Fast dual-loop nonlinear receding horizon control for energy management in hybrid electric vehicles - Buerger, J; East, S; Cannon, M (IEEE, 2018)
This paper proposes a receding horizon optimization strategy for the problem of energy management in plug-in hybrid electric vehicles. The approach employs a dual-loop model predictive control strategy. An inner feedback loop addresses the problem of optimally tracking a given reference trajectory for the battery state of energy over a short future horizon using knowledge of the predicted driving cycle. An outer feedback loop generates the battery state of energy reference trajectory by solving approximately the optimal energy management problem for the entire driving cycle. The receding horizon optimization problems associated with both inner and outer loops are solved using a specialized 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. This paper contains a detailed simulation study: first, this assesses the optimality of the associated uncertainty-free approach and its computational load. Second, the effects of imprecise knowledge of the future driving cycle are illustrated.