In this work, a Prognosis-Decision-Making (PDM) methodology to calculate the best route of an Electric Vehicle (EV) in order to reach a destination in a street network, incorporating stochastic traffic information, is presented. The problem is formulated as an optimization problem that seeks to minimize the expected value of an objective function, which includes the time and energy spent to complete the route. The proposed methodology first selects a number of the best routes using standard routing optimization algorithms. Then, information about the traffic state, state of charge (SOC) of the lithium-ion (Li-ion) battery, as well as the elevation and distance profiles of the routes are used as inputs for a prognosis algorithm, which allows to evaluate the different possible paths. Finally the route with minimal expected travel cost is selected according to the optimization criterion. Simulation results show that the inclusion of the prognosis algorithm, incorporating traffic, battery and route information, effectively helps finding the best route in terms of the travel cost.
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Prognosis-Decision-Making, Prognosis Health Management, Electric Vehicle Routing
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