Nonlinear MPC for supervisory control of hybrid electric vehicles

We propose a hierarchical Model Predictive Control (MPC) strategy for energy management in plugin hybrid electric vehicles. An inner feedback loop addresses the problem of optimally tracking a given reference trajectory for the battery state of charge over a short future horizon using knowledge of the predicted driving cycle. The associated receding horizon optimization problem is solved using a projected Newton method. The controller is compared with existing approaches based on Pontryagin's Minimum Principle and the effects of imprecise knowledge of the future driving cycle are discussed. An outer feedback loop generates the state of charge reference trajectory by solving approximately the optimal control problem for the entire driving cycle. By considering averages of the driver demand over longer time intervals the required number of prediction steps is reduced such that the outer loop problem can also be efficiently solved using the proposed Newton method. Advantages over approaches that assume a linearly decreasing state of charge reference trajectory are discussed