@inproceedings{31f590f0a40e4f4a8eae320cc5a461c8,
title = "A data-driven predictive energy management strategy for plug-in hybrid vehicles",
abstract = "Plug-In Hybrid Electric Vehicles show great potential for decreasing the fuel consumption on specified routes. However, in many cases the trip destination or the distance until the next charge is unknown for the vehicle. This paper presents a data-driven, online energy management strategy that is based on a trip and speed profile prediction for a receding horizon, which takes personal points of interest or upcoming charging stations into consideration. Pontryagin's Minimum Principle including a reduced shooting algorithm is applied to optimize the vehicle state. We evaluated the method for multiple trips of varying length and expect an estimated fuel saving of 8.0% compared to a non-predictive approach.",
keywords = "EMS, HEV, PHEV, intelligent transportation systems, trip prediciton",
author = "J{\"u}rgen Lohrer and Matthias F{\"o}rth and Markus Lienkamp",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Mechanical, System and Control Engineering, ICMSC 2017 ; Conference date: 19-05-2017 Through 21-05-2017",
year = "2017",
month = jun,
day = "26",
doi = "10.1109/ICMSC.2017.7959490",
language = "English",
series = "2017 International Conference on Mechanical, System and Control Engineering, ICMSC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "297--304",
booktitle = "2017 International Conference on Mechanical, System and Control Engineering, ICMSC 2017",
}