Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles |
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Authors: | Yiming He Jackeline RiosMashrur Chowdhury Pierluigi PisuParth Bhavsar |
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Institution: | a Clemson University, Glenn Department of Civil Engineering, Clemson, SC 29634, USA b Clemson University International Center for Automotive Research, 4 Research Drive, Greenville, SC 29607, USA |
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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. |
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Keywords: | Intelligent transportation systems Plug-in-hybrid electric vehicles Driving cycle optimization |
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