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It is desirable to find methods to increase electric vehicle (EV) driving range and reduce performance variability of Plug-in Hybrid Electric Vehicles (PHEV). One strategy to improve EV range is to increase the charge power limit of the traction battery, which allows for more brake energy recovery. This paper applies Big Data technology to investigate how increasing the charge power limit could affect EV range in real world usage with respect to driving behavior. Big Data Drive (BDD) data collected from Ford employee vehicles in Michigan was analyzed to assess the impact of regenerative braking power on EV range. My Ford Mobile (MFM) data was also leveraged to find correlation to drivers nationwide based on brake score statistics. Estimated results show incremental improvements in EV range from increased charge power levels. Subsequently, this methodology and process could be applied to make future design decisions based on the dynamic nature of driving habits.

Electrified vehicles including Battery Electric Vehicles (BEVs) and Plug-In Hybrid Vehicles (PHEVs) made by Ford Motor Company are fitted with a telematics modem to provide customers with the means to communicate with their vehicles and, at the same time, receive insight on their vehicle usage. These services are provided through the “MyFordMobile” website and phone applications, simultaneously collecting information from the vehicle for different event triggers. In this work, we study this data by using Big Data Methodologies including a Hadoop Database for storing data and HiveQL, Pig Latin and Python scripts to perform analytics. We present electrified vehicle customer behaviors including geographical distribution, trip distances, and daily distances and compare these to the Atlanta Regional Survey data. We discuss customer behaviors pertinent to electrified vehicles including charger types used, charging occurrence, charger plug-in times etc.

The efficiency of an electrified powertrain is sensitive to fluctuations in temperature. This impacts the Electric Vehicle Miles Traveled (eVMT), or the miles travelled by Plug-In Hybrid Electric Vehicles (PHEVs) using electrical grid power. In this paper, we discuss various methods used to calculate eVMT for PHEVs and propose an alternate method to calculate eVMT with higher accuracy using real world customer data. Real world customer data is obtained through telematics modems on Ford Energi products powered by the “MyFord Mobile” web and phone applications. Customer and season specific data from pure charge depleting and pure charge sustaining trips are used in this method to generate a customer and season specific conversion factor. As a result, this real world data based method helps track the effect of seasonality on eVMT obtained by customers in a combination of all charge depleting and charge sustaining trips.