Battery health-conscious plug-in hybrid electric vehicle grid demand prediction

Saeid Bashash, Scott J. Moura, Hosam K. Fathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper examines the problem of predicting the aggregate grid load imposed by battery health-conscious plug-in hybrid electric vehicle (PHEV) charging. The paper begins by generating a set of representative daily PHEV trips using the National Household Travel Survey (NHTS) and a set of federal and real-world drive cycles. Each trip is then used in a multiobjective genetic optimizer, along with a PHEV model and a battery degradation model, to simultaneously minimize PHEV energy cost and battery degradation. The optimization variables include the parameters of the PHEV charge pattern, defined as the timing and rate with which the PHEV receives electricity from the grid. For several weightings of the optimization objectives, total PHEV power demand is predicted by accumulating the charge patterns for individual PHEVs. Two charging scenarios, i.e., charging at home only versus charging at home and work, are examined. Results indicate that the main PHEV peak load occurs early in the morning (between 5.00-6.00a.m.), with approximately 45%-60% of vehicles simultaneously charging from the grid. Moreover, charging at work creates additional peaks in this load pattern.

Original languageEnglish (US)
Title of host publicationASME 2010 Dynamic Systems and Control Conference, DSCC2010
Pages489-497
Number of pages9
DOIs
StatePublished - 2010
EventASME 2010 Dynamic Systems and Control Conference, DSCC2010 - Cambridge, MA, United States
Duration: Sep 12 2010Sep 15 2010

Publication series

NameASME 2010 Dynamic Systems and Control Conference, DSCC2010
Volume1

Other

OtherASME 2010 Dynamic Systems and Control Conference, DSCC2010
CountryUnited States
CityCambridge, MA
Period9/12/109/15/10

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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