A Constrained Eco-Routing Strategy for Hybrid Electric Vehicles Based on Semi-Analytical Energy Management

The accuracy of the modern navigation and traffic information systems has increased noticeably. This offers the opportunity of improving the standard powertrain energy management of hybrid electric vehicles (HEVs) by making use of predictive information about the upcoming route. Eco-routing for HEVs aims to optimize fuel consumption by deciding the best route and when to use or recover electrical power based on topological and traffic information. In this work, a simple predictive speed model is used to derive a fast semi-analytical solution of the powertrain energy management. The predicted fuel consumption on each road segment is a function of the desired trade-off between fuel economy and battery use. This modeling complexity is addressed by introducing a novel road network model which takes into account the feasible battery charge variation at road segment level. Also, the routing of HEVs adds several intrinsic constraints and complexity to the classical shortest path problem (SPP). A relaxation of the constrained SPP is proposed to reduce computation time and thus increase user acceptance.

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