes available. The approach allows a vehicle to use only electric-drive on a designated portion of the route, for example to traverse an urban area with electric drive requirements. Validation between the simplified model and a more complete vehicle technology model simulation developed at Argonne National Laboratory was accomplished by “driving” the complete car simulation with the simplified control model. By driving on routes with the same total energy demand but different demand profiles the preliminary results show efficiency gains of 5-15% on mixed urban/suburban routes compared to a Charge Depleting Charge Sustaining (CDCS) battery controller.