Maximum likelihood estimation of Departure and Travel Time of Individual Vehicle using statistics and dynamic programming

Abstract

Electric Vehicles (EVs) and Plug-in Hybrid Vehicles (PHVs) generally equip a battery of high capacity. Cars such as EVs and PHVs are expected to work not only as transportation devices, but also as power storages. However, in order to use the battery effectively, we need to know the future Profile of the Departure and Travel Time (PDTT) of the car. This paper presents an estimation method of the PDTT of the car over one day from the present time based on the Statistics of the Departure and Travel Time (SDTT) and dynamic programming. The prediction problem of PDTT of the car is formulated as a maximum-likelihood estimation problem under the condition that the SDTT is available. In order to find a global optimal solution within a reasonable computational cost, first of all, a Markov model representing all possible PDTT of the car is derived from the SDTT. Then, the dynamic programming is applied to find the most likely PDTT of the car. The usefulness of the proposed method is evaluated by numerical experiments, wherein the SDTT is created by real driving data.