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The new European test procedure, called the worldwide harmonized light vehicle test procedure (WLTP), deviates in some details from the current NEDC-based test which will have an impact on the determination of the official EU fuel consumption values for the new vehicles. The adaptation to the WLTP faces automakers with new challenges for meeting the stringent EU fuel consumption and CO2 emissions standards. This paper investigates the main changes that the new test implies to a mid-size sedan electrified vehicle design and quantifies their impact on the vehicles fuel economy. Three common electrified powertrain architectures including series, parallel P2, and powersplit are studied. A Pontryagin’s Minimum Principle (PMP) optimization-based energy management control strategy is developed to evaluate the energy consumption of the electrified vehicles in both charge-depleting (CD) and charge-sustaining (CS) modes.

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.

Plug-in hybrid electric vehicles (PHEV) have an EV mode driving range which can cover a portion of customer daily driving. This EV mode range affects the refuel frequency substantially compared with conventional vehicle. For a conventional vehicle, daily driving pattern, tank size and fuel economy are the factors affecting the refuel frequency. While for a PHEV, EV range is another factor would affect the results substantially. Traditional method of label range can’t represent real world driving range between fill-ups for PHEV well. How to accurately predict the PHEV refuel distance taking into account real world customer driving patterns and PHEV parameters become critical for PHEV system design and optimization. This paper presents real world big customer data based PHEV refuel distance estimation modeling. The target is to estimate PHEV refuel distance given several specific parameters such as EV range, hybrid mode fuel economy, tank size etc.

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 automotive industry is rapidly expanding its Hybrid, Plug-in Hybrid and Battery Electric Vehicle product offerings in response to meet customer wants and regulatory requirements. One way for electrified vehicles to have an increasing impact on fleet-level CO2 emissions is for their sales volumes to go up. This means that electrified vehicles need to deliver a complete set of vehicle level attributes like performance, Fuel Economy and range that is attractive to a wide customer base at an affordable cost of ownership. As part of “democratizing” the Hybrid and plug-In Hybrid technology, automotive manufacturers aim to deliver these vehicle level attributes with a powertrain architecture at lowest cost and complexity, recognizing that customer wants may vary considerably between different classes of vehicles. For example, a medium duty truck application may have to support good trailer tow whereas a C-sized sedan customer may prefer superior city Fuel Economy.

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.