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Electrified powertrains respond to driver demand for vehicle acceleration by producing power through either the electric drive system or an on-board combustion engine or both. In Plug-In Hybrid Vehicles (PHEVs), the powertrain provides the purest form of transportation when responding to driver demand through the electric drive system. We develop a method to size the electric drive system in PHEVs to provide zero emission driving in densely populated urban regions. We use real world data from Europe and calculate instantaneous wheel power during trips. Ray tracing is used to identify the regions where trips occur and the population density of these regions is obtained from an open source dataset published by Eurostat. Regions are categorized by their population density into urban and extra-urban regions. Real world data from these regions is analyzed to determine the wheel power required in urban and extra-urban settings.

The falling cost of lithium ion batteries combined with an ongoing need to reduce greenhouse gas emissions is driving the proliferation of affordable long-range battery electric vehicles (BEVs). However, an inherent challenge with longer-range BEVs is the increased time required to fully charge the battery using standard 120/240 V AC power outlets. One approach to address this issue involves moving to higher power onboard AC chargers; however, household and utility wiring may not allow for the full capability of these higher power chargers. This study explores the typical time available for vehicle charging during an overnight stop based on real-world customer “MyFord Mobile” (MFM) data collected from Ford electrified vehicles. Through this approach, the available overnight time for recharging and required energy to be added to the battery are evaluated under the influence of typical daily driving distances, extreme ambient temperatures, and value charging time windows.

Electric vehicles are highly sensitive to variations in environmental factors (like temperature, drive style, grade, etc.). The distribution of real-world range of electric vehicles due to these environmental factors is an important consideration in target setting. This distribution can be obtained by running several simulations of an electric vehicle for a number of high-frequency velocity, grade, and temperature real-world trip profiles. However, in order to speed up simulation time, a unique set of drive profiles that represent the entire real-world data set needs to be developed. In this study, we consider 40,000 unique velocity and grade profiles from various real-world applications in EU. We generate metadata that describes these profiles using trip descriptor variables. Due to the large number of descriptor variables when considering second order effects, we normalize each descriptor and use principal component analysis to reduce the dimensions of our dataset to six components.

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.

Integration of a new, complex technology which crosses powertrain system boundaries (and thereby involves multiple organizations), at the optimum cost-attribute balance, is a complex task. An example of such a technology is a Vapor-Compression Heat Pump (VCHP) system. A VCHP system uses a vapor-compression refrigeration cycle to ‘pump’ heat from ambient into the cabin. This system can be used to supplement or replace other less efficient heating systems (e.g. engine, LV-PTC air heater, HV-PTC coolant heater, etc.) - which will improve fuel economy. The use of a heat pump system impacts several primary attributes, including heating, cooling, fuel economy, and electric range. These attributes must be balanced in an ideal fashion against the substantial expense, if a VCHP is to be selected for use in a particular vehicle. This paper walks through the value equation for the VCHP from start to end, addressing potential concerns and opportunities.

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.