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With nearly 220,000 vehicles, the United States Postal Service (USPS) has the largest non-military vehicle fleet in the world. This fleet requires over a billion dollars of fuel annually, and this figure does not include contracted vehicles. As a part of the business strategy, the USPS has embraced and invested in alternative fueled vehicles since 1899, when the first recorded use of an electric vehicle for USPS service was performed as a technology evaluation in Cleveland, OH. As part of a technology evaluation of advanced vehicle systems, the USPS has partnered with the DOE?s Vehicle Technology Program (VTP) to benchmark and quantify the capabilities of five vehicles in meeting specific Urban Route Delivery requirements, both with dynamometer and in-service testing. The all electric vehicle conversions have been developed by established electric vehicle systems manufacturers representing various perspectives on meeting the vehicle specific operation objectives.

Today's automotive and electronics technologies are evolving so rapidly that educators and industry are both challenged to re-educate the technological workforce in the new area before they are replaced with yet another generation. In early November 2009 Ford's Product Development senior management formally approved a proposal by the University of Detroit Mercy to transform 125 of Ford's “IC Engine Automotive Engineers” into “Advanced Electric Vehicle Automotive Engineers.” Two months later, the first course of the Advanced Electric Vehicle Program began in Dearborn. UDM's response to Ford's needs (and those of other OEM's and suppliers) was not only at the rate of “academic light speed,” but it involved direct collaboration of Ford's electric vehicle leaders and subject matter experts and the UDM AEV Program faculty.

Plug-in hybrid electric vehicle (PHEV) technologies have the potential for considerable petroleum consumption reductions, possibly at the expense of increased tailpipe emissions due to multiple “cold” start events and improper use of the engine for PHEV specific operation. PHEVs operate predominantly as electric vehicles (EVs) with intermittent assist from the engine during high power demands. As a consequence, the engine can be subjected to multiple cold start events. These cold start events may have a significant impact on the tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current hybrid electric vehicles (HEVs), the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts.

Response surface methodology (RSM) techniques were applied to develop a predictive model of electric vehicle (EV) energy consumption over the Environmental Protection Agency's (EPA) standardized drive cycles. The model is based on measurements from a synthetic composite drive cycle. The synthetic drive cycle is a minimized statistical composite of the standardized urban (UDDS), highway (HWFET), and US06 cycles. The composite synthetic drive cycle is 20 minutes in length thereby reducing testing time of the three standard EPA cycles by over 55%. Vehicle speed and acceleration were used as model inputs for a third order least squared regression model predicting vehicle battery power output as a function of the drive cycle. The approach reduced three cycles and 46 minutes of drive time to a single test of 20 minutes.

Electrochemical models of lithium ion batteries are today a standard tool in the automotive industry for activities related to the computer-aided engineering design, analysis, and optimization of energy storage systems for electrified vehicles. One of the challenges in the development or use of such models is the need of detailed information on the cell and electrode geometry or properties of the electrode and electrolyte materials, which are typically unavailable or difficult to retrieve by end-users. This forces engineers to resort to “hand-tuning” of many physical and geometrical parameters, using standard cell-level characterization tests. This paper proposes a method to provide information and data on individual electrode performance that can be used to simplify the calibration process for electrochemical models.

Battery temperature variations have a strong effect on both battery aging and battery performance. Significant temperature variations will lead to different battery behaviors. This influences the performance of the Hybrid Electric Vehicle (HEV) energy management strategies. This paper investigates how variations in battery temperature will affect Lithium-ion battery aging and fuel economy of a HEV. The investigated energy management strategy used in this paper is the Equivalent Consumption Minimization Strategy (ECMS) which is a well-known energy management strategy for HEVs. The studied vehicle is a Honda Civic Hybrid and the studied battery, a BLS LiFePO4 3.2Volts 100Ah Electric Vehicle battery cell. Vehicle simulations were done with a validated vehicle model using multiple combinations of highway and city drive cycles. The battery temperature variation is studied with regards to outside air temperature.

