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Machine learning estimates of plug-in hybrid electric vehicle utility factors
تخمین یادگیری ماشین فاکتورهای وسیله نقلیه الکتریکی هیبریدی توکار-2019 Plug-in hybrid electric vehicles (PHEV) combine an electric drive train with a conventional one
and are able to drive on gasoline when the battery is fully depleted. They can thus electrify many
vehicle miles travelled (VMT) without fundamental range limits. The most important variable for
the electrification potential is the ratio of electric VMT to total VMT, the so-called utility factor
(UF). However, the empirical assessment of UFs is difficult since important factors such as daily
driving, re-charging behaviour and frequency of long-distance travel vary noteworthy between
drivers and large data collections are required. Here, we apply machine learning techniques
(regression tree, random forest, support vector machine, and neural nets) to estimate real-world
UF and compare the estimates to actual long-term average UF of 1768 individual Chevrolet Volt
PHEV. Our results show that UFs can be predicted with high accuracy from individual summary
statistics to noteworthy accuracy with a mean absolute error of five percentage points. The accuracy
of these methods is higher than a simple simulation with electric driving until the battery
is discharged and one full daily recharge. The most important variables in estimating UF according
to a linear regression model are the variance and skewness of the daily VMT distributions
as well as the frequency of long-distance driving. Thus, our findings make UF predictions from
existing data sets for driving of conventional vehicles more accurate. Keywords: Electric vehicles | Plug-in hybrid electric vehicle | Utility factor | Machine learning |
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