TY - JOUR
T1 - On the aggregate grid load imposed by battery health-conscious charging of plug-in hybrid electric vehicles
AU - Bashash, Saeid
AU - Moura, Scott J.
AU - Fathy, Hosam K.
N1 - Funding Information:
This research was initially supported by a research partnership led by the University of Michigan and DTE Energy , and funded by a Michigan Public Service Commission Grant . Further support has been provided by the Pennsylvania State University's College of Engineering through the third author's startup grant. The authors gratefully acknowledge this support.
PY - 2011/10/15
Y1 - 2011/10/15
N2 - This article examines the problem of estimating the aggregate load imposed on the power grid by the battery health-conscious charging of plug-in hybrid electric vehicles (PHEVs). The article begins by generating a set of representative daily trips using (i) the National Household Travel Survey (NHTS) and (ii) a Markov chain model of both federal and naturalistic drive cycles. A multi-objective optimizer then uses each of these trips, together with PHEV powertrain and battery degradation models, to optimize both PHEV daily energy cost and battery degradation. The optimizer achieves this by varying (i) the amounts of charge obtained from the grid by each PHEV, and (ii) the timing of this charging. The article finally computes aggregate PHEV power demand by accumulating the charge patterns optimized for individual PHEV trips. The results of this aggregation process show a peak PHEV load in the early morning (between 5.00 and 6.00 a.m.), with approximately half of all PHEVs charging simultaneously. The ability to charge at work introduces smaller additional peaks in the aggregate load pattern. The article concludes by exploring the sensitivity of these results to the relative weighting of the two optimization objectives (energy cost and battery health), battery size, and electricity price.
AB - This article examines the problem of estimating the aggregate load imposed on the power grid by the battery health-conscious charging of plug-in hybrid electric vehicles (PHEVs). The article begins by generating a set of representative daily trips using (i) the National Household Travel Survey (NHTS) and (ii) a Markov chain model of both federal and naturalistic drive cycles. A multi-objective optimizer then uses each of these trips, together with PHEV powertrain and battery degradation models, to optimize both PHEV daily energy cost and battery degradation. The optimizer achieves this by varying (i) the amounts of charge obtained from the grid by each PHEV, and (ii) the timing of this charging. The article finally computes aggregate PHEV power demand by accumulating the charge patterns optimized for individual PHEV trips. The results of this aggregation process show a peak PHEV load in the early morning (between 5.00 and 6.00 a.m.), with approximately half of all PHEVs charging simultaneously. The ability to charge at work introduces smaller additional peaks in the aggregate load pattern. The article concludes by exploring the sensitivity of these results to the relative weighting of the two optimization objectives (energy cost and battery health), battery size, and electricity price.
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U2 - 10.1016/j.jpowsour.2011.06.025
DO - 10.1016/j.jpowsour.2011.06.025
M3 - Article
AN - SCOPUS:79961023201
SN - 0378-7753
VL - 196
SP - 8747
EP - 8754
JO - Journal of Power Sources
JF - Journal of Power Sources
IS - 20
ER -
