Machine learning estimates of plug-in hybrid electric vehicle utility factors

Abstract

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.