An approach to Prognosis-Decision-Making for route calculation of an electric vehicle considering stochastic traffic information

Heraldo Rozas (1) , Diego Munoz-Carpintero (1) , Aramis Perez (1) , Kamal Medjaher (2) , Marcos Orchard (1)

1 Université du Chili
2 LGP - Laboratoire Génie de Production

We present a Prognosis-Decision-Making (PDM) methodology to calculate the best route for an Electric Vehicle (EV) in a street network when incorporating stochastic traffic information. To achieve this objective, we formulate an optimization problem that aims at minimizing the expectation of an objective function that incorporates information about the time and energy spent to complete the route. The proposed method uses standard path optimization algorithms to generate a set of initial candidates for the solution of this routing problem. We evaluate all possible paths by incorporating information about the traffic, elevation and distance profiles, as well as the battery State-of-Charge (SOC), in a prognostic algorithm that computes the SOC at the end of the route. In this regard, the solution of the optimization problem provides a balance between time an energy consumption in the EV. The method is verified in simulation using an artificial street network.

Mots clés: Fault prognostics; Remaining useful life; Decision-making

Domaines: Autre

Heraldo Rozas, Diego Munoz-Carpintero, Aramis Perez, Kamal Medjaher, Marcos Orchard. An approach to Prognosis-Decision-Making for route calculation of an electric vehicle considering stochastic traffic information. Fourth european conference of the prognostics and health management society 2018, Jul 2018, Utrecht, Netherlands. pp.0. ⟨hal-02111807⟩