Electric drives, whether in battery electric vehicles (BEVs) or various other applications, are an important part of modern transportation. Traditionally, physics-based models based on steady-state mapping of electric drives have been used to evaluate their behavior under transient conditions. Hardware-in-the-Loop (HIL) testing seeks to provide a more accurate representation of a component’s behavior under transient load conditions that are more representative of real world conditions it will operate under, without requiring a full vehicle installation. Oak Ridge National Laboratory (ORNL) developed such a HIL test platform capable of subjecting electric drives to both conventional steady-state test procedures as well as transient experiments such as vehicle drive cycles.

The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographics and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs.

Plug-in hybrid electric vehicles (PHEV) operate predominantly as electric vehicles (EV) with intermittent assist from the engine. As a consequence, the engine can be subjected to multiple cold start events. These cold start events have a significant impact on tailpipe emissions due to degraded catalyst performance and starting the engine under less than ideal conditions. On current conventional vehicles, the first cold start of the engine dictates whether or not the vehicle will pass federal emissions tests. PHEV operation compounds this problem due to infrequent, multiple engine cold starts. ORNL, in collaboration with the University of Tennessee, developed an Engine-In-the-Loop (EIL) test platform to investigate cold start emissions on a 2.0l Gasoline Turbocharged Direct Injection (GTDI) Ecotec engine coupled to a virtual series hybrid electric vehicle.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

Fuel cell-powered electric vehicles (FCPEV) require an energy storage device to start up the fuel cells and to store the energy captured during regenerative braking. Low-voltage (12 V) batteries are preferred as the storage device to maintain compatibility with the majority of today's automobile loads. A dc/dc converter is therefore needed to interface the low-voltage batteries with the fuel cell-powered higher-voltage dc bus system (255 V ∼ 425 V), transferring energy in either direction as required. This paper presents a soft-switched, isolated bi-directional dc/dc converter developed at Oak Ridge National Laboratory for FCPEV applications. The converter employs dual half-bridges interconnected with an isolation transformer to minimize the number of switching devices and their associated gate drive requirements. Snubber capacitors including the parasitic capacitance of the switching devices and the transformer leakage inductance are utilized to achieve zero-voltage switching (ZVS).

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.

It is widely recognized in the automotive industry that, in very cold climatic conditions, the driving range of an Electric Vehicle (EV) can be reduced by 50% or more. In an effort to minimize the EV range penalty, a novel thermal energy storage system has been designed to provide cabin heating in EVs and Plug-in Hybrid Electric Vehicles (PHEVs) by using an advanced phase change material (PCM). This system is known as the Electrical PCM-based Thermal Heating System (ePATHS) [1, 2]. When the EV is connected to the electric grid to charge its traction battery, the ePATHS system is also “charged” with thermal energy. The stored heat is subsequently deployed for cabin comfort heating during driving, for example during commuting to and from work. The ePATHS system, especially the PCM heat exchanger component, has gone through substantial redesign in order to meet functionality and commercialization requirements.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. Extended range electric vehicles (EREVs), by decoupling the engine from the drivetrain, allows the engine to operate in a limited operating range; thus, EREVs offer an ideal platform for realizing the advantages of LTC engines. In this study, the global optimum fuel economy improvement of an experimentally developed 2-liter multi-mode LTC engine in a series EREV is investigated. The engine operation modes include Homogeneous-Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI).

The increased market share of electric vehicles and renewable energy resources have raised concerns about their impact on the current electrical distribution grid. To achieve sustainable and stable power distribution, a lot of effort has been made to implement smart grids. This paper addresses Demand Response (DR) load control in a smart grid using Internet of Things (IoT) technology. A smart grid is a networked electrical grid which includes a variety of components and sub-systems, including renewable energy resources, controllable loads, smart meters, and automation devices. An IoT approach is a good fit for the control and energy management of smart grids. Although there are various commercial systems available for smart grid control, the systems based on open sources are limited. In this study, we adopt an open source development platform named Node-RED to integrate DR capabilities in a smart grid for DR load control. The DR system employs the OpenADR standard.

