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Combined time and fuel optimal driving of trucks based on a hybrid model

This paper presents a control strategy for driving trucks on economically optimal speed trajectories over a-priori known road slope profiles. By considering the trip income and costs, the approach leads to an ecological and economic advantage by reducing the fuel consumption and maximizing the profit of transportation at the same time. The optimization task for a truck is formulated as an open-loop, finite-horizon optimal control problem, which is repetitively solved as part of a model predictive control framework. The truck dynamics is represented as a hybrid nonlinear model and a multi-point boundary-value problem (MPBVP) is obtained using Karush-Kuhn-Tucker conditions, the Pontryagin minimum principle for input constraints, interior boundary conditions for state constraints and interior point constraints for gear shifts. The MPBVP is solved numerically by a multiple shooting algorithm. Results are validated in simulations and compared to an ordinary speed controller.