@article{
 title = {Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles},
 type = {article},
 year = {2012},
 identifiers = {[object Object]},
 keywords = {Driving cycle optimization,Intelligent transportation systems,Plug-in-hybrid electric vehicles},
 pages = {201-207},
 volume = {17},
 websites = {http://www.sciencedirect.com/science/article/pii/S1361920911001489},
 month = {5},
 id = {c5a87550-1828-35e0-a945-3594d7746278},
 created = {2015-01-13T16:35:41.000Z},
 accessed = {2015-01-06},
 file_attached = {false},
 profile_id = {9696f304-db07-31d9-a8cd-d208939f9889},
 group_id = {062cfb24-9ce4-3a92-a41f-541a783d7b95},
 last_modified = {2017-03-09T15:29:23.010Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {He2012},
 private_publication = {false},
 abstract = {In this paper, a forward power-train plug-in hybrid electric vehicle model with an energy management system and a cycle optimization algorithm is evaluated for energy efficiency. Using wirelessly communicated predictive traffic data for vehicles in a roadway network, as envisioned in intelligent transportation systems, traffic prediction cycles are optimized using a cycle optimization strategy. This resulted in a 56–86% fuel efficiency improvements for conventional vehicles. When combined with the plug-in hybrid electric vehicle power management system, about 115% energy efficiency improvements were achieved. Further improvements in the overall energy efficiency of the network were achieved with increased penetration rates of the intelligent transportation assisted enabled plug-in hybrid electric vehicles.},
 bibtype = {article},
 author = {He, Yiming and Rios, Jackeline and Chowdhury, Mashrur and Pisu, Pierluigi and Bhavsar, Parth},
 journal = {Transportation Research Part D: Transport and Environment},
 number = {3}
}

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