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

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.

[1]  Michael C. Caramanis,et al.  Management of electric vehicle charging to mitigate renewable generation intermittency and distribution network congestion , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[2]  Jan-Ming Ho,et al.  Travel time prediction with support vector regression , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[3]  Tom Molinski,et al.  PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data , 2012, IEEE Transactions on Smart Grid.

[4]  D. Ettema,et al.  Modelling the joint choice of activity timing and duration , 2007 .

[5]  Hjp Harry Timmermans,et al.  Modeling Departure Time Choice in the Context of Activity Scheduling Behavior , 2003 .

[6]  Shaahin Filizadeh,et al.  Statistical Development of a Duty Cycle for Plug-in Vehicles in a North American Urban Setting Using Fleet Information , 2010, IEEE Transactions on Vehicular Technology.

[7]  Steven I-Jy Chien,et al.  Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based Versus Path Based , 2001 .

[8]  Lang Tong,et al.  iEMS for large scale charging of electric vehicles: Architecture and optimal online scheduling , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).