Vehicle-to-Grid (V2G) service has a potential to improve the reliability and stability of the electrical grid due to the ability of providing bi-directional power flow from/to the grid. However, frequent charging/discharging may impact the battery lifetime. This paper presents the analysis of battery degradation in three scenarios. In the first scenario, different battery capacities are considered. In the second scenario, the battery degradation with various depth of discharge (DOD) are studied. In the third scenario, the capacity loss due to different charging regime are compared. The charging/discharging of plug-in electric vehicles (PEVs) are simulated in a single-phase microgrid system integrated with a photovoltaics (PV) farm, an energy storage system (ESS), and ten electric vehicle service equipment (EVSE). The battery degradation model is an energy throughput model, which is developed based on the Arrhenius equation and a power law relationship between time and capacity fading.

Hybrid electric vehicles are continuously challenged to meet cross attribute performance while minimizing energy usage and component cost in a very competitive automotive market. As electrified vehicles become more mainstream in the marketplace, hybrid customers are expecting more attribute refinement in combination with the enhanced fuel economy benefits. Minimizing fuel consumption, which tends to drive hybrid powertrain engines to operate under lugging type calibrations, traditionally challenge noise, vibration, and harshness (NVH) metrics. Balancing the design space to satisfy the cost metrics, energy efficiency, noise and vibration & drivability under the hybrid engine lugging conditions can be optimized through the use of multiple CAE tools. This paper describes how achieving NVH metrics can put undesirable boundaries on Powertrain Operation which could affect other performance attributes.

This study investigates the life cycle greenhouse gas (GHG) emissions of a set of vehicles using two real-world gliders (vehicles without powertrains or batteries); a steel-intensive 2013 Ford Fusion glider and a multi material lightweight vehicle (MMLV) glider that utilizes significantly more aluminum and carbon fiber. These gliders are used to develop lightweight and conventional models of internal combustion engine vehicles (ICV), hybrid electric vehicles (HEV), and battery electric vehicles (BEV). Our results show that the MMLV glider can reduce life cycle GHG emissions despite its use of lightweight materials, which can be carbon intensive to produce, because the glider enables a decrease in fuel (production and use) cycle emissions. However, the fuel savings, and thus life cycle GHG emission reductions, differ substantially depending on powertrain type. Compared to ICVs, the high efficiency of HEVs decreases the potential fuel savings.

Power modules play a key role in traction inverters for vehicle electrification applications. The harsh automotive operating environment is a big challenge for power modules. The paper highlights the challenges for power modules usage in electrified vehicles (xEVs), and proposes a design verification procedure for such application in order to ensure the reliable operation under all conditions. First, power modules operate in all climate zones and are exposed to a wide ambient temperature range underhood from -40°C to 105°C. A typical automotive power module should therefore withstand a junction temperature from -40°C to up to 175°C without exceeding its safe operating area (SOA), e.g. avalanche breakdown voltage, maximum current, and thermal limit. Second, an inductive induced high voltage spike could be generated during the power semiconductor fast switching at high voltage and high current conditions.

Electric vehicles (EV) and hybrid electric vehicles (HEV) require high torque/acceleration ability and wide speed range. To meet both of them, the traction machines usually have to be oversized, which results in high volume and weight, high cost, and low efficiency. In practical application, high speed motors combining with gear box provide the expected torque and speed capability. If pole-changing machines are employed to achieve wide torque and speed ranges, gear box and motor size can be reduced in EVs/HEVs. This paper presents a pole-phase modulation motor drive which changes both of poles and phases simultaneously, as a result that the motor extends its torque/speed capability in a flexible way. Simulation results verify the principle and control method for this kind of motor drives.