  This paper presents a computationally efficient algorithm for eco-driving
over long prediction horizons. The eco-driving problem is formulated as a
bi-level program, where the bottom level is solved offline, pre-optimizing gear
as a function of longitudinal velocity and acceleration. The top level is
solved online, optimizing a nonlinear dynamic program with travel time, kinetic
energy and acceleration as state variables. To further reduce computational
effort, the travel time is adjoined to the objective by applying necessary
Pontryagin Maximum Principle conditions, and the nonlinear program is solved
using real-time iteration sequential quadratic programming scheme in a model
predictive control framework. Compared to standard cruise control, the energy
savings of using the proposed algorithm is up to 15.71%.
